system

Abstract

We provide the system. [Solution] A means for uploading images taken by participants to send them to the server, An analysis method for analyzing transmitted images using artificial intelligence technology and selecting images, An adjustment mechanism for automatically adjusting the image quality and composition of selected images, A means for users to review the adjusted images and share the selected images, A means for automatically generating an album using multiple shared images, A regional processing method for processing data within a region and improving communication efficiency, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Photo recording activities at events have problems represented by a shortage of dedicated photographers, especially in school or local events. Under such circumstances, it is required that participants themselves take photos and quickly share a high-quality and organized record of the event, which is difficult for many people without technical knowledge. In addition, there is a time and labor burden in selecting and arranging appropriate photos from a large number of photos taken by participants. Furthermore, in local events, in particular, the efficiency of Internet communication and the speed of data processing are regarded as problems. There is a need for a technology to solve these problems and smoothly perform photo recording of events.

Means for Solving the Problems

[0005] This invention provides a system in which participants upload images taken with their mobile devices to a server, and these images are analyzed and selected using artificial intelligence technology. The selected images are automatically adjusted for image quality and composition, allowing users to easily review and share them. Furthermore, the system includes a function to automatically generate albums based on the shared images. This enables event participants to easily provide high-quality photos and optimize event recording. Additionally, by utilizing region-specific data processing methods, it is possible to improve communication efficiency within the region. These means effectively solve various problems related to photographic recording of events.

[0006] "Participants" refer to individuals or groups who take photographs at an event and are involved in the process from taking the photos to sharing them.

[0007] "Images" refer to visual information captured by participants and stored and processed in digital format.

[0008] A "server" is a central computer system that connects to multiple terminals via a network and performs image storage, analysis, and processing.

[0009] "Artificial intelligence technology" refers to technologies that enable computers to mimic human intellectual behavior and perform functions such as data analysis, image recognition, and recommendations.

[0010] "Analysis" is the process of evaluating the components and characteristics of a submitted image and extracting and judging information based on specific criteria.

[0011] "Selection" is the process of choosing valuable images based on the analysis results, and is a step that makes them available to users later.

[0012] "Adjustment" refers to the process of automatically correcting selected images to optimize image quality and composition, thereby improving their visual appeal.

[0013] "Sharing" means making an edited image public to other users or platforms and allowing them to view or use it.

[0014] An "album" is a collection of related images arranged in an easy-to-view format, and is saved as a record of an event.

[0015] "Regional processing means" is a general term for technologies and infrastructure used to process data within a specific region and improve communication efficiency. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0030] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for participants to take photos of an event using their own devices, share them smoothly, and create high-quality records. The system includes image processing, AI analysis, content sharing, album generation, and local processing functions.

[0038] First, the "user" takes photos of the event using their own device. This device has a dedicated application installed and provides an interface for quickly processing the captured images. The "device" uploads the captured images to a central "server" using internet communication. The upload also includes metadata such as the time and location information of the photos taken.

[0039] Next, the "server" analyzes the received images. Using AI technology, it evaluates the image's composition, brightness, and the emotional expression of the subject, and selects high-value images based on this information. In this selection process, scoring is performed based on multiple evaluation metrics, and photos that are likely to please the user are proactively added to the recommendation list.

[0040] Subsequently, the selected images undergo automatic quality adjustments on the server. Specifically, brightness and contrast are optimized, and unnecessary elements are cropped, improving their appearance. These adjusted images are then presented to the user as a preview for confirmation.

[0041] Furthermore, images that a "user" has indicated their intention to share are made public to other users and online platforms via the "device." At this time, settings related to the protection of personal information and copyright are also applied to ensure that sharing is conducted safely.

[0042] In addition, the "server" automatically generates event-specific albums using a large number of shared images. These albums are structured to create a narrative, taking into account the time of shooting and the highlights of the event. The final albums are saved as digital books, allowing all participants to reminisce.

[0043] Furthermore, this invention utilizes a "local processing method," which optimizes communication efficiency and provides users with faster feedback by completing data processing within the local area. This reduces the network load at event venues and enables a smoother user experience.

[0044] As a concrete example, consider a school sports day. Parents, as participants, take photos of their children's events with their smartphones, and the images are immediately uploaded to a server. The server uses AI analysis to recommend photos of the most emotionally expressive moments, and the parents share the adjusted images with other family members and on social media. Finally, a dynamic album of the entire sports day is automatically generated and can be viewed online by all participants.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user takes a photo with their device. After taking the photo, a dedicated app automatically starts preparing to upload the image to the server.

[0048] Step 2:

[0049] The device uploads the captured photos to the server along with metadata (e.g., time of capture, GPS information). This provides a centralized management system for image data.

[0050] Step 3:

[0051] The server supplies the received image data to the AI ​​analysis engine. Here, it analyzes characteristics such as image quality, composition, and the subject's facial expression.

[0052] Step 4:

[0053] The server applies a scoring algorithm based on the analysis results, ranking images in order of priority. It selects images that capture particularly valuable moments as recommended content.

[0054] Step 5:

[0055] The server initiates the image quality adjustment process for the selected images. This includes automatic optimization of brightness and contrast, and cropping of the composition.

[0056] Step 6:

[0057] The user visualizes image previews from the server on their device and selects which images to share from the recommended images. The next action is determined based on the user's selection.

[0058] Step 7:

[0059] The device shares images selected by the user to the communication platform or other participants. Necessary settings are applied for security and privacy.

[0060] Step 8:

[0061] The server uses an album generation module to assemble an album of the entire event based on the collected shared images. Considering the time of shooting and highlights of the content, a visually appealing album is created.

[0062] Step 9:

[0063] The local processing mechanism within the server optimizes the processing data locally. This improves the efficiency of the local network and speeds up overall response times.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Current technologies often require manual data selection and optimization for the rapid and efficient management and sharing of digital data, demanding significant time and effort from users. Furthermore, data sharing frequently raises security and privacy concerns. Additionally, increased network load can reduce communication efficiency, potentially impairing the user experience. Addressing these challenges is crucial.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes a transmission means for transmitting digital data to an information processing device, an analysis means for analyzing the digital data using artificial intelligence technology and selecting the data, and an optimization means for automatically optimizing the quality and content of the digital data. This enables users to efficiently manage and securely share digital data.

[0069] A "participant" is an individual or group that uses the system to generate and process digital data.

[0070] "Digital data" refers to electronic information, including captured images and videos, and their associated metadata.

[0071] An "information processing device" is a device capable of transmitting, storing, and analyzing digital data, and generally refers to a server or computer.

[0072] A "transmission means" is a mechanism for appropriately transmitting digital data generated by participants to an information processing device.

[0073] "Analysis methods" refer to systems that use artificial intelligence technology to analyze digital data and evaluate the value and characteristics of the information.

[0074] "Optimization techniques" are processing technologies used to automatically improve the quality and composition of digital data.

[0075] A "user" is an individual or group that reviews and makes decisions regarding the sharing of optimized digital data.

[0076] "Distribution method" refers to a method for securely sharing digital data selected by a user with other users or platforms.

[0077] A "collection" is a digital album or library that brings together selected and shared digital data, built around a specific theme or purpose.

[0078] A "regional processing method" is a mechanism for distributing and processing digital data within a region to improve communication efficiency.

[0079] An embodiment of this system will be described.

[0080] This invention provides a system that allows participants to efficiently manage and share digital data at events and other occasions. Specifically, it involves collaboration between three entities—a server, a terminal, and a user—to generate, transmit, analyze, optimize, and share digital data.

[0081] First, users generate digital data, such as photos and videos, using devices like smartphones and tablets. These devices have dedicated applications installed, allowing users to manipulate the data through a user-friendly interface. The generated data, along with metadata, is then transmitted to a server via the internet using a transmission method.

[0082] Next, the server analyzes the received digital data using analytical tools. This involves the use of a generative AI model. The server utilizes this model to evaluate the data's quality and structure, and then performs scoring. This analysis process selects the data the user is looking for.

[0083] Subsequently, the server uses optimization techniques to improve the quality of the digital data. Specifically, it automatically adjusts the brightness and contrast of the data and trims off unnecessary parts to enhance the visual appeal of the data. Once the optimization is complete, the data is sent from the server to the terminal, and a preview is provided to the user.

[0084] Data that a user has confirmed and indicated their intention to share is shared from their device to other users or social media platforms via distribution methods. During this process, settings regarding personal information and copyright of the data are applied to ensure secure sharing.

[0085] Furthermore, by having servers process large amounts of digital data within a region using local processing methods, communication efficiency is improved and network load is reduced. This allows users to receive feedback more quickly.

[0086] As a concrete example, consider a photo taken by a user at a school sports day. Immediately after taking the photo, it is uploaded to a server, then undergoes AI analysis, and high-quality photos that capture emotionally rich moments are selected. After adjustment, it can be easily shared with other family members or on social media. An example of a prompt message to the generative AI model involved in this process would be the instruction, "Identify the emotionally rich moments of the event and save them as an album."

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] Users capture event footage using a device equipped with a dedicated application and generate digital data. The input is video data from the camera sensor, and the output is the generated digital image data. Specifically, the user operates the device's camera function to capture images, and the application saves that data to internal storage.

[0090] Step 2:

[0091] The device uploads stored digital data and its metadata (such as date and time of capture and location information) to a server via a network transmission method. The input is the generated digital data and metadata, and the output is the data stored in the server's storage device. Specifically, the data is compressed using Wi-Fi or mobile communication and sent to the server using a secure protocol.

[0092] Step 3:

[0093] The server analyzes the received digital data using artificial intelligence technology and analytical tools. The input is the uploaded data, and the output is selected important digital data. Specifically, the server activates an AI model, scans the data content to evaluate facial expressions and composition, and scores based on certain evaluation criteria. In this process, an emotion analysis algorithm is used to identify emotionally rich moments.

[0094] Step 4:

[0095] The server automatically improves the image quality of digital data using optimization techniques based on the analysis results. The input is selected digital data within the server, and the output is digital data with adjusted image quality. Specifically, it automatically adjusts the brightness and contrast of the data and crops the background to optimize its visual appeal.

[0096] Step 5:

[0097] The server returns optimized digital data to the terminal, which the user reviews through the terminal's interface. The input is the optimized digital data sent from the server, and the output is the digital data reviewed by the user. Specific operations include displaying the data on the device screen using preview software.

[0098] Step 6:

[0099] When a user indicates their intention to share selected data, the device distributes the selected digital data to other users or platforms via a distribution method. The input is digital data confirmed by the user, and the output is digital data shared with the specified recipient. Specifically, the data is encrypted at the endpoint and sent to the designated recipient using a secure protocol such as HTTPS.

[0100] Step 7:

[0101] The server processes a large amount of shared digital data using local processing methods. The input is digital data shared after distribution, and the output is a verified digital collection. Specifically, the data is analyzed in a distributed manner at local processing facilities, and caching technology is used to improve communication efficiency and reduce the load on the local network.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] In modern e-commerce, visually recording and efficiently sharing the purchasing experience is crucial. However, there is a lack of suitable means for users to properly photograph their purchased items and easily share them with friends and on social networks. As a result, reflection on and sharing of the purchasing experience with others is sluggish, leading to a decrease in satisfaction with the purchasing activity.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes upload means for transmitting images taken by participants to a data storage device, analysis means for analyzing the transmitted images using intelligent technology and selecting images, and recording means for recording images of product use based on purchase history and facilitating posting by users on social platforms. This enables users to smoothly record their experiences with purchased products and share them in a visually rich way.

[0107] "Participants" are people who take part in events or activities and photograph them with their own devices.

[0108] A "data storage device" is a device that efficiently stores captured images and makes them accessible later.

[0109] "Uploading means" refers to a method or device for transmitting images taken by participants to a data storage device.

[0110] "Intelligent technology" refers to techniques that use artificial intelligence and machine learning to analyze the composition and content of images.

[0111] "Analysis means" refers to a method or apparatus for evaluating transmitted images using intelligent technology and selecting appropriate images.

[0112] "Generation means" refers to a method or apparatus for automatically constructing an attractive photo collection based on a set of images.

[0113] A "regional processing method" is a method or device for processing data within a specific region and maximizing communication efficiency.

[0114] "Recording means" refers to a method or device for managing images of product use based on purchase history and facilitating easy sharing.

[0115] The following describes a mode for carrying out the invention. The system that realizes this application aims to efficiently manage images taken by participants and share them in a visually appealing way.

[0116] First, the user's device provides an interface for taking images at events, shopping trips, and other locations. This device is often a smartphone or tablet, and it processes image data through a dedicated application installed on the device. The image data is uploaded to a server via the internet. At the same time, metadata such as the time of capture and location information is also transmitted.

[0117] The server uses intelligent technology to analyze the received images. Specifically, it evaluates images using machine learning and artificial intelligence (AI) technologies, such as software like TENSORFLOW® and OpenCV. The server scores the composition, color information, and emotional expression of the subjects in the incoming images and selects the most valuable images. Furthermore, it applies adjustments to the selected images, such as brightness and contrast adjustments and cropping of unnecessary elements.

[0118] The adjusted images are provided to the user as a preview and can be shared on online social platforms and within communities at the user's discretion. The server also integrates with the user's purchase history information and includes recording mechanisms for documenting the use of purchased items and review images, thereby helping users share their experiences attractively on social media.

