System

Abstract

A system is provided.SOLUTION: A system comprising means for uploading data to a cloud storage, means for analyzing metadata of the uploaded data and storing the metadata in a database, means for classifying data based on a time series or a theme, means for extracting data related to a specific person using a face recognition technology, means for automatically generating a content for remembrance from the organized data, means for generating a link for sharing the generated content online, and means for transmitting the link to a designated recipient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, there are many situations where it is necessary to organize content (photos and videos) to commemorate the deceased. However, when a sudden tragedy occurs, organizing a huge number of albums and videos takes a lot of time and effort. Furthermore, in order to share memories of the deceased with many people, they currently have to be organized and distributed manually, which is difficult to do efficiently. To solve this problem, there is a need for a system that can automatically organize the photos and videos of the deceased and generate and distribute memorial content. [Means for solving the problem]

[0005] The present invention is a system that includes a means for uploading data to cloud storage, a means for analyzing the metadata of the uploaded data and storing it in a database, a means for classifying the data based on timeline or theme, a means for extracting data related to a specific person using facial recognition technology, a means for automatically generating memorial content from the organized data, a means for generating a link for sharing the generated content online, and a means for sending the link to a designated recipient.

[0006] This invention allows users to easily upload photos and videos of the deceased, and the system automatically organizes and classifies the data to generate memorial content, enabling people to efficiently share memories with others. This allows for smooth preparation of memorial content even in the event of a sudden tragedy.

[0007] "Cloud storage" refers to remote servers that allow data to be stored and accessed over the internet.

[0008] A "means for uploading data" is a device or method for sending data such as photos and videos from a user's device to cloud storage.

[0009] "Metadata" refers to information about photos and videos, including the date of shooting, location information, person tags, and other additional information.

[0010] A "database" is a system for systematically collecting, storing, managing, and retrieving metadata and other information.

[0011] "Chronological" refers to the order in which data is organized and arranged based on the date of capture or timestamp.

[0012] A "thematic classification tool" is a device or method for grouping data based on a particular topic or common characteristic.

[0013] "Facial recognition technology" is a technology that identifies the faces of people in photos and videos and analyzes and identifies their facial features.

[0014] "Memorial content" refers to digital content such as slideshows, videos, and albums created to commemorate the memory of a deceased person.

[0015] A "link to share online" is a URL or web address that can be used to access the generated content.

[0016] "Means for sending to a designated recipient" refers to a method or device for sending the generated link or content to an email address or social media account designated by the user. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0039] Data Collection Phase

[0040] User upload of data

[0041] Users use a dedicated web portal or mobile app to select and upload photos and videos of the deceased, with the option to add comments and tags.

[0042] Receiving and storing data by the server

[0043] The server receives data uploaded by users and stores it in cloud storage. At this time, it analyzes the metadata of each file (e.g., shooting date, location information, person tags) and stores this information in a database. This enables efficient classification in the subsequent data organization phase.

[0044] Data reduction phase

[0045] Classification by Server

[0046] The server retrieves data from cloud storage and organizes it chronologically or by theme based on metadata, for example, sorting photos by date taken or event, or grouping photos and videos that feature a particular person.

[0047] Application of facial recognition technology

[0048] The server uses facial recognition technology to analyze and identify the faces of people in each image or video, extracting data related to that specific person and grouping them separately. This process facilitates the creation of memorial content centered around the deceased.

[0049] Content Generation Phase

[0050] Automatic generation of memorial content

[0051] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0052] Delivery Phase

[0053] Preparing content for distribution

[0054] The server will generate a link to share the generated memorial content online, which will be set up for easy access by users.

[0055] Send to recipient

[0056] The server then sends the generated link to the recipients specified by the user via email or social media, allowing many people to share memories of the deceased.

[0057] Consider the following scenario: When a user uploads photos and videos of a deceased person, the server stores them in cloud storage, analyzes the metadata, and saves them in a database. The server then organizes the data based on the date it was taken or a specific theme (e.g., "family vacation" or "birthday party"). Using facial recognition technology, it extracts images and videos related to a specific person (the deceased), and automatically generates a memorial slideshow or video from these. This content can then be shared with attendees, relatives, and friends via an online link.

[0058] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and are then shown a screen for uploading this data. Users select the files and click the upload button.

[0062] Step 2:

[0063] The device sends the photos and videos selected by the user to the specified cloud storage. Once the upload is complete, it notifies the server of the relevant information (e.g., file name and file size).

[0064] Step 3:

[0065] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is stored in a database.

[0066] Step 4:

[0067] The server uses the metadata stored in the database to categorize uploaded photos and videos by timeline or theme, for example, sorting them by the date they were taken, or grouping them by theme, such as "family vacation" or "birthday party."

[0068] Step 5:

[0069] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts images and videos related to a specific person (the deceased). This allows for organization of the images and videos with a focus on the deceased.

[0070] Step 6:

[0071] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0072] Step 7:

[0073] The server generates a link for sharing the generated memorial content online, which can have specific access permissions and can be configured to make it easy for users to share.

[0074] Step 8:

[0075] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0076] This process allows for quick and efficient organization and sharing of photos and videos of the deceased.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] In recent years, commemorating the deceased and sharing memories through digital media has become widespread, but many users spend a great deal of time and effort organizing vast amounts of data and creating content. Furthermore, manual classification, facial recognition, and other processes require specialized knowledge, making them a time-consuming process for many. Furthermore, the lack of a consistent system for efficiently performing these tasks increases the burden on users. The purpose of this invention is to solve these problems and provide a system that allows users to easily create and share content for commemorating the deceased.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for users to upload data, means for saving the data in cloud storage, means for analyzing the metadata of the uploaded data and saving it in a database, means for classifying the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient. This allows users to easily upload data, and the server automatically organizes, classifies, creates, and shares the data, making it possible to provide efficient and moving memorial content.

[0082] "User data uploading means" refers to a means by which a user can use a dedicated web portal or mobile app to select photos and videos of the deceased and send that data to the server.

[0083] "Means for storing data in cloud storage" refers to a means by which a server receives data uploaded by a user and stores it in a distributed data storage system.

[0084] "Means for analyzing the metadata of uploaded data and storing it in a database" refers to a means for analyzing the metadata (e.g., shooting date, location information, person tag) of the data received by the server and storing this information in a relational database.

[0085] "Means for classifying data based on timeline or theme" refers to a means by which the server acquires data based on metadata and organizes the data according to a specific time or specific theme (e.g., photo date, event name).

[0086] "Means for extracting data related to a specific person using facial recognition technology" refers to a means in which a server uses facial recognition technology to analyze faces contained in images or videos, and automatically identify and extract data related to a specific person.

[0087] "Means for automatically generating memorial content from organized data" refers to a means by which a server automatically creates memorial content such as slideshows, videos, and albums based on organized data.

[0088] The "means for generating a link to share the generated content online" is a means for a server to host the generated memorial content and create a URL link to access the content.

[0089] The "means for sending the link to the designated recipient" refers to a means for sending the link generated by the server to the recipient's email address or SNS account designated by the user.

[0090] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0091] First, users use a dedicated web portal or mobile app to select photos and videos of the deceased and upload them to the server. This dedicated web portal is built using front-end frameworks such as ReactJS and Angular, while the mobile app uses Flutter (registered trademark) and React Native. Users can add comments and tags as needed. For example, they can add event tags such as "family trip" or "birthday party."

[0092] The server receives the uploaded data and stores it in cloud storage. Examples of cloud storage used for this include Google (registered trademark) Cloud Storage and Amazon S3. The server analyzes the stored data and extracts metadata for each file (e.g., photo date, location information, person tags). To extract the metadata, Python scripts and libraries such as ExifTool are used. The extracted metadata is stored in a relational database such as MySQL (registered trademark) or PostgreSQL.

[0093] The server then retrieves the stored data from the cloud storage and categorizes it based on time series or themes. This categorization can be done using the Python Pandas library. For example, photos and videos can be organized based on the date they were taken or the name of an event. In particular, categorization based on themes such as "family vacation" or "birthday party" is performed.

[0094] The server then uses facial recognition technology to extract data related to specific individuals. This process utilizes Google Vision API and Amazon Rekognition. Facial recognition technology identifies specific individuals in images and videos and extracts related data. For example, it extracts all images and videos containing the face of a deceased person and groups them separately. This process is important for easily generating memorial content centered around the deceased.

[0095] The server then automatically generates memorial slideshows, videos, and albums based on the organized data, editing the content using image processing software (e.g., FFmpeg or OpenCV), adding background music and text comments, and rendering the automatically generated content into its final format and saving it to cloud storage.

[0096] The server then generates a link for sharing the memorial online. This link can be provided as a URL shortener or a dedicated portal page. The prepared link is then sent to the recipient specified by the user via email or social media. This is done using the mail server SendGrid or Amazon SES, and also using the APIs of social media.

[0097] For example, when a user uploads a photo or video of a deceased person, the following prompt text could be used:

[0098] "Upload photos and videos from the deceased's family vacation and create a touching slideshow."

[0099] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Program processing steps

[0102] Step 1:

[0103] User upload of data

[0104] Specific behavior:

[0105] Users log in to a dedicated web portal or mobile app.

[0106] Users click the "Select Photo" button and select photos or videos of the deceased from their device.

[0107] After making your selection, press the "Upload" button to send the data to the server.

[0108] Input: User-selected photo and video files, comments and tags.

[0109] Output: The uploaded data sent to the server.

[0110] Step 2:

[0111] Receiving and storing data by the server

[0112] Specific behavior:

[0113] The server receives data uploaded by users.

[0114] Save the received files to cloud storage (such as Google Cloud Storage or Amazon S3).