[0119] As a concrete example, when a customer purchases new shoes at a shopping mall, they take a photo of the product and upload it to a server. The server analyzes the photo and suggests the most suitable filters and layouts. By posting this to social media, the user can visually promote their new purchase to friends and followers, and also create an album to reflect on their shopping experience.

[0120] An example of a prompt used when giving instructions to a generative AI model is a customized input such as, "Please suggest the best filter and layout to share the new sneakers in this photo in the best possible way." This allows the AI ​​to receive specific instructions for automatically optimizing the photo according to the user's wishes.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user takes an image using their device. This image contains metadata such as the time and location information. The captured image becomes input that is sent to a data storage device via the application.

[0124] Step 2:

[0125] The device uploads the captured image, along with the time and location information, to a server via the internet. The server then receives the image data. The image metadata is also transmitted during this upload.

[0126] Step 3:

[0127] The server analyzes the received images. Using intelligent technologies such as TensorFlow, it scores the image's composition, color information, and the subject's emotion. The input is images uploaded by the user, and the output is a list of valuable images.

[0128] Step 4:

[0129] The server uses OpenCV to adjust the brightness and contrast of the selected images and crop out unnecessary elements. The input is a list of images selected in step 3, and the output is the adjusted image.

[0130] Step 5:

[0131] The server provides the user with a preview of the adjusted image. The user reviews the image and optionally shares it on social platforms. The input is the adjusted image, and the selected image is shared as the output.

[0132] Step 6:

[0133] The server uses purchase history information to record images of users using their purchased items. This makes it easy for users to visually record and share product reviews and usage scenarios.

[0134] Step 7:

[0135] The server uses a generative AI model to suggest optimal filters and layouts based on user input, optimizing photos for sharing on social media. Here, example prompts are crucial, instructing the generative AI on specific desired transformations.

[0136] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0137] This invention provides a system that streamlines the photography and sharing process at events, and further generates and adjusts images while taking user emotions into consideration. The system includes a series of processes for managing, selecting, and adjusting images taken by participants on a server.

[0138] The system begins with the "user" taking photos during the event using their own device. This device has an application installed to upload the captured images to the server in real time. Along with the captured images, the "device" can also send simple emotional data from the user (e.g., reaction buttons or voice recordings) to the server.

[0139] Next, the "server" performs an artificial intelligence analysis on the received images and emotion data. In this analysis, the "emotion engine" evaluates the facial expressions and audio signals of the subjects in the images to understand the user's emotional state. Based on the results, it applies this as feedback in image selection and adjustment, and adds images that particularly match the user's preferences to the recommendation list.

[0140] The selected images automatically enter an image quality adjustment process, where their brightness, contrast, and composition are optimized on the server. Image filtering and color adjustments may also be applied based on sentiment data. After adjustment, the user can review the images on their device and choose which ones to share.

[0141] Furthermore, the "server" generates customized albums that reflect emotional data to provide a new visual experience from a large number of images. This album generation incorporates not only event highlights but also moments that richly express the user's emotions.

[0142] As a concrete example, consider a family celebration event. After a family member takes a photo with their smartphone, they send the photo along with an audio recording of their emotional expression to the server. Using this information, the server analyzes the photo and automatically filters it to highlight the family's smiles. Finally, an album that captures the emotions of the event is generated and shared with everyone. In this way, the present invention optimizes event recording while taking the user's emotions into consideration.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users film the event with their own devices. The devices are equipped with an interface for recording the user's emotional responses along with the images.

[0146] Step 2:

[0147] The device uploads captured images and emotion data to a server. Emotion data is recorded as simple reaction buttons or voice input.

[0148] Step 3:

[0149] The server supplies data to the AI ​​analysis engine and the emotion engine to analyze the received image and emotion data. The AI ​​analysis engine evaluates the composition and quality of the images, while the emotion engine analyzes the user's emotional state.

[0150] Step 4:

[0151] The server scores images based on the analysis results, and in particular selects images with high recommendation scores based on sentiment data. This selection determines how well the images align with the user's preferences.

[0152] Step 5:

[0153] The server adjusts the image quality and composition of the selected images. This includes adjusting brightness, contrast, and cropping, as well as applying filters and adjusting color tones as needed.

[0154] Step 6:

[0155] The user reviews the adjusted image on their device. The user can then select the image to share and direct the next steps based on that selection.

[0156] Step 7:

[0157] The device shares the selected images with other participants and online platforms. Security settings and privacy policies are applied.

[0158] Step 8:

[0159] The server generates customized albums based on the user's emotions, using the shared image collection as a basis. The albums are structured to highlight moments in time when the user's emotions were expressed through the events.

[0160] Step 9:

[0161] The local processing mechanism within the server improves local communication efficiency by optimizing the processing data locally. This shortens the time to final output, enabling faster service to users.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] When streamlining the photo-taking and sharing process at events, and further generating and adjusting images that take user emotions into consideration, conventional systems have struggled to appropriately reflect user emotions in image selection and adjustment. Furthermore, there is a need to improve communication efficiency when using cloud servers. A new system is needed to solve these challenges.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes upload means for transmitting images and emotional data taken by participants to the server, analysis means for analyzing the transmitted images and emotional data using artificial intelligence technology and selecting images, and means for generating a list of recommended images that match the user's preferences based on the analysis results. This enables efficient image selection and adjustment that takes the user's emotions into consideration, as well as improved communication efficiency.

[0167] "Uploading method" refers to the method or device used by participants to send images and emotional data they have captured to a server.

[0168] "Analysis means" refers to the method or process by which a server uses artificial intelligence technology to analyze transmitted image and emotion data and select images.

[0169] "Means for generating recommended lists" refers to the process or method of creating a list of recommended images based on the user's selection of images that match their preferences.

[0170] "Adjustment means" refers to a device or method that automatically adjusts the image quality and composition of selected images and performs filtering based on emotional data as needed.

[0171] "Generation means" refers to a device or method that automatically creates an album by combining multiple images using images that richly express the user's emotions.

[0172] "Local processing methods" refer to processes and technologies for processing data within a region and improving communication efficiency.

[0173] "Emotion analysis means" refers to technologies and methods in which a server uses the facial expressions of subjects in an image and audio signals to evaluate the emotional state of a user.

[0174] As a form for carrying out the invention, this system consists of data communication and processing between a user, a terminal, and a server.

[0175] First, users take photos using their devices during the event. These devices have a dedicated application installed for sending the captured images to a server in real time. Using this application, users can also input their emotional data along with the images through reaction buttons and voice recordings.

[0176] Next, the device collects the captured images and emotion data, and uploads this data to a server via wireless communication. This communication uses a standard internet connection.

[0177] Next, the server analyzes the received images and emotion data using artificial intelligence technology. The server is equipped with an "emotion engine" that performs facial expression analysis and speech recognition in images, making it possible to evaluate the user's emotional state. Based on this evaluation, the server selects images and generates a recommendation list of images that match the user's preferences.

[0178] Furthermore, the server automatically adjusts the image quality of the selected images. Image processing software (e.g., a dedicated API) is used to optimize brightness, contrast, and composition. Appropriate filtering and color adjustments are also applied based on emotional data.

[0179] Users can view a list of recommendations from the server on their device, select their favorite images, and share them via social media or email. The server also automatically generates albums that take into account the overall atmosphere of the event and the user's emotional data. The generation AI model highlights emotionally rich moments, providing a customized visual experience.

[0180] As a concrete example, consider a family celebration. The user takes photos during the event and records the "best moments" with voiceovers. The server then automatically creates an album that emphasizes smiles, allowing the whole family to share the joy. A prompt such as, "I've taken photos of a family event, and I'd like to create an album that emphasizes the smiles of the participants. I've recorded voiceovers of the participants enjoying themselves," could be used.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] Users take photos using their devices during the event. As input, users input emotion data (reaction buttons and voice recordings) along with the photos. The device receives this input and sends the captured images and emotion data to the server. As output, the device generates a data package and uploads it to the server via wireless communication.

[0184] Step 2:

[0185] The server receives image data and emotion data transmitted from the terminal. This data is stored on the server as input. The server saves the stored data to a database and begins analyzing it using artificial intelligence technology. Specifically, it performs facial expression analysis of the subject in the image and speech recognition to understand the user's emotional state. The analyzed emotion evaluation data is generated as output.

[0186] Step 3:

[0187] The server selects images that match the user's preferences based on the analysis results. Sentiment evaluation data and image data are used as input. The server analyzes this data and generates a recommendation list based on specific criteria. Specifically, it selects images that emphasize desirable emotional expressions. The output is a list of images recommended to the user.

[0188] Step 4:

[0189] The server automatically adjusts the image quality of the selected images. Images from the recommendation list are retrieved by the server as input. The server uses image processing software to optimize the brightness, contrast, and composition of the images, and performs filtering and color adjustments based on sentiment data. The output is a high-quality, adjusted image.

[0190] Step 5:

[0191] The user checks a list of recommendations provided by the server on their device. As input, a list of adjusted images is delivered to the user's device. The user browses these images and selects their favorites. Specifically, the user shares the selected images via social media, email, etc. As output, the images chosen by the user are shared with others.

[0192] Step 6:

[0193] The server generates a customized album using multiple adjusted images. The input consists of images selected by the user. Utilizing a generative AI model, the server creates an album that highlights moments that richly express the user's emotions. Specifically, image rearrangement and theme-based layouts are applied. The output is a customized album provided to the user.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] The problem that this invention aims to solve is to personalize the customer experience in physical stores and improve the product purchasing and evaluation experience within the store. In particular, there is a need to share images optimized based on emotions, derived from images taken by customers, in real time within the store to increase the purchasing intent of the customer themselves and other visitors. However, conventional store systems cannot provide real-time emotion-based feedback or displays, and challenges remain in improving communication efficiency and the burden of data processing.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes a regional computing means for performing data calculations within a region and improving communication efficiency, a filtering means for applying an optimized filter to an image based on emotion data, and a display means for enabling sharing on a display device. This makes it possible to provide a personalized purchasing experience based on customer emotions in physical stores.

[0199] A "participant" is a person who gathers at an event or activity for a specific purpose.

[0200] A "storage device" is a device that has the function of securely storing digital information and making it available for later retrieval.

[0201] "Uploading" refers to the technical process of transferring digital data from a user's device to a server.

[0202] "Artificial intelligence technology" refers to a group of programs that analyze images and data, enabling them to make decisions similar to those of a human.

[0203] "Analysis methods" refer to methods for analyzing given data in detail and extracting its characteristics and relationships.

[0204] An "optimization method" is a technique for adjusting data and information to a standardized, efficient, and effective form.

[0205] "User" refers to an individual or group that operates and uses a system or service.

[0206] A "display device" is an electronic device that can visually represent digital information.

[0207] "Visual data" refers to digital media files that are represented as information that can be perceived through sight.

[0208] A "regional computing means" is a device or technology that performs computational processing within a specific region to improve efficiency.

[0209] "Filter application means" refers to a technique or method for applying specific effects or adjustments to images or data.

[0210] A "list" is a data set in a format that systematically enumerates related information.

[0211] This invention is a system for providing personalized purchasing experiences based on customer emotions in physical stores. The system mainly consists of customer terminals (such as smartphones) and a server.

[0212] The server first uploads images transmitted from the customer's terminal in real time to a storage device. During this process, an upload mechanism is used to efficiently transfer the data. The data collected by the server includes not only images but also audio data for emotional analysis. Using artificial intelligence technology, the image and emotional data are analyzed to determine the user's emotional state.

[0213] Next, based on the analyzed sentiment data, the image quality and composition are adjusted using optimization means. This adjustment includes filter application means to apply specific visual effects to the image. The adjusted image is displayed in real time through in-store display devices to enhance the visual experience for other visitors and the customer themselves.

[0214] As a concrete example, imagine a customer visiting an accessory store with a friend. They use a device to take photos for in-store display and upload them along with audio recordings expressing their emotions, such as excitement or surprise. The server analyzes the emotional data and adjusts the image by applying a filter that emphasizes smiles. The adjusted image is then displayed in the store, further stimulating purchasing intent and providing a unique store experience.

[0215] Furthermore, as an example of a prompt, a specific question can be posed to the generating AI model: "What filters and adjustments would be suitable for optimizing a photo that reflects the emotions of a user visiting an accessory store with a friend, and displaying it in the store?" This enables more appropriate image adjustments, further enhancing the value of the on-site experience.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user's device takes photos inside the physical store, acquiring image data and audio data expressing emotions. This data becomes the input for uploading. The device uses an upload mechanism to send the data to a storage device and transfers the data to the server.

[0219] Step 2:

[0220] The server receives image and audio data transmitted from the terminal and inputs them into the analysis system. Using artificial intelligence technology, it analyzes the visual information in the images and the emotional expressions in the audio data. Based on this analysis, the server generates emotional data output and evaluates the user's current emotional state.

[0221] Step 3:

[0222] Based on the analyzed emotion data, the server activates optimization mechanisms. It automatically adjusts the brightness and composition of the input image data and applies emotion-based filters. This results in the output of the adjusted image data.

[0223] Step 4:

[0224] The server sends the adjusted image data to the store's display device, allowing the image to be displayed in real time. The displayed image visually impacts the user and other customers in the store, improving the shopping experience.

[0225] Step 5:

[0226] The server uses a generated AI model to create prompt messages to identify the filters and adjustments that best match the user's emotions and the store's atmosphere. By providing these prompt messages to the AI, feedback is obtained to enable more effective image adjustments.

[0227] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0243] This invention is a system for participants to take photos of an event using their own devices, share them smoothly, and create high-quality records. The system includes image processing, AI analysis, content sharing, album generation, and local processing functions.

[0244] First, the "user" takes photos of the event using their own device. This device has a dedicated application installed and provides an interface for quickly processing the captured images. The "device" uploads the captured images to a central "server" using internet communication. The upload also includes metadata such as the time and location information of the photos taken.

[0245] Next, the "server" analyzes the received images. Using AI technology, it evaluates the image's composition, brightness, and the emotional expression of the subject, and selects high-value images based on this information. In this selection process, scoring is performed based on multiple evaluation metrics, and photos that are likely to please the user are proactively added to the recommendation list.

[0246] Subsequently, the selected images undergo automatic quality adjustments on the server. Specifically, brightness and contrast are optimized, and unnecessary elements are cropped, improving their appearance. These adjusted images are then presented to the user as a preview for confirmation.

[0247] Furthermore, images that a "user" has indicated their intention to share are made public to other users and online platforms via the "device." At this time, settings related to the protection of personal information and copyright are also applied to ensure that sharing is conducted safely.

[0248] In addition, the "server" automatically generates event-specific albums using a large number of shared images. These albums are structured to create a narrative, taking into account the time of shooting and the highlights of the event. The final albums are saved as digital books, allowing all participants to reminisce.

[0249] Furthermore, this invention utilizes a "local processing method," which optimizes communication efficiency and provides users with faster feedback by completing data processing within the local area. This reduces the network load at event venues and enables a smoother user experience.

[0250] As a concrete example, consider a school sports day. Parents, as participants, take photos of their children's events with their smartphones, and the images are immediately uploaded to a server. The server uses AI analysis to recommend photos of the most emotionally expressive moments, and the parents share the adjusted images with other family members and on social media. Finally, a dynamic album of the entire sports day is automatically generated and can be viewed online by all participants.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user takes a photo with their device. After taking the photo, a dedicated app automatically starts preparing to upload the image to the server.

[0254] Step 2:

[0255] The device uploads the captured photos to the server along with metadata (e.g., time of capture, GPS information). This provides a centralized management system for image data.

[0256] Step 3:

[0257] The server supplies the received image data to the AI ​​analysis engine. Here, it analyzes characteristics such as image quality, composition, and the subject's facial expression.

[0258] Step 4:

[0259] The server applies a scoring algorithm based on the analysis results, ranking images in order of priority. It selects images that capture particularly valuable moments as recommended content.

[0260] Step 5:

[0261] The server initiates the image quality adjustment process for the selected images. This includes automatic optimization of brightness and contrast, and cropping of the composition.

[0262] Step 6:

[0263] The user visualizes image previews from the server on their device and selects which images to share from the recommended images. The next action is determined based on the user's selection.

[0264] Step 7:

[0265] The device shares images selected by the user to the communication platform or other participants. Necessary settings are applied for security and privacy.

[0266] Step 8:

[0267] The server uses an album generation module to assemble an album of the entire event based on the collected shared images. Considering the time of shooting and highlights of the content, a visually appealing album is created.

[0268] Step 9:

[0269] The local processing mechanism within the server optimizes the processing data locally. This improves the efficiency of the local network and speeds up overall response times.

[0270] (Example 1)

[0271] Next, we will describe Example 1. 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."

[0272] Current technologies often require manual data selection and optimization for the rapid and efficient management and sharing of digital data, demanding significant time and effort from users. Furthermore, data sharing frequently raises security and privacy concerns. Additionally, increased network load can reduce communication efficiency, potentially impairing the user experience. Addressing these challenges is crucial.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes a transmission means for transmitting digital data to an information processing device, an analysis means for analyzing the digital data using artificial intelligence technology and selecting the data, and an optimization means for automatically optimizing the quality and content of the digital data. This enables users to efficiently manage and securely share digital data.

[0275] A "participant" is an individual or group that uses the system to generate and process digital data.

[0276] "Digital data" refers to electronic information, including captured images and videos, and their associated metadata.

[0277] An "information processing device" is a device capable of transmitting, storing, and analyzing digital data, and generally refers to a server or computer.

[0278] A "transmission means" is a mechanism for appropriately transmitting digital data generated by participants to an information processing device.

[0279] "Analysis methods" refer to systems that use artificial intelligence technology to analyze digital data and evaluate the value and characteristics of the information.

[0280] "Optimization techniques" are processing technologies used to automatically improve the quality and composition of digital data.

[0281] "User" refers to an individual or group who makes decisions to view and share optimized digital data.

[0282] "Distribution means" refers to a method for securely sharing digital data selected by a user with other users or platforms.

[0283] "Collection" refers to a digital album or library that collects a plurality of selected and shared digital data and is constructed based on a certain theme or purpose.

[0284] "Regional processing means" refers to a mechanism for distributing and processing digital data within a region to improve communication efficiency.

[0285] An embodiment of this system will be described.

[0286] The present invention is a system that enables participants to efficiently manage and share digital data in events and the like. Specifically, three entities, namely a server, a terminal, and a user, cooperate to realize the generation, transmission, analysis, optimization, and sharing of digital data.

[0287] First, the user uses a terminal such as a smartphone or a tablet to generate digital data, such as photos or videos. A dedicated application is installed on this terminal, and the user can operate the data through a simple interface. The generated data is transmitted to the server via the Internet using a transmission means together with metadata.

[0288] Next, the server analyzes the received digital data by an analysis means. The generative AI model is used here. The server utilizes this model to evaluate the quality and composition of the data and perform scoring. Through this analysis process, the selection of data required by the user is made.

[0289] Subsequently, the server uses optimization techniques to improve the quality of the digital data. Specifically, it automatically adjusts the brightness and contrast of the data and trims off unnecessary parts to enhance the visual appeal of the data. Once the optimization is complete, the data is sent from the server to the terminal, and a preview is provided to the user.

[0290] Data that a user has confirmed and indicated their intention to share is shared from their device to other users or social media platforms via distribution methods. During this process, settings regarding personal information and copyright of the data are applied to ensure secure sharing.

[0291] Furthermore, by having servers process large amounts of digital data within a region using local processing methods, communication efficiency is improved and network load is reduced. This allows users to receive feedback more quickly.

[0292] As a concrete example, consider a photo taken by a user at a school sports day. Immediately after taking the photo, it is uploaded to a server, then undergoes AI analysis, and high-quality photos that capture emotionally rich moments are selected. After adjustment, it can be easily shared with other family members or on social media. An example of a prompt message to the generative AI model involved in this process would be the instruction, "Identify the emotionally rich moments of the event and save them as an album."

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users capture event footage using a device equipped with a dedicated application and generate digital data. The input is video data from the camera sensor, and the output is the generated digital image data. Specifically, the user operates the device's camera function to capture images, and the application saves that data to internal storage.

[0296] Step 2:

[0297] The device uploads stored digital data and its metadata (such as date and time of capture and location information) to a server via a network transmission method. The input is the generated digital data and metadata, and the output is the data stored in the server's storage device. Specifically, the data is compressed using Wi-Fi or mobile communication and sent to the server using a secure protocol.

[0298] Step 3:

[0299] The server analyzes the received digital data using artificial intelligence technology and analytical tools. The input is the uploaded data, and the output is selected important digital data. Specifically, the server activates an AI model, scans the data content to evaluate facial expressions and composition, and scores based on certain evaluation criteria. In this process, an emotion analysis algorithm is used to identify emotionally rich moments.

[0300] Step 4:

[0301] The server automatically improves the image quality of digital data using optimization techniques based on the analysis results. The input is selected digital data within the server, and the output is digital data with adjusted image quality. Specifically, it automatically adjusts the brightness and contrast of the data and crops the background to optimize its visual appeal.

[0302] Step 5:

[0303] The server returns optimized digital data to the terminal, which the user reviews through the terminal's interface. The input is the optimized digital data sent from the server, and the output is the digital data reviewed by the user. Specific operations include displaying the data on the device screen using preview software.

[0304] Step 6:

[0305] When the user indicates an intention to share the selected data, the terminal distributes the selected digital data to other users or platforms by means of distribution. The input is digital data that has been confirmed by the user, and the output is digital data shared with the specified destination. As a specific process, the data is encrypted at the endpoint and transmitted to the specified recipient using a secure protocol such as HTTPS.

[0306] Step 7:

[0307] The server processes a large number of shared digital data using regional processing means. The input is digital data shared after distribution, and the output is a verified digital collection. Specifically, the data is distributed and analyzed at processing facilities within the region, and caching technology for improving communication efficiency and reducing the regional network load is utilized.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In modern e-commerce transactions, it is important to visually record and efficiently share the purchase experience. However, there is a lack of appropriate means for users to properly photograph the images of the purchased goods and easily share them with friends or on social networks. As a result, there is a problem that the recollection of the purchase experience and sharing with others are less active, and the satisfaction in the purchasing activity decreases.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0312] In this invention, the server includes upload means for transmitting images taken by participants to a data storage device, analysis means for analyzing the transmitted images using intelligent technology and selecting images, and recording means for recording images of product use based on purchase history and facilitating posting by users on social platforms. This enables users to smoothly record their experiences with purchased products and share them in a visually rich way.

[0313] "Participants" are people who take part in events or activities and photograph them with their own devices.

[0314] A "data storage device" is a device that efficiently stores captured images and makes them accessible later.

[0315] "Uploading means" refers to a method or device for transmitting images taken by participants to a data storage device.

[0316] "Intelligent technology" refers to techniques that use artificial intelligence and machine learning to analyze the composition and content of images.

[0317] "Analysis means" refers to a method or apparatus for evaluating transmitted images using intelligent technology and selecting appropriate images.

[0318] "Generation means" refers to a method or apparatus for automatically constructing an attractive photo collection based on a set of images.

[0319] A "regional processing method" is a method or device for processing data within a specific region and maximizing communication efficiency.

[0320] "Recording means" refers to a method or device for managing images of product use based on purchase history and facilitating easy sharing.

[0321] The following describes a mode for carrying out the invention. The system that realizes this application aims to efficiently manage images taken by participants and share them in a visually appealing way.

[0322] First, the user's device provides an interface for taking images at events, shopping trips, and other locations. This device is often a smartphone or tablet, and it processes image data through a dedicated application installed on the device. The image data is uploaded to a server via the internet. At the same time, metadata such as the time of capture and location information is also transmitted.

[0323] The server uses intelligent technology to analyze the received images. Specifically, it evaluates images using machine learning and artificial intelligence (AI) technologies, such as software like TensorFlow and OpenCV. The server scores the composition, color information, and emotional expression of the subjects in the incoming images and selects the most valuable images. Furthermore, it applies adjustments to the selected images, such as brightness and contrast adjustments and cropping of unnecessary elements.

[0324] The adjusted images are provided to the user as a preview and can be shared on online social platforms and within communities at the user's discretion. The server also integrates with the user's purchase history information and includes recording mechanisms for documenting the use of purchased items and review images, thereby helping users share their experiences attractively on social media.

[0325] As a concrete example, when a customer purchases new shoes at a shopping mall, they take a photo of the product and upload it to a server. The server analyzes the photo and suggests the most suitable filters and layouts. By posting this to social media, the user can visually promote their new purchase to friends and followers, and also create an album to reflect on their shopping experience.

[0326] An example of a prompt used when giving instructions to a generative AI model is a customized input such as, "Please suggest the best filter and layout to share the new sneakers in this photo in the best possible way." This allows the AI ​​to receive specific instructions for automatically optimizing the photo according to the user's wishes.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user takes an image using their device. This image contains metadata such as the time and location information. The captured image becomes input that is sent to a data storage device via the application.

[0330] Step 2:

[0331] The device uploads the captured image, along with the time and location information, to a server via the internet. The server then receives the image data. The image metadata is also transmitted during this upload.

[0332] Step 3:

[0333] The server analyzes the received images. Using intelligent technologies such as TensorFlow, it scores the image's composition, color information, and the subject's emotion. The input is images uploaded by the user, and the output is a list of valuable images.

[0334] Step 4:

[0335] The server uses OpenCV to adjust the brightness and contrast of the selected images and crop out unnecessary elements. The input is a list of images selected in step 3, and the output is the adjusted image.

[0336] Step 5:

[0337] The server provides the user with a preview of the adjusted image. The user reviews the image and optionally shares it on social platforms. The input is the adjusted image, and the selected image is shared as the output.

[0338] Step 6:

[0339] The server uses purchase history information to record images of users using their purchased items. This makes it easy for users to visually record and share product reviews and usage scenarios.

[0340] Step 7:

[0341] The server uses a generative AI model to suggest optimal filters and layouts based on user input, optimizing photos for sharing on social media. Here, example prompts are crucial, instructing the generative AI on specific desired transformations.

[0342] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0343] This invention provides a system that streamlines the photography and sharing process at events, and further generates and adjusts images while taking user emotions into consideration. The system includes a series of processes for managing, selecting, and adjusting images taken by participants on a server.