[0115] At the same time, a script is run to analyze the metadata of the received file.

[0116] Input: User-submitted photos and videos, along with accompanying metadata.

[0117] Output: Files stored in cloud storage and parsed metadata.

[0118] Step 3:

[0119] Server parsing and storing metadata

[0120] Specific behavior:

[0121] The analysis script extracts metadata (Exif information, user-added tags) for each file.

[0122] Store the extracted metadata in a relational database (e.g., MySQL or PostgreSQL).

[0123] Input: A file stored in cloud storage.

[0124] Output: Metadata stored in a database.

[0125] Step 4:

[0126] Data classification by server

[0127] Specific behavior:

[0128] The server retrieves the data from the cloud storage.

[0129] Based on the data obtained, we use Python's Pandas library to classify the data based on time series and themes.

[0130] The classification results are stored in a database.

[0131] Input: Metadata stored in a database.

[0132] Output: Data categorized by theme.

[0133] Step 5:

[0134] Extracting data of specific individuals using facial recognition technology

[0135] Specific behavior:

[0136] The server calls the Google Vision API or Amazon Rekognition.

[0137] Use the API to detect faces in images and videos.

[0138] Identify specific people (deceased) and extract and group relevant data into a new dataset.

[0139] Input: Data retrieved from cloud storage.

[0140] Output: A group of data related to a particular person.

[0141] Step 6:

[0142] Automatic generation of memorial content

[0143] Specific behavior:

[0144] Based on the organized data, the server uses image processing software (FFmpeg or OpenCV) to create slideshows and video templates.

[0145] Automatically add background music and text comments and render to final format.

[0146] Input: Classified data and data relating to specific people.

[0147] Output: Auto-generated slideshows and videos.

[0148] Step 7:

[0149] Generated content is linked and ready to share

[0150] Specific behavior:

[0151] The server stores the generated content in cloud storage.

[0152] Generate links to share online, create URL shorteners and dedicated portal pages.

[0153] Input: Auto-generated memorial content.

[0154] Output: A shareable link.

[0155] Step 8:

[0156] Send link to designated recipient

[0157] Specific behavior:

[0158] The server retrieves contact information for the recipient specified by the user.

[0159] Share the link using a mail server (SendGrid or Amazon SES) or SNS API.

[0160] Log the sending status and send a confirmation email to the user.

[0161] Input: Share link and recipient's contact information.

[0162] Output: Link sent to recipient and confirmation email to user.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] In today's world, delivery services play an important role in the logistics industry, but there is a growing demand for improved delivery efficiency and customer satisfaction. In particular, there are many challenges in efficiently reaching multiple delivery destinations for delivery personnel and providing services tailored to the characteristics of each customer. Furthermore, there is a lack of effective methods for utilizing data such as accurate location information of delivery destinations and facial recognition of customers. To address these challenges, efficient delivery support through delivery route optimization and data analysis is required.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes a means for uploading data to cloud storage, a means for analyzing metadata of the uploaded data and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes, which enables delivery personnel to reach multiple delivery destinations quickly and efficiently, improving customer satisfaction.

[0168] "Cloud storage" is an online storage system for storing and managing data via the Internet.

[0169] "Data upload" is the process of sending and storing data from a local device to cloud storage.

[0170] "Metadata" is attribute information of the data itself, and includes, for example, the date of shooting, location information, person tags, and the like.

[0171] A "database" is a structured collection of data that allows for efficient management and manipulation of data.

[0172] "Time series" is a method of organizing and classifying data along the flow of time.

[0173] A "theme" is a concept that organizes and classifies data based on a specific content or topic.

[0174] "Facial recognition technology" is a technology that detects and identifies the faces of people contained in images and videos.

[0175] "Data extraction" is the process of extracting the necessary information from data.

[0176] "Automatic content generation" means mechanically creating content based on input data.

[0177] An "online sharing link" is a URL link that allows access to content over the Internet.

[0178] "Location information of delivery destination" is data indicating the geographical location of the delivery destination.

[0179] "Delivery route calculation" is the process of calculating the optimal route to efficiently reach multiple delivery destinations.

[0180] To implement this invention, a server, a terminal, and a user mainly work together. The server includes a means for uploading data to cloud storage, a means for analyzing metadata and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes. The terminal includes a smartphone or other mobile device, which a user uses to upload photos and notes of delivery destinations.

[0181] Data Collection Phase

[0182] Users take photos of delivery locations using their smartphones and upload them through a dedicated mobile app. Users can add comments and tags (for example, specific customer needs or points of caution) as needed. The server receives the uploaded photos and stores them in cloud storage.

[0183] Metadata Analysis Phase

[0184] The server analyzes the uploaded photo's metadata, which includes the date it was taken, location information, person tags, etc. This information is stored in a database and used later in the data organization and delivery route calculation phases.

[0185] Data reduction phase

[0186] The server retrieves data from the cloud storage and categorizes the photos based on the collected metadata, for example, by timeline or theme (e.g., "office area," "residential area"), and can also use facial recognition technology to extract and categorize photos related to specific customers.

[0187] Delivery route calculation phase

[0188] The server then uses the organized data and location information to efficiently calculate delivery routes, taking into account distances between specific destinations and traffic conditions. The results are then sent back to the driver's device and displayed.

[0189] Content Generation Phase

[0190] The server uses this organized data to automatically generate delivery schedules and service proposals tailored to each customer's characteristics, using AI technology to take into account the customer's past purchase history and feedback.

[0191] Example

[0192] For example, when a user uploads a "photo of a delivery destination," the server stores it in cloud storage, analyzes the metadata, and saves it in a database. The server then organizes the photos based on the date and location of the photo, and uses facial recognition technology to extract data related to specific customers. Finally, the server calculates an efficient delivery route and notifies the delivery person. This process improves delivery efficiency and customer satisfaction.

[0193] Prompt Sentence Examples

[0194] Prompt: Calculate delivery route by facial recognition and geolocation analysis of delivery destination photos

[0195] Data upload: ['photo1.jpg', 'photo2.jpg']

[0196] User ID: '12345'

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Users take photos of delivery destinations using their smartphones and upload these photos to the server through a dedicated mobile app. The photo data of the delivery destination is provided as input, and is saved on the server as output. Specifically, when the user presses the "upload" button on the app, the photos and related notes (comments and tags) are attached.

[0200] Step 2:

[0201] The server receives uploaded photos and metadata (photo date, location information, person tags) and stores them in cloud storage. The input is the photo data and metadata uploaded by the user, and the output is the data stored in cloud storage. The server verifies the received data, converts it into the required format, and stores it.

[0202] Step 3:

[0203] The server analyzes the metadata from the data on the cloud storage and stores it in a database. In this process, it uses facial recognition technology to analyze the faces of people in the photos. The photo data and metadata from the cloud storage are used as input, and the analyzed metadata is stored in the database as output. Specifically, the server runs a facial recognition algorithm to identify the faces of people in the photos.

[0204] Step 4:

[0205] The server uses the analyzed metadata to classify photos based on time sequence and theme (e.g., "office area," "residential area"). Using the analyzed metadata as input, the server obtains organized photo data as output. Specifically, the server groups the data based on the time of shooting and location information, and then categorizes them into appropriate categories.

[0206] Step 5:

[0207] The server uses the organized data to analyze the location information of the delivery destinations and calculates an efficient delivery route. The organized data including location information is used as input, and the delivery route is obtained as output. The server uses a specific algorithm (e.g., Dijkstra's algorithm) to calculate the optimal order of delivery destinations.

[0208] Step 6:

[0209] The server sends the calculated delivery route information to the device (smartphone) and displays delivery instructions to the user. The calculated delivery route information is used as input, and delivery instructions are obtained as output and displayed on the device. Specifically, the server converts the delivery route information into JSON format and sends it to the device app.

[0210] Step 7:

[0211] Once the delivery is complete, the user uses the device to confirm the delivery completion and, if necessary, send feedback to the server. The completion report and feedback data are provided as input, and the output is saved in cloud storage. Specifically, the user presses the "Delivery Complete" button to send the completion report to the server.

[0212] This process creates a system that provides efficient delivery routes and supports the work of delivery personnel.

[0213] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0214] To implement this invention, the server, the terminal, and the user work together. One of the features of this invention is that it incorporates an emotion engine that recognizes the user's emotions and reflects them in the generation of memorial content. The specific operation of this engine is described below.

[0215] Data Collection Phase

[0216] User upload of data

[0217] Users log in through a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system, adding comments and person tags as needed.

[0218] Receiving and storing data by the server

[0219] The server stores the data uploaded by users in cloud storage. It also analyzes the metadata of each file (such as the date of shooting, location information, and person tags) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0220] Data reduction phase

[0221] Classification by Server

[0222] The server retrieves the data from the cloud storage and categorizes it based on the metadata, such as by the date the photos were taken, or by themes like "family vacation" or "birthday party."

[0223] Application of facial recognition technology

[0224] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts data related to the specific person (deceased person). This allows data organization centered on the deceased person.

[0225] Applying the Emotion Engine

[0226] Recognizing user emotions

[0227] The device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input, and is used to capture the user's feelings when creating memorial content.

[0228] Content Generation Phase

[0229] Automatic generation of memorial content

[0230] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information obtained from the emotion engine. By adding appropriate background music and text comments according to the emotional information, more moving content can be created.

[0231] For example, when a user creates memorial content, the server analyzes the user's facial expressions and voice in real time to recognize feelings of sadness or gratitude. If the user feels sad, the server adds quiet, relaxing music, and if the user expresses gratitude, it inserts a warm comment.