[0344] The system begins with the "user" taking photos during the event using their own device. This device has an application installed to upload the captured images to the server in real time. Along with the captured images, the "device" can also send simple emotional data from the user (e.g., reaction buttons or voice recordings) to the server.

[0345] Next, the "server" performs an artificial intelligence analysis on the received images and emotion data. In this analysis, the "emotion engine" evaluates the facial expressions and audio signals of the subjects in the images to understand the user's emotional state. Based on the results, it applies this as feedback in image selection and adjustment, and adds images that particularly match the user's preferences to the recommendation list.

[0346] The selected images automatically enter an image quality adjustment process, where their brightness, contrast, and composition are optimized on the server. Image filtering and color adjustments may also be applied based on sentiment data. After adjustment, the user can review the images on their device and choose which ones to share.

[0347] Furthermore, the "server" generates customized albums that reflect emotional data to provide a new visual experience from a large number of images. This album generation incorporates not only event highlights but also moments that richly express the user's emotions.

[0348] As a concrete example, consider a family celebration event. After a family member takes a photo with their smartphone, they send the photo along with an audio recording of their emotional expression to the server. Using this information, the server analyzes the photo and automatically filters it to highlight the family's smiles. Finally, an album that captures the emotions of the event is generated and shared with everyone. In this way, the present invention optimizes event recording while taking the user's emotions into consideration.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] Users film the event with their own devices. The devices are equipped with an interface for recording the user's emotional responses along with the images.

[0352] Step 2:

[0353] The device uploads captured images and emotion data to a server. Emotion data is recorded as simple reaction buttons or voice input.

[0354] Step 3:

[0355] The server supplies data to the AI ​​analysis engine and the emotion engine to analyze the received image and emotion data. The AI ​​analysis engine evaluates the composition and quality of the images, while the emotion engine analyzes the user's emotional state.

[0356] Step 4:

[0357] The server scores images based on the analysis results, and in particular selects images with high recommendation scores based on sentiment data. This selection determines how well the images align with the user's preferences.

[0358] Step 5:

[0359] The server adjusts the image quality and composition of the selected images. This includes adjusting brightness, contrast, and cropping, as well as applying filters and adjusting color tones as needed.

[0360] Step 6:

[0361] The user reviews the adjusted image on their device. The user can then select the image to share and direct the next steps based on that selection.

[0362] Step 7:

[0363] The device shares the selected images with other participants and online platforms. Security settings and privacy policies are applied.

[0364] Step 8:

[0365] The server generates customized albums based on the user's emotions, using the shared image collection as a basis. The albums are structured to highlight moments in time when the user's emotions were expressed through the events.

[0366] Step 9:

[0367] The local processing mechanism within the server improves local communication efficiency by optimizing the processing data locally. This shortens the time to final output, enabling faster service to users.

[0368] (Example 2)

[0369] Next, we will describe Example 2. 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".

[0370] When streamlining the photo-taking and sharing process at events, and further generating and adjusting images that take user emotions into consideration, conventional systems have struggled to appropriately reflect user emotions in image selection and adjustment. Furthermore, there is a need to improve communication efficiency when using cloud servers. A new system is needed to solve these challenges.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes upload means for transmitting images and emotional data taken by participants to the server, analysis means for analyzing the transmitted images and emotional data using artificial intelligence technology and selecting images, and means for generating a list of recommended images that match the user's preferences based on the analysis results. This enables efficient image selection and adjustment that takes the user's emotions into consideration, as well as improved communication efficiency.

[0373] "Uploading method" refers to the method or device used by participants to send images and emotional data they have captured to a server.

[0374] "Analysis means" refers to the method or process by which a server uses artificial intelligence technology to analyze transmitted image and emotion data and select images.

[0375] "Means for generating recommended lists" refers to the process or method of creating a list of recommended images based on the user's selection of images that match their preferences.

[0376] "Adjustment means" refers to a device or method that automatically adjusts the image quality and composition of selected images and performs filtering based on emotional data as needed.

[0377] "Generation means" refers to a device or method that automatically creates an album by combining multiple images using images that richly express the user's emotions.

[0378] "Local processing methods" refer to processes and technologies for processing data within a region and improving communication efficiency.

[0379] "Emotion analysis means" refers to technologies and methods in which a server uses the facial expressions of subjects in an image and audio signals to evaluate the emotional state of a user.

[0380] As a form for carrying out the invention, this system consists of data communication and processing between a user, a terminal, and a server.

[0381] First, users take photos using their devices during the event. These devices have a dedicated application installed for sending the captured images to a server in real time. Using this application, users can also input their emotional data along with the images through reaction buttons and voice recordings.

[0382] Next, the device collects the captured images and emotion data, and uploads this data to a server via wireless communication. This communication uses a standard internet connection.

[0383] Next, the server analyzes the received images and emotion data using artificial intelligence technology. The server is equipped with an "emotion engine" that performs facial expression analysis and speech recognition in images, making it possible to evaluate the user's emotional state. Based on this evaluation, the server selects images and generates a recommendation list of images that match the user's preferences.

[0384] Furthermore, the server automatically adjusts the image quality of the selected images. Image processing software (e.g., a dedicated API) is used to optimize brightness, contrast, and composition. Appropriate filtering and color adjustments are also applied based on emotional data.

[0385] Users can view a list of recommendations from the server on their device, select their favorite images, and share them via social media or email. The server also automatically generates albums that take into account the overall atmosphere of the event and the user's emotional data. The generation AI model highlights emotionally rich moments, providing a customized visual experience.

[0386] As a concrete example, consider a family celebration. The user takes photos during the event and records the "best moments" with voiceovers. The server then automatically creates an album that emphasizes smiles, allowing the whole family to share the joy. A prompt such as, "I've taken photos of a family event, and I'd like to create an album that emphasizes the smiles of the participants. I've recorded voiceovers of the participants enjoying themselves," could be used.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] Users take photos using their devices during the event. As input, users input emotion data (reaction buttons and voice recordings) along with the photos. The device receives this input and sends the captured images and emotion data to the server. As output, the device generates a data package and uploads it to the server via wireless communication.

[0390] Step 2:

[0391] The server receives image data and emotion data transmitted from the terminal. This data is stored on the server as input. The server saves the stored data to a database and begins analyzing it using artificial intelligence technology. Specifically, it performs facial expression analysis of the subject in the image and speech recognition to understand the user's emotional state. The analyzed emotion evaluation data is generated as output.

[0392] Step 3:

[0393] The server selects images that match the user's preferences based on the analysis results. Sentiment evaluation data and image data are used as input. The server analyzes this data and generates a recommendation list based on specific criteria. Specifically, it selects images that emphasize desirable emotional expressions. The output is a list of images recommended to the user.

[0394] Step 4:

[0395] The server automatically adjusts the image quality of the selected images. Images from the recommendation list are retrieved by the server as input. The server uses image processing software to optimize the brightness, contrast, and composition of the images, and performs filtering and color adjustments based on sentiment data. The output is a high-quality, adjusted image.

[0396] Step 5:

[0397] The user checks a list of recommendations provided by the server on their device. As input, a list of adjusted images is delivered to the user's device. The user browses these images and selects their favorites. Specifically, the user shares the selected images via social media, email, etc. As output, the images chosen by the user are shared with others.

[0398] Step 6:

[0399] The server generates a customized album using multiple adjusted images. The input consists of images selected by the user. Utilizing a generative AI model, the server creates an album that highlights moments that richly express the user's emotions. Specifically, image rearrangement and theme-based layouts are applied. The output is a customized album provided to the user.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] The problem that this invention aims to solve is to personalize the customer experience in physical stores and improve the product purchasing and evaluation experience within the store. In particular, there is a need to share images optimized based on emotions, derived from images taken by customers, in real time within the store to increase the purchasing intent of the customer themselves and other visitors. However, conventional store systems cannot provide real-time emotion-based feedback or displays, and challenges remain in improving communication efficiency and the burden of data processing.

[0403] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0404] In this invention, the server includes a regional computing means for performing data calculations within a region and improving communication efficiency, a filtering means for applying an optimized filter to an image based on emotion data, and a display means for enabling sharing on a display device. This makes it possible to provide a personalized purchasing experience based on customer emotions in physical stores.

[0405] A "participant" is a person who gathers at an event or activity for a specific purpose.

[0406] A "storage device" is a device that has the function of securely storing digital information and making it available for later retrieval.

[0407] "Uploading" refers to the technical process of transferring digital data from a user's device to a server.

[0408] "Artificial intelligence technology" refers to a group of programs that analyze images and data, enabling them to make decisions similar to those of a human.

[0409] "Analysis methods" refer to methods for analyzing given data in detail and extracting its characteristics and relationships.

[0410] An "optimization method" is a technique for adjusting data and information to a standardized, efficient, and effective form.

[0411] "User" refers to an individual or group that operates and uses a system or service.

[0412] A "display device" is an electronic device that can visually represent digital information.

[0413] "Visual data" refers to digital media files that are represented as information that can be perceived through sight.

[0414] A "regional computing means" is a device or technology that performs computational processing within a specific region to improve efficiency.

[0415] "Filter application means" refers to a technique or method for applying specific effects or adjustments to images or data.

[0416] A "list" is a data set in a format that systematically enumerates related information.

[0417] This invention is a system for providing personalized purchasing experiences based on customer emotions in physical stores. The system mainly consists of customer terminals (such as smartphones) and a server.

[0418] The server first uploads images transmitted from the customer's terminal in real time to a storage device. During this process, an upload mechanism is used to efficiently transfer the data. The data collected by the server includes not only images but also audio data for emotional analysis. Using artificial intelligence technology, the image and emotional data are analyzed to determine the user's emotional state.

[0419] Next, based on the analyzed sentiment data, the image quality and composition are adjusted using optimization means. This adjustment includes filter application means to apply specific visual effects to the image. The adjusted image is displayed in real time through in-store display devices to enhance the visual experience for other visitors and the customer themselves.

[0420] As a concrete example, imagine a customer visiting an accessory store with a friend. They use a device to take photos for in-store display and upload them along with audio recordings expressing their emotions, such as excitement or surprise. The server analyzes the emotional data and adjusts the image by applying a filter that emphasizes smiles. The adjusted image is then displayed in the store, further stimulating purchasing intent and providing a unique store experience.

[0421] Furthermore, as an example of a prompt, a specific question can be posed to the generating AI model: "What filters and adjustments would be suitable for optimizing a photo that reflects the emotions of a user visiting an accessory store with a friend, and displaying it in the store?" This enables more appropriate image adjustments, further enhancing the value of the on-site experience.

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] The user's device takes photos inside the physical store, acquiring image data and audio data expressing emotions. This data becomes the input for uploading. The device uses an upload mechanism to send the data to a storage device and transfers the data to the server.

[0425] Step 2:

[0426] The server receives image and audio data transmitted from the terminal and inputs them into the analysis system. Using artificial intelligence technology, it analyzes the visual information in the images and the emotional expressions in the audio data. Based on this analysis, the server generates emotional data output and evaluates the user's current emotional state.

[0427] Step 3:

[0428] Based on the analyzed emotion data, the server activates optimization mechanisms. It automatically adjusts the brightness and composition of the input image data and applies emotion-based filters. This results in the output of the adjusted image data.

[0429] Step 4:

[0430] The server sends the adjusted image data to the store's display device, allowing the image to be displayed in real time. The displayed image visually impacts the user and other customers in the store, improving the shopping experience.

[0431] Step 5:

[0432] The server uses a generated AI model to create prompt messages to identify the filters and adjustments that best match the user's emotions and the store's atmosphere. By providing these prompt messages to the AI, feedback is obtained to enable more effective image adjustments.

[0433] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0434] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0439] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0441] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0443] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0444] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0445] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0446] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0448] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0449] This invention is a system for participants to take photos of an event using their own devices, share them smoothly, and create high-quality records. The system includes image processing, AI analysis, content sharing, album generation, and local processing functions.

[0450] First, the "user" takes photos of the event using their own device. This device has a dedicated application installed and provides an interface for quickly processing the captured images. The "device" uploads the captured images to a central "server" using internet communication. The upload also includes metadata such as the time and location information of the photos taken.

[0451] Next, the "server" analyzes the received images. Using AI technology, it evaluates the image's composition, brightness, and the emotional expression of the subject, and selects high-value images based on this information. In this selection process, scoring is performed based on multiple evaluation metrics, and photos that are likely to please the user are proactively added to the recommendation list.

[0452] Subsequently, the selected images undergo automatic quality adjustments on the server. Specifically, brightness and contrast are optimized, and unnecessary elements are cropped, improving their appearance. These adjusted images are then presented to the user as a preview for confirmation.

[0453] Furthermore, images that a "user" has indicated their intention to share are made public to other users and online platforms via the "device." At this time, settings related to the protection of personal information and copyright are also applied to ensure that sharing is conducted safely.

[0454] In addition, the "server" automatically generates event-specific albums using a large number of shared images. These albums are structured to create a narrative, taking into account the time of shooting and the highlights of the event. The final albums are saved as digital books, allowing all participants to reminisce.

[0455] Furthermore, this invention utilizes a "local processing method," which optimizes communication efficiency and provides users with faster feedback by completing data processing within the local area. This reduces the network load at event venues and enables a smoother user experience.

[0456] As a concrete example, consider a school sports day. Parents, as participants, take photos of their children's events with their smartphones, and the images are immediately uploaded to a server. The server uses AI analysis to recommend photos of the most emotionally expressive moments, and the parents share the adjusted images with other family members and on social media. Finally, a dynamic album of the entire sports day is automatically generated and can be viewed online by all participants.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The user takes a photo with their device. After taking the photo, a dedicated app automatically starts preparing to upload the image to the server.

[0460] Step 2:

[0461] The device uploads the captured photos to the server along with metadata (e.g., time of capture, GPS information). This provides a centralized management system for image data.

[0462] Step 3:

[0463] The server supplies the received image data to the AI ​​analysis engine. Here, it analyzes characteristics such as image quality, composition, and the subject's facial expression.

[0464] Step 4:

[0465] The server applies a scoring algorithm based on the analysis results, ranking images in order of priority. It selects images that capture particularly valuable moments as recommended content.

[0466] Step 5:

[0467] The server initiates the image quality adjustment process for the selected images. This includes automatic optimization of brightness and contrast, and cropping of the composition.

[0468] Step 6:

[0469] The user visualizes image previews from the server on their device and selects which images to share from the recommended images. The next action is determined based on the user's selection.

[0470] Step 7:

[0471] The device shares images selected by the user to the communication platform or other participants. Necessary settings are applied for security and privacy.

[0472] Step 8:

[0473] The server uses an album generation module to assemble an album of the entire event based on the collected shared images. Considering the time of shooting and highlights of the content, a visually appealing album is created.

[0474] Step 9:

[0475] The local processing mechanism within the server optimizes the processing data locally. This improves the efficiency of the local network and speeds up overall response times.

[0476] (Example 1)

[0477] Next, we will describe Example 1. 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."

[0478] Current technologies often require manual data selection and optimization for the rapid and efficient management and sharing of digital data, demanding significant time and effort from users. Furthermore, data sharing frequently raises security and privacy concerns. Additionally, increased network load can reduce communication efficiency, potentially impairing the user experience. Addressing these challenges is crucial.

[0479] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0480] In this invention, the server includes a transmission means for transmitting digital data to an information processing device, an analysis means for analyzing the digital data using artificial intelligence technology and selecting the data, and an optimization means for automatically optimizing the quality and content of the digital data. This enables users to efficiently manage and securely share digital data.

[0481] A "participant" is an individual or group that uses the system to generate and process digital data.

[0482] "Digital data" refers to electronic information, including captured images and videos, and their associated metadata.

[0483] An "information processing device" is a device capable of transmitting, storing, and analyzing digital data, and generally refers to a server or computer.

[0484] A "transmission means" is a mechanism for appropriately transmitting digital data generated by participants to an information processing device.

[0485] "Analysis methods" refer to systems that use artificial intelligence technology to analyze digital data and evaluate the value and characteristics of the information.

[0486] "Optimization techniques" are processing technologies used to automatically improve the quality and composition of digital data.

[0487] A "user" is an individual or group that reviews and makes decisions regarding the sharing of optimized digital data.

[0488] "Distribution method" refers to a method for securely sharing digital data selected by a user with other users or platforms.

[0489] A "collection" is a digital album or library that brings together selected and shared digital data, built around a specific theme or purpose.

[0490] A "regional processing method" is a mechanism for distributing and processing digital data within a region to improve communication efficiency.

[0491] An embodiment of this system will be described.

[0492] This invention provides a system that allows participants to efficiently manage and share digital data at events and other occasions. Specifically, it involves collaboration between three entities—a server, a terminal, and a user—to generate, transmit, analyze, optimize, and share digital data.

[0493] First, users generate digital data, such as photos and videos, using devices like smartphones and tablets. These devices have dedicated applications installed, allowing users to manipulate the data through a user-friendly interface. The generated data, along with metadata, is then transmitted to a server via the internet using a transmission method.

[0494] Next, the server analyzes the received digital data using analytical tools. This involves the use of a generative AI model. The server utilizes this model to evaluate the data's quality and structure, and then performs scoring. This analysis process selects the data the user is looking for.

[0495] Subsequently, the server uses optimization techniques to improve the quality of the digital data. Specifically, it automatically adjusts the brightness and contrast of the data and trims off unnecessary parts to enhance the visual appeal of the data. Once the optimization is complete, the data is sent from the server to the terminal, and a preview is provided to the user.

[0496] Data that a user has confirmed and indicated their intention to share is shared from their device to other users or social media platforms via distribution methods. During this process, settings regarding personal information and copyright of the data are applied to ensure secure sharing.

[0497] Furthermore, by having servers process large amounts of digital data within a region using local processing methods, communication efficiency is improved and network load is reduced. This allows users to receive feedback more quickly.

[0498] As a concrete example, consider a photo taken by a user at a school sports day. Immediately after taking the photo, it is uploaded to a server, then undergoes AI analysis, and high-quality photos that capture emotionally rich moments are selected. After adjustment, it can be easily shared with other family members or on social media. An example of a prompt message to the generative AI model involved in this process would be the instruction, "Identify the emotionally rich moments of the event and save them as an album."

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] Users capture event footage using a device equipped with a dedicated application and generate digital data. The input is video data from the camera sensor, and the output is the generated digital image data. Specifically, the user operates the device's camera function to capture images, and the application saves that data to internal storage.

[0502] Step 2:

[0503] The device uploads stored digital data and its metadata (such as date and time of capture and location information) to a server via a network transmission method. The input is the generated digital data and metadata, and the output is the data stored in the server's storage device. Specifically, the data is compressed using Wi-Fi or mobile communication and sent to the server using a secure protocol.

[0504] Step 3:

[0505] The server analyzes the received digital data using artificial intelligence technology and analytical tools. The input is the uploaded data, and the output is selected important digital data. Specifically, the server activates an AI model, scans the data content to evaluate facial expressions and composition, and scores based on certain evaluation criteria. In this process, an emotion analysis algorithm is used to identify emotionally rich moments.

[0506] Step 4:

[0507] The server automatically improves the image quality of digital data using optimization techniques based on the analysis results. The input is selected digital data within the server, and the output is digital data with adjusted image quality. Specifically, it automatically adjusts the brightness and contrast of the data and crops the background to optimize its visual appeal.

[0508] Step 5:

[0509] The server returns optimized digital data to the terminal, which the user reviews through the terminal's interface. The input is the optimized digital data sent from the server, and the output is the digital data reviewed by the user. Specific operations include displaying the data on the device screen using preview software.

[0510] Step 6:

[0511] When a user indicates their intention to share selected data, the device distributes the selected digital data to other users or platforms via a distribution method. The input is digital data confirmed by the user, and the output is digital data shared with the specified recipient. Specifically, the data is encrypted at the endpoint and sent to the designated recipient using a secure protocol such as HTTPS.

[0512] Step 7:

[0513] The server processes a large amount of shared digital data using local processing methods. The input is digital data shared after distribution, and the output is a verified digital collection. Specifically, the data is analyzed in a distributed manner at local processing facilities, and caching technology is used to improve communication efficiency and reduce the load on the local network.

[0514] (Application Example 1)

[0515] Next, we will explain Application Example 1. In the following explanation, 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."

[0516] In modern e-commerce, visually recording and efficiently sharing the purchasing experience is crucial. However, there is a lack of suitable means for users to properly photograph their purchased items and easily share them with friends and on social networks. As a result, reflection on and sharing of the purchasing experience with others is sluggish, leading to a decrease in satisfaction with the purchasing activity.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0518] In this invention, the server includes upload means for transmitting images taken by participants to a data storage device, analysis means for analyzing the transmitted images using intelligent technology and selecting images, and recording means for recording images of product use based on purchase history and facilitating posting by users on social platforms. This enables users to smoothly record their experiences with purchased products and share them in a visually rich way.

[0519] "Participants" are people who take part in events or activities and photograph them with their own devices.

[0520] A "data storage device" is a device that efficiently stores captured images and makes them accessible later.

[0521] "Uploading means" refers to a method or device for transmitting images taken by participants to a data storage device.

[0522] "Intelligent technology" refers to techniques that use artificial intelligence and machine learning to analyze the composition and content of images.

[0523] "Analysis means" refers to a method or apparatus for evaluating transmitted images using intelligent technology and selecting appropriate images.

[0524] "Generation means" refers to a method or apparatus for automatically constructing an attractive photo collection based on a set of images.

[0525] A "regional processing method" is a method or device for processing data within a specific region and maximizing communication efficiency.

[0526] "Recording means" refers to a method or device for managing images of product use based on purchase history and facilitating easy sharing.

[0527] The following describes a mode for carrying out the invention. The system that realizes this application aims to efficiently manage images taken by participants and share them in a visually appealing way.

[0528] First, the user's device provides an interface for taking images at events, shopping trips, and other locations. This device is often a smartphone or tablet, and it processes image data through a dedicated application installed on the device. The image data is uploaded to a server via the internet. At the same time, metadata such as the time of capture and location information is also transmitted.

[0529] The server uses intelligent technology to analyze the received images. Specifically, it evaluates images using machine learning and artificial intelligence (AI) technologies, such as software like TensorFlow and OpenCV. The server scores the composition, color information, and emotional expression of the subjects in the incoming images and selects the most valuable images. Furthermore, it applies adjustments to the selected images, such as brightness and contrast adjustments and cropping of unnecessary elements.

[0530] The adjusted images are provided to the user as a preview and can be shared on online social platforms and within communities at the user's discretion. The server also integrates with the user's purchase history information and includes recording mechanisms for documenting the use of purchased items and review images, thereby helping users share their experiences attractively on social media.

[0531] As a concrete example, when a customer purchases new shoes at a shopping mall, they take a photo of the product and upload it to a server. The server analyzes the photo and suggests the most suitable filters and layouts. By posting this to social media, the user can visually promote their new purchase to friends and followers, and also create an album to reflect on their shopping experience.

[0532] An example of a prompt used when giving instructions to a generative AI model is a customized input such as, "Please suggest the best filter and layout to share the new sneakers in this photo in the best possible way." This allows the AI ​​to receive specific instructions for automatically optimizing the photo according to the user's wishes.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The user takes an image using their device. This image contains metadata such as the time and location information. The captured image becomes input that is sent to a data storage device via the application.

[0536] Step 2:

[0537] The device uploads the captured image, along with the time and location information, to a server via the internet. The server then receives the image data. The image metadata is also transmitted during this upload.

[0538] Step 3:

[0539] The server analyzes the received images. Using intelligent technologies such as TensorFlow, it scores the image's composition, color information, and the subject's emotion. The input is images uploaded by the user, and the output is a list of valuable images.

[0540] Step 4:

[0541] The server uses OpenCV to adjust the brightness and contrast of the selected images and crop out unnecessary elements. The input is a list of images selected in step 3, and the output is the adjusted image.

[0542] Step 5:

[0543] The server provides the user with a preview of the adjusted image. The user reviews the image and optionally shares it on social platforms. The input is the adjusted image, and the selected image is shared as the output.

[0544] Step 6:

[0545] The server uses purchase history information to record images of users using their purchased items. This makes it easy for users to visually record and share product reviews and usage scenarios.

[0546] Step 7:

[0547] The server uses a generative AI model to suggest optimal filters and layouts based on user input, optimizing photos for sharing on social media. Here, example prompts are crucial, instructing the generative AI on specific desired transformations.

[0548] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0549] This invention provides a system that streamlines the photography and sharing process at events, and further generates and adjusts images while taking user emotions into consideration. The system includes a series of processes for managing, selecting, and adjusting images taken by participants on a server.

[0550] The system begins with the "user" taking photos during the event using their own device. This device has an application installed to upload the captured images to the server in real time. Along with the captured images, the "device" can also send simple emotional data from the user (e.g., reaction buttons or voice recordings) to the server.

[0551] Next, the "server" performs an artificial intelligence analysis on the received images and emotion data. In this analysis, the "emotion engine" evaluates the facial expressions and audio signals of the subjects in the images to understand the user's emotional state. Based on the results, it applies this as feedback in image selection and adjustment, and adds images that particularly match the user's preferences to the recommendation list.

[0552] The selected images automatically enter an image quality adjustment process, where their brightness, contrast, and composition are optimized on the server. Image filtering and color adjustments may also be applied based on sentiment data. After adjustment, the user can review the images on their device and choose which ones to share.

[0553] Furthermore, the "server" generates customized albums that reflect emotional data to provide a new visual experience from a large number of images. This album generation incorporates not only event highlights but also moments that richly express the user's emotions.

[0554] As a concrete example, consider a family celebration event. After a family member takes a photo with their smartphone, they send the photo along with an audio recording of their emotional expression to the server. Using this information, the server analyzes the photo and automatically filters it to highlight the family's smiles. Finally, an album that captures the emotions of the event is generated and shared with everyone. In this way, the present invention optimizes event recording while taking the user's emotions into consideration.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users film the event with their own devices. The devices are equipped with an interface for recording the user's emotional responses along with the images.

[0558] Step 2:

[0559] The device uploads captured images and emotion data to a server. Emotion data is recorded as simple reaction buttons or voice input.