[0232] Delivery Phase

[0233] Preparing content for distribution

[0234] The server generates a link for sharing the generated memorial content online, with specific access permissions configured to make it easy for users to share.

[0235] Send to recipient

[0236] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0237] This process allows for fast and efficient organization and sharing of photos and videos of the deceased, while providing personalized memorial content tailored to the user's emotions.

[0238] The processing flow will be explained below.

[0239] Step 1:

[0240] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and begin uploading. On the upload screen, select the files and add comments and tags as needed.

[0241] Step 2:

[0242] The device sends the photos and videos specified by the user to cloud storage, and once the transfer is complete, it notifies the server of the relevant metadata (file name, file size, etc.).

[0243] Step 3:

[0244] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is then stored in a database.

[0245] Step 4:

[0246] The server uses the metadata in the database to categorize the uploaded photos and videos by timeline or theme, for example, by a specific theme such as "family vacation" or "birthday party."

[0247] Step 5:

[0248] The server uses facial recognition technology to detect human faces in each image or video, analyzes the detected facial features, extracts data related to a specific person (deceased person), and groups them separately.

[0249] Step 6:

[0250] The device uses an emotion engine to recognize the user's emotions in real time, analyzing changes in facial expressions, tone of voice, and text input to determine the user's emotional state (e.g., sadness, gratitude, etc.).

[0251] Step 7:

[0252] The server automatically generates memorial slideshows, videos, and albums based on the collected data and the emotional information obtained using an emotion engine. For example, if the user is feeling sad, it will add calm and quiet background music, and if the user is expressing gratitude, it will insert warm comments.

[0253] Step 8:

[0254] The server generates a link to share the generated memorial content online, which can have specific access permissions and can be easily accessed by those with whom the link is shared.

[0255] Step 9:

[0256] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0257] The system efficiently organizes photos and videos of the deceased, automatically generates emotionally sensitive memorial content, and allows people to share their memories in a moving way.

[0258] Example 2

[0259] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0260] Currently, creating memorial content requires users to manually select and edit photos and videos, which requires a great deal of time and effort. Furthermore, existing systems do not personalize content based on the user's emotions, making it difficult to create moving memorial content. Furthermore, there is a lack of efficient ways to share the generated content. A system that can solve these issues is needed.

[0261] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0262] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for recognizing a user's emotions, means for automatically generating memorial content based on the organized data and emotional information, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient, thereby reducing the burden on the user and enabling the generation and sharing of moving and personalized memorial content.

[0263] "Cloud storage" is a storage system built on remote servers where data can be stored and accessed over the Internet.

[0264] "Data upload" is the operation of sending files such as photos and videos from a user's device to a remote server.

[0265] "Metadata" refers to file attribute information, and includes, for example, the date of shooting, location information, and person tags.

[0266] A "database" is a system that stores data systematically and enables fast and efficient retrieval, storage, and updating.

[0267] "Chronological" refers to a method of arranging data in chronological order, either from most recent to oldest.

[0268] A "theme" is a criterion for classifying data into a specific subject or category, examples of which include "family vacation" or "birthday party."

[0269] "Facial recognition technology" is a technology that detects and individually identifies human faces in images and videos.

[0270] "Emotion recognition" is the process of analyzing and identifying a user's emotional state from facial expressions, tone of voice, and text input.

[0271] "Memorial content" refers to digital media such as slideshows, videos, and albums created to commemorate and share memories of the deceased.

[0272] "Link" refers to a hypertext link or URL that allows access to specific data or content on the Internet.

[0273] "Recipient" refers to the person or group that receives the link to the content that a User wishes to share.

[0274] To implement the present invention, the server, the terminal, and the user work together. The specific operation will be described below.

[0275] First, users register an account using a dedicated web portal or mobile app and log in. At this time, the system authenticates the user through an authentication server. After logging in, users select photos and videos of the deceased from their device and upload them to the system. They can also add comments and person tags when uploading.

[0276] The server receives the uploaded data via HTTP requests and stores it in cloud storage (e.g., Amazon S3). It also uses a metadata analysis library such as ExifTool to analyze the metadata of the received data (e.g., shooting date, location information, person tags) and stores it in a database.

[0277] The server then categorizes the data based on timeline and themes. Specifically, it retrieves metadata from the database using SQL queries and applies classification algorithms. It then uses facial recognition technology (e.g., OpenCV or AWS® Rekognition) to detect faces in each image or video and extract data related to specific people. This information is then stored in a database.

[0278] The device uses an emotion engine, such as the Microsoft® Azure® Emotion API, to recognize the user's emotions. While the user is creating memorial content, the device analyzes video camera and microphone data in real time. This emotional information is then sent to the server.

[0279] Based on the organized data and emotional information, the server automatically generates a memorial slideshow, video, and album. Specifically, it uses personalized audio generation software (e.g., AIVA) and text generation models (e.g., GPT-3 (registered trademark)) to generate background music and comments. For example, if the user's emotion is sadness, quiet, relaxing music is added, and if the user expresses gratitude, warm comments are inserted.

[0280] Here are some examples of prompts:

[0281] "Select appropriate music based on the user's facial expressions and voice analysis."

[0282] "Generate text comments according to emotional information."

[0283] Finally, the server generates a link for sharing the memorial content and sets specific access permissions. This link is sent to the recipients specified by the user using an email sending API (e.g., SendGrid) or a social networking API, allowing users to easily share memories of the deceased with attendees, relatives, and friends.

[0284] This system reduces the burden on users and enables the creation and efficient sharing of moving and personalized memorial content.

[0285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0286] Step 1:

[0287] A user opens a dedicated web portal or mobile app, registers an account, and logs in. As input, the user enters their email address and password, and as output, the authentication server returns the authentication result. Specifically, the user is authenticated via the authentication API.

[0288] Step 2:

[0289] After logging in, the user selects photos and videos of the deceased from their device and uploads them to the system. The input is the photo or video file selected by the user, and the output is the data sent to the server. Specifically, the web portal or mobile app uses a file selection button, and the data is uploaded to the server via an HTTP request.

[0290] Step 3:

[0291] The server receives the uploaded data and stores it in cloud storage. The input is the photo and video data received by the server, and the output is the file stored in cloud storage. The specific operation is to store the data in a cloud storage service such as Amazon S3.

[0292] Step 4:

[0293] The server uses a metadata analysis library such as ExifTool to analyze the metadata of the uploaded data and store it in a database. The uploaded photo or video file is used as input, and the analyzed metadata is used as output and stored in the database. Specifically, the metadata analysis library is called to extract information and then stored in the database using SQL queries.

[0294] Step 5:

[0295] The server retrieves metadata from the database and categorizes the data based on timeline and themes. The input is the retrieved metadata, and the output is the categorized data. Specifically, it retrieves the metadata using SQL queries and applies classification algorithms to organize the data.

[0296] Step 6:

[0297] The server uses facial recognition technology (such as OpenCV or AWS Rekognition) to detect faces in images and videos and extract data related to specific people. Uploaded images and video files are used as input, and recognized face data is used as output, which is then stored in a database. Specifically, the server calls a facial recognition API to perform face detection and stores the results in a database.

[0298] Step 7:

[0299] The device recognizes the user's emotions using an emotion engine (such as the Microsoft Azure Emotion API). It receives real-time data from a video camera and microphone as input, and analyzes emotional data as output. Specifically, it analyzes the user's facial expressions and tone of voice, and sends the information to a server.

[0300] Step 8:

[0301] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information. The input is the organized data and emotional information, and the generated memorial content is obtained as the output. Specifically, the system uses personalized audio generation software (such as AIVA) and text generation models (such as GPT-3).

[0302] Step 9:

[0303] The server generates a link for sharing the generated memorial content and sets specific access permissions. The input is the generated memorial content, and the output is the generated link. Specifically, the server uses the cloud storage's shared link function or API to generate the link and set access permissions.

[0304] Step 10:

[0305] The server sends the generated link to the specified recipient via email or social media. The input is the recipient information specified by the user and the generated link, and the output is the link sent to the recipient. Specific operations use an email sending API (such as SendGrid) or a social media API.

[0306] (Application example 2)

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

[0308] Conventional memorial content creation systems generate content without considering the user's emotional state, making it difficult to provide a personalized experience that reflects individual emotions. Furthermore, they are unable to respond to the emotions of event participants in real time, making it difficult to provide appropriate content that reflects their emotions at memorial events.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, means for sending the link to a specified recipient, means for analyzing the user's emotional state, and means for displaying memorial content according to the analyzed emotional information, thereby enabling the generation and display of personalized memorial content in real time in accordance with the emotions of participants.

[0311] "Cloud storage" is a system for storing and managing data on remote servers via the Internet.

[0312] "Metadata" refers to information about the data itself, and examples include the date it was taken, location information, and person tags.

[0313] "Facial recognition technology" is a technology for detecting the faces of people in images or videos and identifying specific people.

[0314] "Memorial content" is a moving combination of photos, video, music, and text used to remember a deceased person.

[0315] A "link" is an online generated URL that allows access to particular content.

[0316] "Emotional state" refers to the user's current feelings and sensations, including sadness, gratitude, joy, and the like.

[0317] "Analysis" is the process of examining data or information in detail and extracting specific features or patterns.

[0318] "Personalization" refers to customizing content and experiences to suit the characteristics and emotions of individual users.

[0319] A "time series" refers to a series of data or events arranged in chronological order.

[0320] A "theme" refers to a category or topic classified based on a certain content or subject.

[0321] A "database" is a system for efficiently storing, searching, and managing data, and is a collection of structured data.

[0322] "Analyzed emotion information" refers to specific emotional information of a user that has been analyzed using the user's emotion engine.