[0560] Step 3:

[0561] The server supplies data to the AI ​​analysis engine and the emotion engine to analyze the received image and emotion data. The AI ​​analysis engine evaluates the composition and quality of the images, while the emotion engine analyzes the user's emotional state.

[0562] Step 4:

[0563] The server scores images based on the analysis results, and in particular selects images with high recommendation scores based on sentiment data. This selection determines how well the images align with the user's preferences.

[0564] Step 5:

[0565] The server adjusts the image quality and composition of the selected images. This includes adjusting brightness, contrast, and cropping, as well as applying filters and adjusting color tones as needed.

[0566] Step 6:

[0567] The user reviews the adjusted image on their device. The user can then select the image to share and direct the next steps based on that selection.

[0568] Step 7:

[0569] The device shares the selected images with other participants and online platforms. Security settings and privacy policies are applied.

[0570] Step 8:

[0571] The server generates customized albums based on the user's emotions, using the shared image collection as a basis. The albums are structured to highlight moments in time when the user's emotions were expressed through the events.

[0572] Step 9:

[0573] The local processing mechanism within the server improves local communication efficiency by optimizing the processing data locally. This shortens the time to final output, enabling faster service to users.

[0574] (Example 2)

[0575] Next, we will describe Example 2. 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."

[0576] When streamlining the photo-taking and sharing process at events, and further generating and adjusting images that take user emotions into consideration, conventional systems have struggled to appropriately reflect user emotions in image selection and adjustment. Furthermore, there is a need to improve communication efficiency when using cloud servers. A new system is needed to solve these challenges.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0578] In this invention, the server includes upload means for transmitting images and emotional data taken by participants to the server, analysis means for analyzing the transmitted images and emotional data using artificial intelligence technology and selecting images, and means for generating a list of recommended images that match the user's preferences based on the analysis results. This enables efficient image selection and adjustment that takes the user's emotions into consideration, as well as improved communication efficiency.

[0579] "Uploading method" refers to the method or device used by participants to send images and emotional data they have captured to a server.

[0580] "Analysis means" refers to the method or process by which a server uses artificial intelligence technology to analyze transmitted image and emotion data and select images.

[0581] "Means for generating recommended lists" refers to the process or method of creating a list of recommended images based on the user's selection of images that match their preferences.

[0582] "Adjustment means" refers to a device or method that automatically adjusts the image quality and composition of selected images and performs filtering based on emotional data as needed.

[0583] "Generation means" refers to a device or method that automatically creates an album by combining multiple images using images that richly express the user's emotions.

[0584] "Local processing methods" refer to processes and technologies for processing data within a region and improving communication efficiency.

[0585] "Emotion analysis means" refers to technologies and methods in which a server uses the facial expressions of subjects in an image and audio signals to evaluate the emotional state of a user.

[0586] As a form for carrying out the invention, this system consists of data communication and processing between a user, a terminal, and a server.

[0587] First, users take photos using their devices during the event. These devices have a dedicated application installed for sending the captured images to a server in real time. Using this application, users can also input their emotional data along with the images through reaction buttons and voice recordings.

[0588] Next, the device collects the captured images and emotion data, and uploads this data to a server via wireless communication. This communication uses a standard internet connection.

[0589] Next, the server analyzes the received images and emotion data using artificial intelligence technology. The server is equipped with an "emotion engine" that performs facial expression analysis and speech recognition in images, making it possible to evaluate the user's emotional state. Based on this evaluation, the server selects images and generates a recommendation list of images that match the user's preferences.

[0590] Furthermore, the server automatically adjusts the image quality of the selected images. Image processing software (e.g., a dedicated API) is used to optimize brightness, contrast, and composition. Appropriate filtering and color adjustments are also applied based on emotional data.

[0591] Users can view a list of recommendations from the server on their device, select their favorite images, and share them via social media or email. The server also automatically generates albums that take into account the overall atmosphere of the event and the user's emotional data. The generation AI model highlights emotionally rich moments, providing a customized visual experience.

[0592] As a concrete example, consider a family celebration. The user takes photos during the event and records the "best moments" with voiceovers. The server then automatically creates an album that emphasizes smiles, allowing the whole family to share the joy. A prompt such as, "I've taken photos of a family event, and I'd like to create an album that emphasizes the smiles of the participants. I've recorded voiceovers of the participants enjoying themselves," could be used.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] Users take photos using their devices during the event. As input, users input emotion data (reaction buttons and voice recordings) along with the photos. The device receives this input and sends the captured images and emotion data to the server. As output, the device generates a data package and uploads it to the server via wireless communication.

[0596] Step 2:

[0597] The server receives image data and emotion data transmitted from the terminal. This data is stored on the server as input. The server saves the stored data to a database and begins analyzing it using artificial intelligence technology. Specifically, it performs facial expression analysis of the subject in the image and speech recognition to understand the user's emotional state. The analyzed emotion evaluation data is generated as output.

[0598] Step 3:

[0599] The server selects images that match the user's preferences based on the analysis results. Sentiment evaluation data and image data are used as input. The server analyzes this data and generates a recommendation list based on specific criteria. Specifically, it selects images that emphasize desirable emotional expressions. The output is a list of images recommended to the user.

[0600] Step 4:

[0601] The server automatically adjusts the image quality of the selected images. Images from the recommendation list are retrieved by the server as input. The server uses image processing software to optimize the brightness, contrast, and composition of the images, and performs filtering and color adjustments based on sentiment data. The output is a high-quality, adjusted image.

[0602] Step 5:

[0603] The user checks a list of recommendations provided by the server on their device. As input, a list of adjusted images is delivered to the user's device. The user browses these images and selects their favorites. Specifically, the user shares the selected images via social media, email, etc. As output, the images chosen by the user are shared with others.

[0604] Step 6:

[0605] The server generates a customized album using multiple adjusted images. The input consists of images selected by the user. Utilizing a generative AI model, the server creates an album that highlights moments that richly express the user's emotions. Specifically, image rearrangement and theme-based layouts are applied. The output is a customized album provided to the user.

[0606] (Application Example 2)

[0607] Next, we will explain application example 2. In the following explanation, 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."

[0608] The problem that this invention aims to solve is to personalize the customer experience in physical stores and improve the product purchasing and evaluation experience within the store. In particular, there is a need to share images optimized based on emotions, derived from images taken by customers, in real time within the store to increase the purchasing intent of the customer themselves and other visitors. However, conventional store systems cannot provide real-time emotion-based feedback or displays, and challenges remain in improving communication efficiency and the burden of data processing.

[0609] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0610] In this invention, the server includes a regional computing means for performing data calculations within a region and improving communication efficiency, a filtering means for applying an optimized filter to an image based on emotion data, and a display means for enabling sharing on a display device. This makes it possible to provide a personalized purchasing experience based on customer emotions in physical stores.

[0611] A "participant" is a person who gathers at an event or activity for a specific purpose.

[0612] A "storage device" is a device that has the function of securely storing digital information and making it available for later retrieval.

[0613] "Uploading" refers to the technical process of transferring digital data from a user's device to a server.

[0614] "Artificial intelligence technology" refers to a group of programs that analyze images and data, enabling them to make decisions similar to those of a human.

[0615] "Analysis methods" refer to methods for analyzing given data in detail and extracting its characteristics and relationships.

[0616] An "optimization method" is a technique for adjusting data and information to a standardized, efficient, and effective form.

[0617] "User" refers to an individual or group that operates and uses a system or service.

[0618] A "display device" is an electronic device that can visually represent digital information.

[0619] "Visual data" refers to digital media files that are represented as information that can be perceived through sight.

[0620] A "regional computing means" is a device or technology that performs computational processing within a specific region to improve efficiency.

[0621] "Filter application means" refers to a technique or method for applying specific effects or adjustments to images or data.

[0622] A "list" is a data set in a format that systematically enumerates related information.

[0623] This invention is a system for providing personalized purchasing experiences based on customer emotions in physical stores. The system mainly consists of customer terminals (such as smartphones) and a server.

[0624] The server first uploads images transmitted from the customer's terminal in real time to a storage device. During this process, an upload mechanism is used to efficiently transfer the data. The data collected by the server includes not only images but also audio data for emotional analysis. Using artificial intelligence technology, the image and emotional data are analyzed to determine the user's emotional state.

[0625] Next, based on the analyzed sentiment data, the image quality and composition are adjusted using optimization means. This adjustment includes filter application means to apply specific visual effects to the image. The adjusted image is displayed in real time through in-store display devices to enhance the visual experience for other visitors and the customer themselves.

[0626] As a concrete example, imagine a customer visiting an accessory store with a friend. They use a device to take photos for in-store display and upload them along with audio recordings expressing their emotions, such as excitement or surprise. The server analyzes the emotional data and adjusts the image by applying a filter that emphasizes smiles. The adjusted image is then displayed in the store, further stimulating purchasing intent and providing a unique store experience.

[0627] Furthermore, as an example of a prompt, a specific question can be posed to the generating AI model: "What filters and adjustments would be suitable for optimizing a photo that reflects the emotions of a user visiting an accessory store with a friend, and displaying it in the store?" This enables more appropriate image adjustments, further enhancing the value of the on-site experience.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The user's device takes photos inside the physical store, acquiring image data and audio data expressing emotions. This data becomes the input for uploading. The device uses an upload mechanism to send the data to a storage device and transfers the data to the server.

[0631] Step 2:

[0632] The server receives image and audio data transmitted from the terminal and inputs them into the analysis system. Using artificial intelligence technology, it analyzes the visual information in the images and the emotional expressions in the audio data. Based on this analysis, the server generates emotional data output and evaluates the user's current emotional state.

[0633] Step 3:

[0634] Based on the analyzed emotion data, the server activates optimization mechanisms. It automatically adjusts the brightness and composition of the input image data and applies emotion-based filters. This results in the output of the adjusted image data.

[0635] Step 4:

[0636] The server sends the adjusted image data to the store's display device, allowing the image to be displayed in real time. The displayed image visually impacts the user and other customers in the store, improving the shopping experience.

[0637] Step 5:

[0638] The server uses a generated AI model to create prompt messages to identify the filters and adjustments that best match the user's emotions and the store's atmosphere. By providing these prompt messages to the AI, feedback is obtained to enable more effective image adjustments.

[0639] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0640] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0641] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0645] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0646] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0647] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0648] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0649] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0650] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0651] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0652] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0653] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0654] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0655] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] This invention is a system for participants to take photos of an event using their own devices, share them smoothly, and create high-quality records. The system includes image processing, AI analysis, content sharing, album generation, and local processing functions.

[0657] First, the "user" takes photos of the event using their own device. This device has a dedicated application installed and provides an interface for quickly processing the captured images. The "device" uploads the captured images to a central "server" using internet communication. The upload also includes metadata such as the time and location information of the photos taken.

[0658] Next, the "server" analyzes the received images. Using AI technology, it evaluates the image's composition, brightness, and the emotional expression of the subject, and selects high-value images based on this information. In this selection process, scoring is performed based on multiple evaluation metrics, and photos that are likely to please the user are proactively added to the recommendation list.

[0659] Subsequently, the selected images undergo automatic quality adjustments on the server. Specifically, brightness and contrast are optimized, and unnecessary elements are cropped, improving their appearance. These adjusted images are then presented to the user as a preview for confirmation.

[0660] Furthermore, images that a "user" has indicated their intention to share are made public to other users and online platforms via the "device." At this time, settings related to the protection of personal information and copyright are also applied to ensure that sharing is conducted safely.

[0661] In addition, the "server" automatically generates event-specific albums using a large number of shared images. These albums are structured to create a narrative, taking into account the time of shooting and the highlights of the event. The final albums are saved as digital books, allowing all participants to reminisce.

[0662] Furthermore, this invention utilizes a "local processing method," which optimizes communication efficiency and provides users with faster feedback by completing data processing within the local area. This reduces the network load at event venues and enables a smoother user experience.

[0663] As a concrete example, consider a school sports day. Parents, as participants, take photos of their children's events with their smartphones, and the images are immediately uploaded to a server. The server uses AI analysis to recommend photos of the most emotionally expressive moments, and the parents share the adjusted images with other family members and on social media. Finally, a dynamic album of the entire sports day is automatically generated and can be viewed online by all participants.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The user takes a photo with their device. After taking the photo, a dedicated app automatically starts preparing to upload the image to the server.

[0667] Step 2:

[0668] The device uploads the captured photos to the server along with metadata (e.g., time of capture, GPS information). This provides a centralized management system for image data.

[0669] Step 3:

[0670] The server supplies the received image data to the AI ​​analysis engine. Here, it analyzes characteristics such as image quality, composition, and the subject's facial expression.

[0671] Step 4:

[0672] The server applies a scoring algorithm based on the analysis results, ranking images in order of priority. It selects images that capture particularly valuable moments as recommended content.

[0673] Step 5:

[0674] The server initiates the image quality adjustment process for the selected images. This includes automatic optimization of brightness and contrast, and cropping of the composition.

[0675] Step 6:

[0676] The user visualizes image previews from the server on their device and selects which images to share from the recommended images. The next action is determined based on the user's selection.

[0677] Step 7:

[0678] The device shares images selected by the user to the communication platform or other participants. Necessary settings are applied for security and privacy.

[0679] Step 8:

[0680] The server uses an album generation module to assemble an album of the entire event based on the collected shared images. Considering the time of shooting and highlights of the content, a visually appealing album is created.