[0323] To implement this invention, the server, terminal, and user work together. First, the user logs in through a dedicated web portal or mobile app, selects photos and videos of the deceased, and uploads them to the system. The user can also add comments and person tags as needed.

[0324] The server stores the data uploaded by users in cloud storage. It analyzes the metadata of the stored data (photography date, location information, person tags, etc.) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0325] The server then retrieves the data from the cloud storage and categorizes it by timeline and theme based on the metadata. It also uses facial recognition technology to detect people in each image and video, extracting data related to a specific person (the deceased). This allows data organization to be focused on the deceased.

[0326] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The emotion engine is used to capture the user's feelings when creating memorial content. Based on the analyzed emotion information, the user's device generates memorial content with appropriate background music and text comments.

[0327] The server automatically generates memorial content in real time based on the organized data and user emotion information acquired from the emotion engine. The generated content is displayed to participants using smart glasses, for example.

[0328] The hardware used includes a client-server network system and smart glasses, and the software used includes cloud storage services (e.g., Amazon S3), face recognition technology (e.g., OpenCV), an emotion engine (EmotionEngine), a content generation module (MediaGenerator), and a display management module (DisplayManager).

[0329] As a concrete example, consider a scenario at a memorial event. As participants wear smart glasses and view photos of the deceased, the emotion engine analyzes the user's emotional state. If the system determines that the participant is sad, it displays a remembrance message about the deceased accompanied by quiet, soothing music.

[0330] An example of a prompt is as follows:

[0331] The user is feeling sad when looking at a photo of the deceased. Generate memorial content that matches this emotion (e.g., calming music and a message).

[0332] As described above, the present invention makes it possible to provide personalized memorial content in real time according to the user's emotions.

[0333] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0334] Step 1:

[0335] Users log in via a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system. The input is the photos and videos selected by the user, and the output is the data uploaded to the server. Specifically, the user presses the file selection button, and then presses the upload button after selection to send the data to the server.

[0336] Step 2:

[0337] The server stores the uploaded data in cloud storage. The input is the data uploaded by the user, and the output is the data stored in cloud storage. Specifically, the data is stored in a cloud storage service (e.g., Amazon S3) via an API.

[0338] Step 3:

[0339] The server analyzes the metadata of the stored data and stores it in a database. The input is the data stored in cloud storage, and the output is the metadata stored in the database. Specifically, the server extracts the shooting date, location information, and person tags from the EXIF ​​information of the image file and stores them in the database.

[0340] Step 4:

[0341] The server retrieves data from cloud storage and classifies it according to time series or theme based on metadata. The input is the data stored in cloud storage and its metadata, and the output is the classified data. Specific operations include sorting the data by the date it was taken, or separating the data by theme, such as "family trip" or "birthday party."

[0342] Step 5:

[0343] The server uses facial recognition technology to detect people in each image or video and extract data related to the specific person (deceased person). The input is classified data, and the output is data related to the specific person. Specifically, it uses OpenCV to perform facial recognition and filters the data based on specific facial features.

[0344] Step 6:

[0345] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The input is the user's facial expressions, tone of voice, and text input, and the output is the user's emotional information. Specifically, the Emotion Engine is used to analyze video and audio data from the user's device in real time.

[0346] Step 7:

[0347] The server generates memorial content from the organized data based on the user's emotional information obtained from the emotion engine. The input is the user's emotional information and the organized data, and the output is personalized memorial content. Specifically, it uses a MediaGenerator to generate content with background music and text comments added according to the user's emotions.

[0348] Step 8:

[0349] The server generates a link for sharing the generated memorial content online. The input is the generated memorial content, and the output is a sharing link. Specific operations include generating a URL and setting specific access permissions.

[0350] Step 9:

[0351] The server sends the generated link to the specified recipient. The input is the shared link and the specified recipient's contact information, and the output is the link sent to the recipient. The specific operation is to call an API to send the link via email or social media.

[0352] Step 10:

[0353] The user's device displays memorial content to the participants via smart glasses. The input is the participant's emotional information and memorial content, and the output is personalized memorial content displayed on the smart glasses. Specifically, the content is displayed on the smart glasses' display using DisplayManager.

[0354] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0355] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0356] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0357] [Second embodiment]

[0358] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0359] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0360] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0361] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0362] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0363] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0364] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0365] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0366] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0367] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0368] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0369] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0370] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0371] Data Collection Phase

[0372] User upload of data

[0373] Users use a dedicated web portal or mobile app to select and upload photos and videos of the deceased, with the option to add comments and tags.

[0374] Receiving and storing data by the server

[0375] The server receives data uploaded by users and stores it in cloud storage. At this time, it analyzes the metadata of each file (e.g., shooting date, location information, person tags) and stores this information in a database. This enables efficient classification in the subsequent data organization phase.

[0376] Data reduction phase

[0377] Classification by Server

[0378] The server retrieves data from cloud storage and organizes it chronologically or by theme based on metadata, for example, sorting photos by date taken or event, or grouping photos and videos that feature a particular person.

[0379] Application of facial recognition technology

[0380] The server uses facial recognition technology to analyze and identify the faces of people in each image or video, extracting data related to that specific person and grouping them separately. This process facilitates the creation of memorial content centered around the deceased.

[0381] Content Generation Phase

[0382] Automatic generation of memorial content

[0383] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0384] Delivery Phase

[0385] Preparing content for distribution

[0386] The server will generate a link to share the generated memorial content online, which will be set up for easy access by users.

[0387] Send to recipient

[0388] The server then sends the generated link to the recipients specified by the user via email or social media, allowing many people to share memories of the deceased.

[0389] Consider the following scenario: When a user uploads photos and videos of a deceased person, the server stores them in cloud storage, analyzes the metadata, and saves them in a database. The server then organizes the data based on the date it was taken or a specific theme (e.g., "family vacation" or "birthday party"). Using facial recognition technology, it extracts images and videos related to a specific person (the deceased), and automatically generates a memorial slideshow or video from these. This content can then be shared with attendees, relatives, and friends via an online link.

[0390] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0391] The processing flow will be explained below.

[0392] Step 1:

[0393] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and are then shown a screen for uploading this data. Users select the files and click the upload button.

[0394] Step 2:

[0395] The device sends the photos and videos selected by the user to the specified cloud storage. Once the upload is complete, it notifies the server of the relevant information (e.g., file name and file size).

[0396] Step 3:

[0397] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is stored in a database.

[0398] Step 4:

[0399] The server uses the metadata stored in the database to categorize uploaded photos and videos by timeline or theme, for example, sorting them by the date they were taken, or grouping them by theme, such as "family vacation" or "birthday party."

[0400] Step 5:

[0401] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts images and videos related to a specific person (the deceased). This allows for organization of the images and videos with a focus on the deceased.

[0402] Step 6:

[0403] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0404] Step 7:

[0405] The server generates a link for sharing the generated memorial content online, which can have specific access permissions and can be configured to make it easy for users to share.

[0406] Step 8:

[0407] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0408] This process allows for quick and efficient organization and sharing of photos and videos of the deceased.

[0409] Example 1

[0410] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0411] In recent years, commemorating the deceased and sharing memories through digital media has become widespread, but many users spend a great deal of time and effort organizing vast amounts of data and creating content. Furthermore, manual classification, facial recognition, and other processes require specialized knowledge, making them a time-consuming process for many. Furthermore, the lack of a consistent system for efficiently performing these tasks increases the burden on users. The purpose of this invention is to solve these problems and provide a system that allows users to easily create and share content for commemorating the deceased.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0413] In this invention, the server includes means for users to upload data, means for saving the data in cloud storage, means for analyzing the metadata of the uploaded data and saving it in a database, means for classifying the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient. This allows users to easily upload data, and the server automatically organizes, classifies, creates, and shares the data, making it possible to provide efficient and moving memorial content.

[0414] "User data uploading means" refers to a means by which a user can use a dedicated web portal or mobile app to select photos and videos of the deceased and send that data to the server.

[0415] "Means for storing data in cloud storage" refers to a means by which a server receives data uploaded by a user and stores it in a distributed data storage system.

[0416] "Means for analyzing the metadata of uploaded data and storing it in a database" refers to a means for analyzing the metadata (e.g., shooting date, location information, person tag) of the data received by the server and storing this information in a relational database.

[0417] "Means for classifying data based on timeline or theme" refers to a means by which the server acquires data based on metadata and organizes the data according to a specific time or specific theme (e.g., photo date, event name).

[0418] "Means for extracting data related to a specific person using facial recognition technology" refers to a means in which a server uses facial recognition technology to analyze faces contained in images or videos, and automatically identify and extract data related to a specific person.

[0419] "Means for automatically generating memorial content from organized data" refers to a means by which a server automatically creates memorial content such as slideshows, videos, and albums based on organized data.

[0420] The "means for generating a link to share the generated content online" is a means for a server to host the generated memorial content and create a URL link to access the content.

[0421] The "means for sending the link to the designated recipient" refers to a means for sending the link generated by the server to the recipient's email address or SNS account designated by the user.

[0422] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0423] First, users use a dedicated web portal or mobile app to select photos and videos of the deceased and upload them to the server. The dedicated web portal is built using front-end frameworks such as ReactJS and Angular, while the mobile app uses Flutter and React Native. Users can add comments and tags as needed. For example, they can add event tags such as "family trip" or "birthday party."

[0424] The server receives the uploaded data and stores it in cloud storage, such as Google Cloud Storage or Amazon S3. The server analyzes the stored data and extracts metadata for each file (e.g., photo date, location information, person tags). The metadata is extracted using Python scripts and libraries such as ExifTool. The extracted metadata is stored in a relational database such as MySQL or PostgreSQL.

[0425] The server then retrieves the stored data from the cloud storage and categorizes it based on time series or themes. This categorization can be done using the Python Pandas library. For example, photos and videos can be organized based on the date they were taken or the name of an event. In particular, categorization based on themes such as "family vacation" or "birthday party" is performed.

[0426] The server then uses facial recognition technology to extract data related to specific individuals. This process utilizes Google Vision API and Amazon Rekognition. Facial recognition technology identifies specific individuals in images and videos and extracts related data. For example, it extracts all images and videos containing the face of a deceased person and groups them separately. This process is important for easily generating memorial content centered around the deceased.

[0427] The server then automatically generates memorial slideshows, videos, and albums based on the organized data, editing the content using image processing software (e.g., FFmpeg or OpenCV), adding background music and text comments, and rendering the automatically generated content into its final format and saving it to cloud storage.

[0428] The server then generates a link for sharing the memorial online. This link can be provided as a URL shortener or a dedicated portal page. The prepared link is then sent to the recipient specified by the user via email or social media. This is done using the mail server SendGrid or Amazon SES, and also using the APIs of social media.

[0429] For example, when a user uploads a photo or video of a deceased person, the following prompt text could be used:

[0430] "Upload photos and videos from the deceased's family vacation and create a touching slideshow."

[0431] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0432] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0433] Program processing steps

[0434] Step 1:

[0435] User upload of data

[0436] Specific behavior:

[0437] Users log in to a dedicated web portal or mobile app.

[0438] Users click the "Select Photo" button and select photos or videos of the deceased from their device.

[0439] After making your selection, press the "Upload" button to send the data to the server.

[0440] Input: User-selected photo and video files, comments and tags.

[0441] Output: The uploaded data sent to the server.

[0442] Step 2:

[0443] Receiving and storing data by the server

[0444] Specific behavior:

[0445] The server receives data uploaded by users.

[0446] Save the received files to cloud storage (such as Google Cloud Storage or Amazon S3).

[0447] At the same time, a script is run to analyze the metadata of the received file.

[0448] Input: User-submitted photos and videos, along with accompanying metadata.

[0449] Output: Files stored in cloud storage and parsed metadata.

[0450] Step 3:

[0451] Server parsing and storing metadata

[0452] Specific behavior:

[0453] The analysis script extracts metadata (Exif information, user-added tags) for each file.

[0454] Store the extracted metadata in a relational database (e.g., MySQL or PostgreSQL).

[0455] Input: A file stored in cloud storage.

[0456] Output: Metadata stored in a database.

[0457] Step 4:

[0458] Data classification by server

[0459] Specific behavior:

[0460] The server retrieves the data from the cloud storage.

[0461] Based on the data obtained, we use Python's Pandas library to classify the data based on time series and themes.

[0462] The classification results are stored in a database.

[0463] Input: Metadata stored in a database.

[0464] Output: Data categorized by theme.

[0465] Step 5:

[0466] Extracting data of specific individuals using facial recognition technology

[0467] Specific behavior:

[0468] The server calls the Google Vision API or Amazon Rekognition.

[0469] Use the API to detect faces in images and videos.

[0470] Identify specific people (deceased) and extract and group relevant data into a new dataset.

[0471] Input: Data retrieved from cloud storage.

[0472] Output: A group of data related to a particular person.

[0473] Step 6:

[0474] Automatic generation of memorial content

[0475] Specific behavior:

[0476] Based on the organized data, the server uses image processing software (FFmpeg or OpenCV) to create slideshows and video templates.

[0477] Automatically add background music and text comments and render to final format.

[0478] Input: Classified data and data relating to specific people.

[0479] Output: Auto-generated slideshows and videos.

[0480] Step 7:

[0481] Generated content is linked and ready to share

[0482] Specific behavior:

[0483] The server stores the generated content in cloud storage.

[0484] Generate links to share online, create URL shorteners and dedicated portal pages.

[0485] Input: Auto-generated memorial content.

[0486] Output: A shareable link.

[0487] Step 8:

[0488] Send link to designated recipient

[0489] Specific behavior:

[0490] The server retrieves contact information for the recipient specified by the user.

[0491] Share the link using a mail server (SendGrid or Amazon SES) or SNS API.

[0492] Log the sending status and send a confirmation email to the user.

[0493] Input: Share link and recipient's contact information.

[0494] Output: Link sent to recipient and confirmation email to user.

[0495] (Application example 1)

[0496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] In today's world, delivery services play an important role in the logistics industry, but there is a growing demand for improved delivery efficiency and customer satisfaction. In particular, there are many challenges in efficiently reaching multiple delivery destinations for delivery personnel and providing services tailored to the characteristics of each customer. Furthermore, there is a lack of effective methods for utilizing data such as accurate location information of delivery destinations and facial recognition of customers. To address these challenges, efficient delivery support through delivery route optimization and data analysis is required.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0499] In this invention, the server includes a means for uploading data to cloud storage, a means for analyzing metadata of the uploaded data and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes, which enables delivery personnel to reach multiple delivery destinations quickly and efficiently, improving customer satisfaction.

[0500] "Cloud storage" is an online storage system for storing and managing data via the Internet.

[0501] "Data upload" is the process of sending and storing data from a local device to cloud storage.

[0502] "Metadata" is attribute information of the data itself, and includes, for example, the date of shooting, location information, person tags, and the like.

[0503] A "database" is a structured collection of data that allows for efficient management and manipulation of data.

[0504] "Time series" is a method of organizing and classifying data along the flow of time.

[0505] A "theme" is a concept that organizes and classifies data based on a specific content or topic.

[0506] "Facial recognition technology" is a technology that detects and identifies the faces of people contained in images and videos.

[0507] "Data extraction" is the process of extracting the necessary information from data.

[0508] "Automatic content generation" means mechanically creating content based on input data.

[0509] An "online sharing link" is a URL link that allows access to content over the Internet.

[0510] "Location information of delivery destination" is data indicating the geographical location of the delivery destination.

[0511] "Delivery route calculation" is the process of calculating the optimal route to efficiently reach multiple delivery destinations.

[0512] To implement this invention, a server, a terminal, and a user mainly work together. The server includes a means for uploading data to cloud storage, a means for analyzing metadata and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes. The terminal includes a smartphone or other mobile device, which a user uses to upload photos and notes of delivery destinations.

[0513] Data Collection Phase

[0514] Users take photos of delivery locations using their smartphones and upload them through a dedicated mobile app. Users can add comments and tags (for example, specific customer needs or points of caution) as needed. The server receives the uploaded photos and stores them in cloud storage.

[0515] Metadata Analysis Phase

[0516] The server analyzes the uploaded photo's metadata, which includes the date it was taken, location information, person tags, etc. This information is stored in a database and used later in the data organization and delivery route calculation phases.

[0517] Data reduction phase

[0518] The server retrieves data from the cloud storage and categorizes the photos based on the collected metadata, for example, by timeline or theme (e.g., "office area," "residential area"), and can also use facial recognition technology to extract and categorize photos related to specific customers.

[0519] Delivery route calculation phase

[0520] The server then uses the organized data and location information to efficiently calculate delivery routes, taking into account distances between specific destinations and traffic conditions. The results are then sent back to the driver's device and displayed.

[0521] Content Generation Phase

[0522] The server uses this organized data to automatically generate delivery schedules and service proposals tailored to each customer's characteristics, using AI technology to take into account the customer's past purchase history and feedback.

[0523] Example

[0524] For example, when a user uploads a "photo of a delivery destination," the server stores it in cloud storage, analyzes the metadata, and saves it in a database. The server then organizes the photos based on the date and location of the photo, and uses facial recognition technology to extract data related to specific customers. Finally, the server calculates an efficient delivery route and notifies the delivery person. This process improves delivery efficiency and customer satisfaction.

[0525] Prompt Sentence Examples

[0526] Prompt: Calculate delivery route by facial recognition and geolocation analysis of delivery destination photos

[0527] Data upload: ['photo1.jpg', 'photo2.jpg']

[0528] User ID: '12345'

[0529] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0530] Step 1:

[0531] Users take photos of delivery destinations using their smartphones and upload these photos to the server through a dedicated mobile app. The photo data of the delivery destination is provided as input, and is saved on the server as output. Specifically, when the user presses the "upload" button on the app, the photos and related notes (comments and tags) are attached.

[0532] Step 2:

[0533] The server receives uploaded photos and metadata (photo date, location information, person tags) and stores them in cloud storage. The input is the photo data and metadata uploaded by the user, and the output is the data stored in cloud storage. The server verifies the received data, converts it into the required format, and stores it.

[0534] Step 3:

[0535] The server analyzes the metadata from the data on the cloud storage and stores it in a database. In this process, it uses facial recognition technology to analyze the faces of people in the photos. The photo data and metadata from the cloud storage are used as input, and the analyzed metadata is stored in the database as output. Specifically, the server runs a facial recognition algorithm to identify the faces of people in the photos.

[0536] Step 4:

[0537] The server uses the analyzed metadata to classify photos based on time sequence and theme (e.g., "office area," "residential area"). Using the analyzed metadata as input, the server obtains organized photo data as output. Specifically, the server groups the data based on the time of shooting and location information, and then categorizes them into appropriate categories.

[0538] Step 5:

[0539] The server uses the organized data to analyze the location information of the delivery destinations and calculates an efficient delivery route. The organized data including location information is used as input, and the delivery route is obtained as output. The server uses a specific algorithm (e.g., Dijkstra's algorithm) to calculate the optimal order of delivery destinations.

[0540] Step 6:

[0541] The server sends the calculated delivery route information to the device (smartphone) and displays delivery instructions to the user. The calculated delivery route information is used as input, and delivery instructions are obtained as output and displayed on the device. Specifically, the server converts the delivery route information into JSON format and sends it to the device app.