[0681] Step 9:

[0682] The local processing mechanism within the server optimizes the processing data locally. This improves the efficiency of the local network and speeds up overall response times.

[0683] (Example 1)

[0684] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Current technologies often require manual data selection and optimization for the rapid and efficient management and sharing of digital data, demanding significant time and effort from users. Furthermore, data sharing frequently raises security and privacy concerns. Additionally, increased network load can reduce communication efficiency, potentially impairing the user experience. Addressing these challenges is crucial.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0687] In this invention, the server includes a transmission means for transmitting digital data to an information processing device, an analysis means for analyzing the digital data using artificial intelligence technology and selecting the data, and an optimization means for automatically optimizing the quality and content of the digital data. This enables users to efficiently manage and securely share digital data.

[0688] A "participant" is an individual or group that uses the system to generate and process digital data.

[0689] "Digital data" refers to electronic information, including captured images and videos, and their associated metadata.

[0690] An "information processing device" is a device capable of transmitting, storing, and analyzing digital data, and generally refers to a server or computer.

[0691] A "transmission means" is a mechanism for appropriately transmitting digital data generated by participants to an information processing device.

[0692] "Analysis methods" refer to systems that use artificial intelligence technology to analyze digital data and evaluate the value and characteristics of the information.

[0693] "Optimization techniques" are processing technologies used to automatically improve the quality and composition of digital data.

[0694] A "user" is an individual or group that reviews and makes decisions regarding the sharing of optimized digital data.

[0695] "Distribution method" refers to a method for securely sharing digital data selected by a user with other users or platforms.

[0696] A "collection" is a digital album or library that brings together selected and shared digital data, built around a specific theme or purpose.

[0697] A "regional processing method" is a mechanism for distributing and processing digital data within a region to improve communication efficiency.

[0698] An embodiment of this system will be described.

[0699] This invention provides a system that allows participants to efficiently manage and share digital data at events and other occasions. Specifically, it involves collaboration between three entities—a server, a terminal, and a user—to generate, transmit, analyze, optimize, and share digital data.

[0700] First, users generate digital data, such as photos and videos, using devices like smartphones and tablets. These devices have dedicated applications installed, allowing users to manipulate the data through a user-friendly interface. The generated data, along with metadata, is then transmitted to a server via the internet using a transmission method.

[0701] Next, the server analyzes the received digital data using analytical tools. This involves the use of a generative AI model. The server utilizes this model to evaluate the data's quality and structure, and then performs scoring. This analysis process selects the data the user is looking for.

[0702] Subsequently, the server uses optimization techniques to improve the quality of the digital data. Specifically, it automatically adjusts the brightness and contrast of the data and trims off unnecessary parts to enhance the visual appeal of the data. Once the optimization is complete, the data is sent from the server to the terminal, and a preview is provided to the user.

[0703] Data that a user has confirmed and indicated their intention to share is shared from their device to other users or social media platforms via distribution methods. During this process, settings regarding personal information and copyright of the data are applied to ensure secure sharing.

[0704] Furthermore, by having servers process large amounts of digital data within a region using local processing methods, communication efficiency is improved and network load is reduced. This allows users to receive feedback more quickly.

[0705] As a concrete example, consider a photo taken by a user at a school sports day. Immediately after taking the photo, it is uploaded to a server, then undergoes AI analysis, and high-quality photos that capture emotionally rich moments are selected. After adjustment, it can be easily shared with other family members or on social media. An example of a prompt message to the generative AI model involved in this process would be the instruction, "Identify the emotionally rich moments of the event and save them as an album."

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] Users capture event footage using a device equipped with a dedicated application and generate digital data. The input is video data from the camera sensor, and the output is the generated digital image data. Specifically, the user operates the device's camera function to capture images, and the application saves that data to internal storage.

[0709] Step 2:

[0710] The device uploads stored digital data and its metadata (such as date and time of capture and location information) to a server via a network transmission method. The input is the generated digital data and metadata, and the output is the data stored in the server's storage device. Specifically, the data is compressed using Wi-Fi or mobile communication and sent to the server using a secure protocol.

[0711] Step 3:

[0712] The server analyzes the received digital data using artificial intelligence technology and analytical tools. The input is the uploaded data, and the output is selected important digital data. Specifically, the server activates an AI model, scans the data content to evaluate facial expressions and composition, and scores based on certain evaluation criteria. In this process, an emotion analysis algorithm is used to identify emotionally rich moments.

[0713] Step 4:

[0714] The server automatically improves the image quality of digital data using optimization techniques based on the analysis results. The input is selected digital data within the server, and the output is digital data with adjusted image quality. Specifically, it automatically adjusts the brightness and contrast of the data and crops the background to optimize its visual appeal.

[0715] Step 5:

[0716] The server returns optimized digital data to the terminal, which the user reviews through the terminal's interface. The input is the optimized digital data sent from the server, and the output is the digital data reviewed by the user. Specific operations include displaying the data on the device screen using preview software.

[0717] Step 6:

[0718] When a user indicates their intention to share selected data, the device distributes the selected digital data to other users or platforms via a distribution method. The input is digital data confirmed by the user, and the output is digital data shared with the specified recipient. Specifically, the data is encrypted at the endpoint and sent to the designated recipient using a secure protocol such as HTTPS.

[0719] Step 7:

[0720] The server processes a large amount of shared digital data using local processing methods. The input is digital data shared after distribution, and the output is a verified digital collection. Specifically, the data is analyzed in a distributed manner at local processing facilities, and caching technology is used to improve communication efficiency and reduce the load on the local network.

[0721] (Application Example 1)

[0722] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] In modern e-commerce, visually recording and efficiently sharing the purchasing experience is crucial. However, there is a lack of suitable means for users to properly photograph their purchased items and easily share them with friends and on social networks. As a result, reflection on and sharing of the purchasing experience with others is sluggish, leading to a decrease in satisfaction with the purchasing activity.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0725] In this invention, the server includes upload means for transmitting images taken by participants to a data storage device, analysis means for analyzing the transmitted images using intelligent technology and selecting images, and recording means for recording images of product use based on purchase history and facilitating posting by users on social platforms. This enables users to smoothly record their experiences with purchased products and share them in a visually rich way.

[0726] "Participants" are people who take part in events or activities and photograph them with their own devices.

[0727] A "data storage device" is a device that efficiently stores captured images and makes them accessible later.

[0728] "Uploading means" refers to a method or device for transmitting images taken by participants to a data storage device.

[0729] "Intelligent technology" refers to techniques that use artificial intelligence and machine learning to analyze the composition and content of images.

[0730] "Analysis means" refers to a method or apparatus for evaluating transmitted images using intelligent technology and selecting appropriate images.

[0731] "Generation means" refers to a method or apparatus for automatically constructing an attractive photo collection based on a set of images.

[0732] A "regional processing method" is a method or device for processing data within a specific region and maximizing communication efficiency.

[0733] "Recording means" refers to a method or device for managing images of product use based on purchase history and facilitating easy sharing.

[0734] The following describes a mode for carrying out the invention. The system that realizes this application aims to efficiently manage images taken by participants and share them in a visually appealing way.

[0735] First, the user's device provides an interface for taking images at events, shopping trips, and other locations. This device is often a smartphone or tablet, and it processes image data through a dedicated application installed on the device. The image data is uploaded to a server via the internet. At the same time, metadata such as the time of capture and location information is also transmitted.

[0736] The server uses intelligent technology to analyze the received images. Specifically, it evaluates images using machine learning and artificial intelligence (AI) technologies, such as software like TensorFlow and OpenCV. The server scores the composition, color information, and emotional expression of the subjects in the incoming images and selects the most valuable images. Furthermore, it applies adjustments to the selected images, such as brightness and contrast adjustments and cropping of unnecessary elements.

[0737] The adjusted images are provided to the user as a preview and can be shared on online social platforms and within communities at the user's discretion. The server also integrates with the user's purchase history information and includes recording mechanisms for documenting the use of purchased items and review images, thereby helping users share their experiences attractively on social media.

[0738] As a concrete example, when a customer purchases new shoes at a shopping mall, they take a photo of the product and upload it to a server. The server analyzes the photo and suggests the most suitable filters and layouts. By posting this to social media, the user can visually promote their new purchase to friends and followers, and also create an album to reflect on their shopping experience.

[0739] An example of a prompt used when giving instructions to a generative AI model is a customized input such as, "Please suggest the best filter and layout to share the new sneakers in this photo in the best possible way." This allows the AI ​​to receive specific instructions for automatically optimizing the photo according to the user's wishes.

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The user takes an image using their device. This image contains metadata such as the time and location information. The captured image becomes input that is sent to a data storage device via the application.

[0743] Step 2:

[0744] The device uploads the captured image, along with the time and location information, to a server via the internet. The server then receives the image data. The image metadata is also transmitted during this upload.

[0745] Step 3:

[0746] The server analyzes the received images. Using intelligent technologies such as TensorFlow, it scores the image's composition, color information, and the subject's emotion. The input is images uploaded by the user, and the output is a list of valuable images.

[0747] Step 4:

[0748] The server uses OpenCV to adjust the brightness and contrast of the selected images and crop out unnecessary elements. The input is a list of images selected in step 3, and the output is the adjusted image.

[0749] Step 5:

[0750] The server provides the user with a preview of the adjusted image. The user reviews the image and optionally shares it on social platforms. The input is the adjusted image, and the selected image is shared as the output.

[0751] Step 6:

[0752] The server uses purchase history information to record images of users using their purchased items. This makes it easy for users to visually record and share product reviews and usage scenarios.

[0753] Step 7:

[0754] The server uses a generative AI model to suggest optimal filters and layouts based on user input, optimizing photos for sharing on social media. Here, example prompts are crucial, instructing the generative AI on specific desired transformations.

[0755] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0756] This invention provides a system that streamlines the photography and sharing process at events, and further generates and adjusts images while taking user emotions into consideration. The system includes a series of processes for managing, selecting, and adjusting images taken by participants on a server.

[0757] The system begins with the "user" taking photos during the event using their own device. This device has an application installed to upload the captured images to the server in real time. Along with the captured images, the "device" can also send simple emotional data from the user (e.g., reaction buttons or voice recordings) to the server.

[0758] Next, the "server" performs an artificial intelligence analysis on the received images and emotion data. In this analysis, the "emotion engine" evaluates the facial expressions and audio signals of the subjects in the images to understand the user's emotional state. Based on the results, it applies this as feedback in image selection and adjustment, and adds images that particularly match the user's preferences to the recommendation list.

[0759] The selected images automatically enter an image quality adjustment process, where their brightness, contrast, and composition are optimized on the server. Image filtering and color adjustments may also be applied based on sentiment data. After adjustment, the user can review the images on their device and choose which ones to share.

[0760] Furthermore, the "server" generates customized albums that reflect emotional data to provide a new visual experience from a large number of images. This album generation incorporates not only event highlights but also moments that richly express the user's emotions.

[0761] As a concrete example, consider a family celebration event. After a family member takes a photo with their smartphone, they send the photo along with an audio recording of their emotional expression to the server. Using this information, the server analyzes the photo and automatically filters it to highlight the family's smiles. Finally, an album that captures the emotions of the event is generated and shared with everyone. In this way, the present invention optimizes event recording while taking the user's emotions into consideration.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] Users film the event with their own devices. The devices are equipped with an interface for recording the user's emotional responses along with the images.

[0765] Step 2:

[0766] The device uploads captured images and emotion data to a server. Emotion data is recorded as simple reaction buttons or voice input.

[0767] Step 3:

[0768] The server supplies data to the AI ​​analysis engine and the emotion engine to analyze the received image and emotion data. The AI ​​analysis engine evaluates the composition and quality of the images, while the emotion engine analyzes the user's emotional state.

[0769] Step 4:

[0770] The server scores images based on the analysis results, and in particular selects images with high recommendation scores based on sentiment data. This selection determines how well the images align with the user's preferences.

[0771] Step 5:

[0772] The server adjusts the image quality and composition of the selected images. This includes adjusting brightness, contrast, and cropping, as well as applying filters and adjusting color tones as needed.

[0773] Step 6:

[0774] The user reviews the adjusted image on their device. The user can then select the image to share and direct the next steps based on that selection.

[0775] Step 7:

[0776] The device shares the selected images with other participants and online platforms. Security settings and privacy policies are applied.

[0777] Step 8:

[0778] The server generates customized albums based on the user's emotions, using the shared image collection as a basis. The albums are structured to highlight moments in time when the user's emotions were expressed through the events.

[0779] Step 9:

[0780] The local processing mechanism within the server improves local communication efficiency by optimizing the processing data locally. This shortens the time to final output, enabling faster service to users.

[0781] (Example 2)

[0782] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] When streamlining the photo-taking and sharing process at events, and further generating and adjusting images that take user emotions into consideration, conventional systems have struggled to appropriately reflect user emotions in image selection and adjustment. Furthermore, there is a need to improve communication efficiency when using cloud servers. A new system is needed to solve these challenges.

[0784] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0785] In this invention, the server includes upload means for transmitting images and emotional data taken by participants to the server, analysis means for analyzing the transmitted images and emotional data using artificial intelligence technology and selecting images, and means for generating a list of recommended images that match the user's preferences based on the analysis results. This enables efficient image selection and adjustment that takes the user's emotions into consideration, as well as improved communication efficiency.