[0542] Step 7:

[0543] Once the delivery is complete, the user uses the device to confirm the delivery completion and, if necessary, send feedback to the server. The completion report and feedback data are provided as input, and the output is saved in cloud storage. Specifically, the user presses the "Delivery Complete" button to send the completion report to the server.

[0544] This process creates a system that provides efficient delivery routes and supports the work of delivery personnel.

[0545] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0546] To implement this invention, the server, the terminal, and the user work together. One of the features of this invention is that it incorporates an emotion engine that recognizes the user's emotions and reflects them in the generation of memorial content. The specific operation of this engine is described below.

[0547] Data Collection Phase

[0548] User upload of data

[0549] Users log in through a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system, adding comments and person tags as needed.

[0550] Receiving and storing data by the server

[0551] The server stores the data uploaded by users in cloud storage. It also analyzes the metadata of each file (such as the date of shooting, location information, and person tags) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0552] Data reduction phase

[0553] Classification by Server

[0554] The server retrieves the data from the cloud storage and categorizes it based on the metadata, such as by the date the photos were taken, or by themes like "family vacation" or "birthday party."

[0555] Application of facial recognition technology

[0556] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts data related to the specific person (deceased person). This allows data organization centered on the deceased person.

[0557] Applying the Emotion Engine

[0558] Recognizing user emotions

[0559] The device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input, and is used to capture the user's feelings when creating memorial content.

[0560] Content Generation Phase

[0561] Automatic generation of memorial content

[0562] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information obtained from the emotion engine. By adding appropriate background music and text comments according to the emotional information, more moving content can be created.

[0563] For example, when a user creates memorial content, the server analyzes the user's facial expressions and voice in real time to recognize feelings of sadness or gratitude. If the user feels sad, the server adds quiet, relaxing music, and if the user expresses gratitude, it inserts a warm comment.

[0564] Delivery Phase

[0565] Preparing content for distribution

[0566] The server generates a link for sharing the generated memorial content online, with specific access permissions configured to make it easy for users to share.

[0567] Send to recipient

[0568] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0569] This process allows for fast and efficient organization and sharing of photos and videos of the deceased, while providing personalized memorial content tailored to the user's emotions.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and begin uploading. On the upload screen, select the files and add comments and tags as needed.

[0573] Step 2:

[0574] The device sends the photos and videos specified by the user to cloud storage, and once the transfer is complete, it notifies the server of the relevant metadata (file name, file size, etc.).

[0575] Step 3:

[0576] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is then stored in a database.

[0577] Step 4:

[0578] The server uses the metadata in the database to categorize the uploaded photos and videos by timeline or theme, for example, by a specific theme such as "family vacation" or "birthday party."

[0579] Step 5:

[0580] The server uses facial recognition technology to detect human faces in each image or video, analyzes the detected facial features, extracts data related to a specific person (deceased person), and groups them separately.

[0581] Step 6:

[0582] The device uses an emotion engine to recognize the user's emotions in real time, analyzing changes in facial expressions, tone of voice, and text input to determine the user's emotional state (e.g., sadness, gratitude, etc.).

[0583] Step 7:

[0584] The server automatically generates memorial slideshows, videos, and albums based on the collected data and the emotional information obtained using an emotion engine. For example, if the user is feeling sad, it will add calm and quiet background music, and if the user is expressing gratitude, it will insert warm comments.

[0585] Step 8:

[0586] The server generates a link to share the generated memorial content online, which can have specific access permissions and can be easily accessed by those with whom the link is shared.

[0587] Step 9:

[0588] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0589] The system efficiently organizes photos and videos of the deceased, automatically generates emotionally sensitive memorial content, and allows people to share their memories in a moving way.

[0590] Example 2

[0591] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0592] Currently, creating memorial content requires users to manually select and edit photos and videos, which requires a great deal of time and effort. Furthermore, existing systems do not personalize content based on the user's emotions, making it difficult to create moving memorial content. Furthermore, there is a lack of efficient ways to share the generated content. A system that can solve these issues is needed.

[0593] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0594] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for recognizing a user's emotions, means for automatically generating memorial content based on the organized data and emotional information, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient, thereby reducing the burden on the user and enabling the generation and sharing of moving and personalized memorial content.

[0595] "Cloud storage" is a storage system built on remote servers where data can be stored and accessed over the Internet.

[0596] "Data upload" is the operation of sending files such as photos and videos from a user's device to a remote server.

[0597] "Metadata" refers to file attribute information, and includes, for example, the date of shooting, location information, and person tags.

[0598] A "database" is a system that stores data systematically and enables fast and efficient retrieval, storage, and updating.

[0599] "Chronological" refers to a method of arranging data in chronological order, either from most recent to oldest.

[0600] A "theme" is a criterion for classifying data into a specific subject or category, examples of which include "family vacation" or "birthday party."

[0601] "Facial recognition technology" is a technology that detects and individually identifies human faces in images and videos.

[0602] "Emotion recognition" is the process of analyzing and identifying a user's emotional state from facial expressions, tone of voice, and text input.

[0603] "Memorial content" refers to digital media such as slideshows, videos, and albums created to commemorate and share memories of the deceased.

[0604] "Link" refers to a hypertext link or URL that allows access to specific data or content on the Internet.

[0605] "Recipient" refers to the person or group that receives the link to the content that a User wishes to share.

[0606] To implement the present invention, the server, the terminal, and the user work together. The specific operation will be described below.

[0607] First, users register an account using a dedicated web portal or mobile app and log in. At this time, the system authenticates the user through an authentication server. After logging in, users select photos and videos of the deceased from their device and upload them to the system. They can also add comments and person tags when uploading.

[0608] The server receives the uploaded data via HTTP requests and stores it in cloud storage (e.g., Amazon S3). It also uses a metadata analysis library such as ExifTool to analyze the metadata of the received data (e.g., shooting date, location information, person tags) and stores it in a database.

[0609] The server then categorizes the data based on timeline and themes. Specifically, it retrieves metadata from the database using SQL queries and applies classification algorithms. It then uses facial recognition technology (e.g., OpenCV or AWS Rekognition) to detect faces in each image or video and extract data related to specific people. This information is then stored in a database.

[0610] The device uses an emotion engine, such as the Microsoft Azure Emotion API, to recognize the user's emotions. While the user is creating memorial content, the device analyzes video camera and microphone data in real time. This emotional information is then sent to the server.

[0611] Based on the organized data and emotional information, the server automatically generates a memorial slideshow, video, or album. Specifically, it uses personalized audio generation software (e.g., AIVA) and text generation models (e.g., GPT-3) to generate background music and comments. For example, if the user's emotion is sadness, quiet, relaxing music is added, and if the user expresses gratitude, warm comments are inserted.

[0612] Here are some examples of prompts:

[0613] "Select appropriate music based on the user's facial expressions and voice analysis."

[0614] "Generate text comments according to emotional information."

[0615] Finally, the server generates a link for sharing the memorial content and sets specific access permissions. This link is sent to the recipients specified by the user using an email sending API (e.g., SendGrid) or a social networking API, allowing users to easily share memories of the deceased with attendees, relatives, and friends.

[0616] This system reduces the burden on users and enables the creation and efficient sharing of moving and personalized memorial content.

[0617] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0618] Step 1:

[0619] A user opens a dedicated web portal or mobile app, registers an account, and logs in. As input, the user enters their email address and password, and as output, the authentication server returns the authentication result. Specifically, the user is authenticated via the authentication API.

[0620] Step 2:

[0621] After logging in, the user selects photos and videos of the deceased from their device and uploads them to the system. The input is the photo or video file selected by the user, and the output is the data sent to the server. Specifically, the web portal or mobile app uses a file selection button, and the data is uploaded to the server via an HTTP request.

[0622] Step 3:

[0623] The server receives the uploaded data and stores it in cloud storage. The input is the photo and video data received by the server, and the output is the file stored in cloud storage. The specific operation is to store the data in a cloud storage service such as Amazon S3.

[0624] Step 4:

[0625] The server uses a metadata analysis library such as ExifTool to analyze the metadata of the uploaded data and store it in a database. The uploaded photo or video file is used as input, and the analyzed metadata is used as output and stored in the database. Specifically, the metadata analysis library is called to extract information and then stored in the database using SQL queries.

[0626] Step 5:

[0627] The server retrieves metadata from the database and categorizes the data based on timeline and themes. The input is the retrieved metadata, and the output is the categorized data. Specifically, it retrieves the metadata using SQL queries and applies classification algorithms to organize the data.

[0628] Step 6:

[0629] The server uses facial recognition technology (such as OpenCV or AWS Rekognition) to detect faces in images and videos and extract data related to specific people. Uploaded images and video files are used as input, and recognized face data is used as output, which is then stored in a database. Specifically, the server calls a facial recognition API to perform face detection and stores the results in a database.

[0630] Step 7:

[0631] The device recognizes the user's emotions using an emotion engine (such as the Microsoft Azure Emotion API). It receives real-time data from a video camera and microphone as input, and analyzes emotional data as output. Specifically, it analyzes the user's facial expressions and tone of voice, and sends the information to a server.

[0632] Step 8:

[0633] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information. The input is the organized data and emotional information, and the generated memorial content is obtained as the output. Specifically, the system uses personalized audio generation software (such as AIVA) and text generation models (such as GPT-3).

[0634] Step 9:

[0635] The server generates a link for sharing the generated memorial content and sets specific access permissions. The input is the generated memorial content, and the output is the generated link. Specifically, the server uses the cloud storage's shared link function or API to generate the link and set access permissions.