[0786] "Uploading method" refers to the method or device used by participants to send images and emotional data they have captured to a server.

[0787] "Analysis means" refers to the method or process by which a server uses artificial intelligence technology to analyze transmitted image and emotion data and select images.

[0788] "Means for generating recommended lists" refers to the process or method of creating a list of recommended images based on the user's selection of images that match their preferences.

[0789] "Adjustment means" refers to a device or method that automatically adjusts the image quality and composition of selected images and performs filtering based on emotional data as needed.

[0790] "Generation means" refers to a device or method that automatically creates an album by combining multiple images using images that richly express the user's emotions.

[0791] "Local processing methods" refer to processes and technologies for processing data within a region and improving communication efficiency.

[0792] "Emotion analysis means" refers to technologies and methods in which a server uses the facial expressions of subjects in an image and audio signals to evaluate the emotional state of a user.

[0793] As a form for carrying out the invention, this system consists of data communication and processing between a user, a terminal, and a server.

[0794] First, users take photos using their devices during the event. These devices have a dedicated application installed for sending the captured images to a server in real time. Using this application, users can also input their emotional data along with the images through reaction buttons and voice recordings.

[0795] Next, the device collects the captured images and emotion data, and uploads this data to a server via wireless communication. This communication uses a standard internet connection.

[0796] Next, the server analyzes the received images and emotion data using artificial intelligence technology. The server is equipped with an "emotion engine" that performs facial expression analysis and speech recognition in images, making it possible to evaluate the user's emotional state. Based on this evaluation, the server selects images and generates a recommendation list of images that match the user's preferences.

[0797] Furthermore, the server automatically adjusts the image quality of the selected images. Image processing software (e.g., a dedicated API) is used to optimize brightness, contrast, and composition. Appropriate filtering and color adjustments are also applied based on emotional data.

[0798] Users can view a list of recommendations from the server on their device, select their favorite images, and share them via social media or email. The server also automatically generates albums that take into account the overall atmosphere of the event and the user's emotional data. The generation AI model highlights emotionally rich moments, providing a customized visual experience.

[0799] As a concrete example, consider a family celebration. The user takes photos during the event and records the "best moments" with voiceovers. The server then automatically creates an album that emphasizes smiles, allowing the whole family to share the joy. A prompt such as, "I've taken photos of a family event, and I'd like to create an album that emphasizes the smiles of the participants. I've recorded voiceovers of the participants enjoying themselves," could be used.

[0800] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0801] Step 1:

[0802] Users take photos using their devices during the event. As input, users input emotion data (reaction buttons and voice recordings) along with the photos. The device receives this input and sends the captured images and emotion data to the server. As output, the device generates a data package and uploads it to the server via wireless communication.

[0803] Step 2:

[0804] The server receives image data and emotion data transmitted from the terminal. This data is stored on the server as input. The server saves the stored data to a database and begins analyzing it using artificial intelligence technology. Specifically, it performs facial expression analysis of the subject in the image and speech recognition to understand the user's emotional state. The analyzed emotion evaluation data is generated as output.

[0805] Step 3:

[0806] The server selects images that match the user's preferences based on the analysis results. Sentiment evaluation data and image data are used as input. The server analyzes this data and generates a recommendation list based on specific criteria. Specifically, it selects images that emphasize desirable emotional expressions. The output is a list of images recommended to the user.

[0807] Step 4:

[0808] The server automatically adjusts the image quality of the selected images. Images from the recommendation list are retrieved by the server as input. The server uses image processing software to optimize the brightness, contrast, and composition of the images, and performs filtering and color adjustments based on sentiment data. The output is a high-quality, adjusted image.

[0809] Step 5:

[0810] The user checks a list of recommendations provided by the server on their device. As input, a list of adjusted images is delivered to the user's device. The user browses these images and selects their favorites. Specifically, the user shares the selected images via social media, email, etc. As output, the images chosen by the user are shared with others.

[0811] Step 6:

[0812] The server generates a customized album using multiple adjusted images. The input consists of images selected by the user. Utilizing a generative AI model, the server creates an album that highlights moments that richly express the user's emotions. Specifically, image rearrangement and theme-based layouts are applied. The output is a customized album provided to the user.

[0813] (Application Example 2)

[0814] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0815] The problem that this invention aims to solve is to personalize the customer experience in physical stores and improve the product purchasing and evaluation experience within the store. In particular, there is a need to share images optimized based on emotions, derived from images taken by customers, in real time within the store to increase the purchasing intent of the customer themselves and other visitors. However, conventional store systems cannot provide real-time emotion-based feedback or displays, and challenges remain in improving communication efficiency and the burden of data processing.

[0816] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0817] In this invention, the server includes a regional computing means for performing data calculations within a region and improving communication efficiency, a filtering means for applying an optimized filter to an image based on emotion data, and a display means for enabling sharing on a display device. This makes it possible to provide a personalized purchasing experience based on customer emotions in physical stores.

[0818] A "participant" is a person who gathers at an event or activity for a specific purpose.

[0819] A "storage device" is a device that has the function of securely storing digital information and making it available for later retrieval.

[0820] "Uploading" refers to the technical process of transferring digital data from a user's device to a server.

[0821] "Artificial intelligence technology" refers to a group of programs that analyze images and data, enabling them to make decisions similar to those of a human.

[0822] "Analysis methods" refer to methods for analyzing given data in detail and extracting its characteristics and relationships.

[0823] An "optimization method" is a technique for adjusting data and information to a standardized, efficient, and effective form.

[0824] "User" refers to an individual or group that operates and uses a system or service.

[0825] A "display device" is an electronic device that can visually represent digital information.

[0826] "Visual data" refers to digital media files that are represented as information that can be perceived through sight.

[0827] A "regional computing means" is a device or technology that performs computational processing within a specific region to improve efficiency.

[0828] "Filter application means" refers to a technique or method for applying specific effects or adjustments to images or data.

[0829] A "list" is a data set in a format that systematically enumerates related information.

[0830] This invention is a system for providing personalized purchasing experiences based on customer emotions in physical stores. The system mainly consists of customer terminals (such as smartphones) and a server.

[0831] The server first uploads images transmitted from the customer's terminal in real time to a storage device. During this process, an upload mechanism is used to efficiently transfer the data. The data collected by the server includes not only images but also audio data for emotional analysis. Using artificial intelligence technology, the image and emotional data are analyzed to determine the user's emotional state.

[0832] Next, based on the analyzed sentiment data, the image quality and composition are adjusted using optimization means. This adjustment includes filter application means to apply specific visual effects to the image. The adjusted image is displayed in real time through in-store display devices to enhance the visual experience for other visitors and the customer themselves.

[0833] As a concrete example, imagine a customer visiting an accessory store with a friend. They use a device to take photos for in-store display and upload them along with audio recordings expressing their emotions, such as excitement or surprise. The server analyzes the emotional data and adjusts the image by applying a filter that emphasizes smiles. The adjusted image is then displayed in the store, further stimulating purchasing intent and providing a unique store experience.

[0834] Furthermore, as an example of a prompt, a specific question can be posed to the generating AI model: "What filters and adjustments would be suitable for optimizing a photo that reflects the emotions of a user visiting an accessory store with a friend, and displaying it in the store?" This enables more appropriate image adjustments, further enhancing the value of the on-site experience.

[0835] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0836] Step 1:

[0837] The user's device takes photos inside the physical store, acquiring image data and audio data expressing emotions. This data becomes the input for uploading. The device uses an upload mechanism to send the data to a storage device and transfers the data to the server.

[0838] Step 2:

[0839] The server receives image and audio data transmitted from the terminal and inputs them into the analysis system. Using artificial intelligence technology, it analyzes the visual information in the images and the emotional expressions in the audio data. Based on this analysis, the server generates emotional data output and evaluates the user's current emotional state.

[0840] Step 3:

[0841] Based on the analyzed emotion data, the server activates optimization mechanisms. It automatically adjusts the brightness and composition of the input image data and applies emotion-based filters. This results in the output of the adjusted image data.

[0842] Step 4:

[0843] The server sends the adjusted image data to the store's display device, allowing the image to be displayed in real time. The displayed image visually impacts the user and other customers in the store, improving the shopping experience.

[0844] Step 5:

[0845] The server uses a generated AI model to create prompt messages to identify the filters and adjustments that best match the user's emotions and the store's atmosphere. By providing these prompt messages to the AI, feedback is obtained to enable more effective image adjustments.

[0846] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0847] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0848] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0849] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0850] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0851] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0852] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0853] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0854] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0855] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0856] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0857] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0858] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0859] 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.

[0860] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0861] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0862] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0863] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0864] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0865] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0866] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0867] The following is further disclosed regarding the embodiments described above.

[0868] (Claim 1)

[0869] A means for uploading images taken by participants to send them to the server,

[0870] An analysis method for analyzing transmitted images using artificial intelligence technology and selecting images,

[0871] An adjustment mechanism for automatically adjusting the image quality and composition of selected images,

[0872] A means for users to review the adjusted images and share the selected images,

[0873] A means for automatically generating an album using multiple shared images,

[0874] A regional processing method for processing data within a region and improving communication efficiency,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, further comprising means for the server to create a list of images that the user may be interested in.

[0878] (Claim 3)

[0879] The system according to claim 1, wherein the adjustment means includes means for automatically adjusting the brightness, contrast, and cropping of an image.

[0880] "Example 1"

[0881] (Claim 1)

[0882] A means for transmitting digital data generated by participants to an information processing device,

[0883] An analysis means for analyzing transmitted digital data using artificial intelligence technology and selecting data,

[0884] An optimization means for automatically optimizing the quality and content of selected digital data,

[0885] A distribution method for users to review optimized digital data and share selected digital data,

[0886] A means for automatically building a collection using multiple distributed digital data,

[0887] A regional processing means for processing information within a region and optimizing communication efficiency,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for creating a list of digital data that may be of interest to a user.

[0891] (Claim 3)

[0892] The system according to claim 1, wherein the optimization means includes means for automatically performing brightness, contrast, and removal of unwanted parts of digital data.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] An upload method for sending images taken by participants to a data storage device,

[0896] An analysis means for analyzing transmitted images using intelligent technology and selecting images,

[0897] An adjustment mechanism for automatically adjusting the image quality and composition of selected images,

[0898] A means for users to review the adjusted images and share the selected images,

[0899] A means for automatically generating a photo album using multiple shared images,

[0900] A local processing method for processing information within a region and improving communication efficiency,

[0901] A recording means that records images of product use based on purchase history, and facilitates users from posting on social platforms.

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising means for the server to create a list of images that may be of interest to the user.

[0905] (Claim 3)

[0906] The system according to claim 1, wherein the adjustment means includes means for automatically adjusting the brightness, contrast, and cropping of an image.

[0907] "Example 2 of combining an emotion engine"

[0908] (Claim 1)

[0909] A means for uploading images and emotional data taken by participants to a server,

[0910] An analysis method for selecting images by analyzing transmitted images and emotional data using artificial intelligence technology,

[0911] A means for generating a list of images that recommend images that match the user's preferences based on the analysis results,

[0912] An adjustment mechanism for automatically adjusting the image quality and composition of selected images and applying filtering based on emotional data,

[0913] A means for users to review the adjusted images and share the selected images,

[0914] A generation method for automatically generating a customized album in which the user's emotions are expressed using multiple adjusted images,

[0915] A regional processing method for processing data within a region and improving communication efficiency,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, further comprising emotion analysis means for evaluating the facial expressions and audio signals of subjects in an image and understanding the user's emotional state.

[0919] (Claim 3)

[0920] The system according to claim 1, wherein the adjustment means includes means for automatically optimizing the brightness, contrast, and composition of an image and performing color adjustments based on sentiment data.

[0921] "Application example 2 when combining with an emotional engine"

[0922] (Claim 1)

[0923] An upload method for sending images taken by participants to a storage device,

[0924] An analysis means for analyzing transmitted images using artificial intelligence technology and selecting images,

[0925] An optimization means for automatically adjusting the image quality, contrast, and composition of selected images,

[0926] A display means for users to review the adjusted image and share the selected image on a display device,

[0927] A generation means for automatically generating visual data using multiple shared images,

[0928] A regional computing means for performing data calculations within a region and improving communication efficiency,

[0929] A filter application means for applying an optimized filter to an image based on emotion data,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, further comprising means for the server to create a list of images that the user might be interested in.

[0933] (Claim 3)

[0934] The system according to claim 1, wherein the adjustment means includes means for automatically adjusting the brightness, contrast, and cropping of an image. [Explanation of Symbols]

[0935] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for uploading images taken by participants to send them to the server, An analysis method for analyzing transmitted images using artificial intelligence technology and selecting images, An adjustment mechanism for automatically adjusting the image quality and composition of selected images, A means for users to review the adjusted images and share the selected images, A means for automatically generating an album using multiple shared images, A regional processing method for processing data within a region and improving communication efficiency, A system that includes this.

2. The system according to claim 1, further comprising means for the server to create a list of images that the user may be interested in.

3. The system according to claim 1, wherein the adjustment means includes means for automatically adjusting the brightness, contrast, and cropping of an image.

Images