[0636] Step 10:

[0637] The server sends the generated link to the specified recipient via email or social media. The input is the recipient information specified by the user and the generated link, and the output is the link sent to the recipient. Specific operations use an email sending API (such as SendGrid) or a social media API.

[0638] (Application example 2)

[0639] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0640] Conventional memorial content creation systems generate content without considering the user's emotional state, making it difficult to provide a personalized experience that reflects individual emotions. Furthermore, they are unable to respond to the emotions of event participants in real time, making it difficult to provide appropriate content that reflects their emotions at memorial events.

[0641] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0642] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, means for sending the link to a specified recipient, means for analyzing the user's emotional state, and means for displaying memorial content according to the analyzed emotional information, thereby enabling the generation and display of personalized memorial content in real time in accordance with the emotions of participants.

[0643] "Cloud storage" is a system for storing and managing data on remote servers via the Internet.

[0644] "Metadata" refers to information about the data itself, and examples include the date it was taken, location information, and person tags.

[0645] "Facial recognition technology" is a technology for detecting the faces of people in images or videos and identifying specific people.

[0646] "Memorial content" is a moving combination of photos, video, music, and text used to remember a deceased person.

[0647] A "link" is an online generated URL that allows access to particular content.

[0648] "Emotional state" refers to the user's current feelings and sensations, including sadness, gratitude, joy, and the like.

[0649] "Analysis" is the process of examining data or information in detail and extracting specific features or patterns.

[0650] "Personalization" refers to customizing content and experiences to suit the characteristics and emotions of individual users.

[0651] A "time series" refers to a series of data or events arranged in chronological order.

[0652] A "theme" refers to a category or topic classified based on a certain content or subject.

[0653] A "database" is a system for efficiently storing, searching, and managing data, and is a collection of structured data.

[0654] "Analyzed emotion information" refers to specific emotional information of a user that has been analyzed using the user's emotion engine.

[0655] To implement this invention, the server, terminal, and user work together. First, the user logs in through a dedicated web portal or mobile app, selects photos and videos of the deceased, and uploads them to the system. The user can also add comments and person tags as needed.

[0656] The server stores the data uploaded by users in cloud storage. It analyzes the metadata of the stored data (photography date, location information, person tags, etc.) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0657] The server then retrieves the data from the cloud storage and categorizes it by timeline and theme based on the metadata. It also uses facial recognition technology to detect people in each image and video, extracting data related to a specific person (the deceased). This allows data organization to be focused on the deceased.

[0658] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The emotion engine is used to capture the user's feelings when creating memorial content. Based on the analyzed emotion information, the user's device generates memorial content with appropriate background music and text comments.

[0659] The server automatically generates memorial content in real time based on the organized data and user emotion information acquired from the emotion engine. The generated content is displayed to participants using smart glasses, for example.

[0660] The hardware used includes a client-server network system and smart glasses, and the software used includes cloud storage services (e.g., Amazon S3), face recognition technology (e.g., OpenCV), an emotion engine (EmotionEngine), a content generation module (MediaGenerator), and a display management module (DisplayManager).

[0661] As a concrete example, consider a scenario at a memorial event. As participants wear smart glasses and view photos of the deceased, the emotion engine analyzes the user's emotional state. If the system determines that the participant is sad, it displays a remembrance message about the deceased accompanied by quiet, soothing music.

[0662] An example of a prompt is as follows:

[0663] The user is feeling sad when looking at a photo of the deceased. Generate memorial content that matches this emotion (e.g., calming music and a message).

[0664] As described above, the present invention makes it possible to provide personalized memorial content in real time according to the user's emotions.

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] Users log in via a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system. The input is the photos and videos selected by the user, and the output is the data uploaded to the server. Specifically, the user presses the file selection button, and then presses the upload button after selection to send the data to the server.

[0668] Step 2:

[0669] The server stores the uploaded data in cloud storage. The input is the data uploaded by the user, and the output is the data stored in cloud storage. Specifically, the data is stored in a cloud storage service (e.g., Amazon S3) via an API.

[0670] Step 3:

[0671] The server analyzes the metadata of the stored data and stores it in a database. The input is the data stored in cloud storage, and the output is the metadata stored in the database. Specifically, the server extracts the shooting date, location information, and person tags from the EXIF ​​information of the image file and stores them in the database.

[0672] Step 4:

[0673] The server retrieves data from cloud storage and classifies it according to time series or theme based on metadata. The input is the data stored in cloud storage and its metadata, and the output is the classified data. Specific operations include sorting the data by the date it was taken, or separating the data by theme, such as "family trip" or "birthday party."

[0674] Step 5:

[0675] The server uses facial recognition technology to detect people in each image or video and extract data related to the specific person (deceased person). The input is classified data, and the output is data related to the specific person. Specifically, it uses OpenCV to perform facial recognition and filters the data based on specific facial features.

[0676] Step 6:

[0677] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The input is the user's facial expressions, tone of voice, and text input, and the output is the user's emotional information. Specifically, the Emotion Engine is used to analyze video and audio data from the user's device in real time.

[0678] Step 7:

[0679] The server generates memorial content from the organized data based on the user's emotional information obtained from the emotion engine. The input is the user's emotional information and the organized data, and the output is personalized memorial content. Specifically, it uses a MediaGenerator to generate content with background music and text comments added according to the user's emotions.

[0680] Step 8:

[0681] The server generates a link for sharing the generated memorial content online. The input is the generated memorial content, and the output is a sharing link. Specific operations include generating a URL and setting specific access permissions.

[0682] Step 9:

[0683] The server sends the generated link to the specified recipient. The input is the shared link and the specified recipient's contact information, and the output is the link sent to the recipient. The specific operation is to call an API to send the link via email or social media.

[0684] Step 10:

[0685] The user's device displays memorial content to the participants via smart glasses. The input is the participant's emotional information and memorial content, and the output is personalized memorial content displayed on the smart glasses. Specifically, the content is displayed on the smart glasses' display using DisplayManager.

[0686] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0688] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0689] [Third embodiment]

[0690] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0691] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0692] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0693] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0694] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0695] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0696] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0697] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0698] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0699] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0700] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0701] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0702] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0703] Data Collection Phase

[0704] User upload of data

[0705] Users use a dedicated web portal or mobile app to select and upload photos and videos of the deceased, with the option to add comments and tags.

[0706] Receiving and storing data by the server

[0707] The server receives data uploaded by users and stores it in cloud storage. At this time, it analyzes the metadata of each file (e.g., shooting date, location information, person tags) and stores this information in a database. This enables efficient classification in the subsequent data organization phase.

[0708] Data reduction phase

[0709] Classification by Server

[0710] The server retrieves data from cloud storage and organizes it chronologically or by theme based on metadata, for example, sorting photos by date taken or event, or grouping photos and videos that feature a particular person.

[0711] Application of facial recognition technology

[0712] The server uses facial recognition technology to analyze and identify the faces of people in each image or video, extracting data related to that specific person and grouping them separately. This process facilitates the creation of memorial content centered around the deceased.

[0713] Content Generation Phase

[0714] Automatic generation of memorial content

[0715] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0716] Delivery Phase

[0717] Preparing content for distribution

[0718] The server will generate a link to share the generated memorial content online, which will be set up for easy access by users.

[0719] Send to recipient

[0720] The server then sends the generated link to the recipients specified by the user via email or social media, allowing many people to share memories of the deceased.

[0721] Consider the following scenario: When a user uploads photos and videos of a deceased person, the server stores them in cloud storage, analyzes the metadata, and saves them in a database. The server then organizes the data based on the date it was taken or a specific theme (e.g., "family vacation" or "birthday party"). Using facial recognition technology, it extracts images and videos related to a specific person (the deceased), and automatically generates a memorial slideshow or video from these. This content can then be shared with attendees, relatives, and friends via an online link.

[0722] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0723] The processing flow will be explained below.

[0724] Step 1:

[0725] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and are then shown a screen for uploading this data. Users select the files and click the upload button.

[0726] Step 2:

[0727] The device sends the photos and videos selected by the user to the specified cloud storage. Once the upload is complete, it notifies the server of the relevant information (e.g., file name and file size).

[0728] Step 3:

[0729] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is stored in a database.

[0730] Step 4:

[0731] The server uses the metadata stored in the database to categorize uploaded photos and videos by timeline or theme, for example, sorting them by the date they were taken, or grouping them by theme, such as "family vacation" or "birthday party."

[0732] Step 5:

[0733] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts images and videos related to a specific person (the deceased). This allows for organization of the images and videos with a focus on the deceased.

[0734] Step 6:

[0735] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[0736] Step 7:

[0737] The server generates a link for sharing the generated memorial content online, which can have specific access permissions and can be configured to make it easy for users to share.

[0738] Step 8:

[0739] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0740] This process allows for quick and efficient organization and sharing of photos and videos of the deceased.

[0741] Example 1

[0742] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0743] In recent years, commemorating the deceased and sharing memories through digital media has become widespread, but many users spend a great deal of time and effort organizing vast amounts of data and creating content. Furthermore, manual classification, facial recognition, and other processes require specialized knowledge, making them a time-consuming process for many. Furthermore, the lack of a consistent system for efficiently performing these tasks increases the burden on users. The purpose of this invention is to solve these problems and provide a system that allows users to easily create and share content for commemorating the deceased.

[0744] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0745] In this invention, the server includes means for users to upload data, means for saving the data in cloud storage, means for analyzing the metadata of the uploaded data and saving it in a database, means for classifying the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient. This allows users to easily upload data, and the server automatically organizes, classifies, creates, and shares the data, making it possible to provide efficient and moving memorial content.

[0746] "User data uploading means" refers to a means by which a user can use a dedicated web portal or mobile app to select photos and videos of the deceased and send that data to the server.

[0747] "Means for storing data in cloud storage" refers to a means by which a server receives data uploaded by a user and stores it in a distributed data storage system.

[0748] "Means for analyzing the metadata of uploaded data and storing it in a database" refers to a means for analyzing the metadata (e.g., shooting date, location information, person tag) of the data received by the server and storing this information in a relational database.

[0749] "Means for classifying data based on timeline or theme" refers to a means by which the server acquires data based on metadata and organizes the data according to a specific time or specific theme (e.g., photo date, event name).

[0750] "Means for extracting data related to a specific person using facial recognition technology" refers to a means in which a server uses facial recognition technology to analyze faces contained in images or videos, and automatically identify and extract data related to a specific person.

[0751] "Means for automatically generating memorial content from organized data" refers to a means by which a server automatically creates memorial content such as slideshows, videos, and albums based on organized data.

[0752] The "means for generating a link to share the generated content online" is a means for a server to host the generated memorial content and create a URL link to access the content.

[0753] The "means for sending the link to the designated recipient" refers to a means for sending the link generated by the server to the recipient's email address or SNS account designated by the user.

[0754] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[0755] First, users use a dedicated web portal or mobile app to select photos and videos of the deceased and upload them to the server. The dedicated web portal is built using front-end frameworks such as ReactJS and Angular, while the mobile app uses Flutter and React Native. Users can add comments and tags as needed. For example, they can add event tags such as "family trip" or "birthday party."

[0756] The server receives the uploaded data and stores it in cloud storage, such as Google Cloud Storage or Amazon S3. The server analyzes the stored data and extracts metadata for each file (e.g., photo date, location information, person tags). The metadata is extracted using Python scripts and libraries such as ExifTool. The extracted metadata is stored in a relational database such as MySQL or PostgreSQL.

[0757] The server then retrieves the stored data from the cloud storage and categorizes it based on time series or themes. This categorization can be done using the Python Pandas library. For example, photos and videos can be organized based on the date they were taken or the name of an event. In particular, categorization based on themes such as "family vacation" or "birthday party" is performed.

[0758] The server then uses facial recognition technology to extract data related to specific individuals. This process utilizes Google Vision API and Amazon Rekognition. Facial recognition technology identifies specific individuals in images and videos and extracts related data. For example, it extracts all images and videos containing the face of a deceased person and groups them separately. This process is important for easily generating memorial content centered around the deceased.

[0759] The server then automatically generates memorial slideshows, videos, and albums based on the organized data, editing the content using image processing software (e.g., FFmpeg or OpenCV), adding background music and text comments, and rendering the automatically generated content into its final format and saving it to cloud storage.

[0760] The server then generates a link for sharing the memorial online. This link can be provided as a URL shortener or a dedicated portal page. The prepared link is then sent to the recipient specified by the user via email or social media. This is done using the mail server SendGrid or Amazon SES, and also using the APIs of social media.

[0761] For example, when a user uploads a photo or video of a deceased person, the following prompt text could be used:

[0762] "Upload photos and videos from the deceased's family vacation and create a touching slideshow."

[0763] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[0764] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0765] Program processing steps

[0766] Step 1:

[0767] User upload of data

[0768] Specific behavior:

[0769] Users log in to a dedicated web portal or mobile app.

[0770] Users click the "Select Photo" button and select photos or videos of the deceased from their device.

[0771] After making your selection, press the "Upload" button to send the data to the server.

[0772] Input: User-selected photo and video files, comments and tags.

[0773] Output: The uploaded data sent to the server.

[0774] Step 2:

[0775] Receiving and storing data by the server

[0776] Specific behavior:

[0777] The server receives data uploaded by users.

[0778] Save the received files to cloud storage (such as Google Cloud Storage or Amazon S3).

[0779] At the same time, a script is run to analyze the metadata of the received file.

[0780] Input: User-submitted photos and videos, along with accompanying metadata.

[0781] Output: Files stored in cloud storage and parsed metadata.

[0782] Step 3:

[0783] Server parsing and storing metadata

[0784] Specific behavior:

[0785] The analysis script extracts metadata (Exif information, user-added tags) for each file.

[0786] Store the extracted metadata in a relational database (e.g., MySQL or PostgreSQL).

[0787] Input: A file stored in cloud storage.

[0788] Output: Metadata stored in a database.

[0789] Step 4:

[0790] Data classification by server

[0791] Specific behavior:

[0792] The server retrieves the data from the cloud storage.

[0793] Based on the data obtained, we use Python's Pandas library to classify the data based on time series and themes.

[0794] The classification results are stored in a database.

[0795] Input: Metadata stored in a database.

[0796] Output: Data categorized by theme.

[0797] Step 5:

[0798] Extracting data of specific individuals using facial recognition technology

[0799] Specific behavior:

[0800] The server calls the Google Vision API or Amazon Rekognition.

[0801] Use the API to detect faces in images and videos.

[0802] Identify specific people (deceased) and extract and group relevant data into a new dataset.

[0803] Input: Data retrieved from cloud storage.

[0804] Output: A group of data related to a particular person.

[0805] Step 6:

[0806] Automatic generation of memorial content

[0807] Specific behavior:

[0808] Based on the organized data, the server uses image processing software (FFmpeg or OpenCV) to create slideshows and video templates.

[0809] Automatically add background music and text comments and render to final format.

[0810] Input: Classified data and data relating to specific people.

[0811] Output: Auto-generated slideshows and videos.

[0812] Step 7:

[0813] Generated content is linked and ready to share

[0814] Specific behavior:

[0815] The server stores the generated content in cloud storage.

[0816] Generate links to share online, create URL shorteners and dedicated portal pages.

[0817] Input: Auto-generated memorial content.

[0818] Output: A shareable link.

[0819] Step 8:

[0820] Send link to designated recipient

[0821] Specific behavior:

[0822] The server retrieves contact information for the recipient specified by the user.

[0823] Share the link using a mail server (SendGrid or Amazon SES) or SNS API.

[0824] Log the sending status and send a confirmation email to the user.

[0825] Input: Share link and recipient's contact information.

[0826] Output: Link sent to recipient and confirmation email to user.

[0827] (Application example 1)

[0828] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] In today's world, delivery services play an important role in the logistics industry, but there is a growing demand for improved delivery efficiency and customer satisfaction. In particular, there are many challenges in efficiently reaching multiple delivery destinations for delivery personnel and providing services tailored to the characteristics of each customer. Furthermore, there is a lack of effective methods for utilizing data such as accurate location information of delivery destinations and facial recognition of customers. To address these challenges, efficient delivery support through delivery route optimization and data analysis is required.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0831] In this invention, the server includes a means for uploading data to cloud storage, a means for analyzing metadata of the uploaded data and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes, which enables delivery personnel to reach multiple delivery destinations quickly and efficiently, improving customer satisfaction.

[0832] "Cloud storage" is an online storage system for storing and managing data via the Internet.

[0833] "Data upload" is the process of sending and storing data from a local device to cloud storage.

[0834] "Metadata" is attribute information of the data itself, and includes, for example, the date of shooting, location information, person tags, and the like.

[0835] A "database" is a structured collection of data that allows for efficient management and manipulation of data.

[0836] "Time series" is a method of organizing and classifying data along the flow of time.

[0837] A "theme" is a concept that organizes and classifies data based on a specific content or topic.

[0838] "Facial recognition technology" is a technology that detects and identifies the faces of people contained in images and videos.

[0839] "Data extraction" is the process of extracting the necessary information from data.

[0840] "Automatic content generation" means mechanically creating content based on input data.

[0841] An "online sharing link" is a URL link that allows access to content over the Internet.

[0842] "Location information of delivery destination" is data indicating the geographical location of the delivery destination.

[0843] "Delivery route calculation" is the process of calculating the optimal route to efficiently reach multiple delivery destinations.

[0844] To implement this invention, a server, a terminal, and a user mainly work together. The server includes a means for uploading data to cloud storage, a means for analyzing metadata and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes. The terminal includes a smartphone or other mobile device, which a user uses to upload photos and notes of delivery destinations.

[0845] Data Collection Phase

[0846] Users take photos of delivery locations using their smartphones and upload them through a dedicated mobile app. Users can add comments and tags (for example, specific customer needs or points of caution) as needed. The server receives the uploaded photos and stores them in cloud storage.

[0847] Metadata Analysis Phase

[0848] The server analyzes the uploaded photo's metadata, which includes the date it was taken, location information, person tags, etc. This information is stored in a database and used later in the data organization and delivery route calculation phases.

[0849] Data reduction phase

[0850] The server retrieves data from the cloud storage and categorizes the photos based on the collected metadata, for example, by timeline or theme (e.g., "office area," "residential area"), and can also use facial recognition technology to extract and categorize photos related to specific customers.

[0851] Delivery route calculation phase

[0852] The server then uses the organized data and location information to efficiently calculate delivery routes, taking into account distances between specific destinations and traffic conditions. The results are then sent back to the driver's device and displayed.

[0853] Content Generation Phase

[0854] The server uses this organized data to automatically generate delivery schedules and service proposals tailored to each customer's characteristics, using AI technology to take into account the customer's past purchase history and feedback.

[0855] Example

[0856] For example, when a user uploads a "photo of a delivery destination," the server stores it in cloud storage, analyzes the metadata, and saves it in a database. The server then organizes the photos based on the date and location of the photo, and uses facial recognition technology to extract data related to specific customers. Finally, the server calculates an efficient delivery route and notifies the delivery person. This process improves delivery efficiency and customer satisfaction.

[0857] Prompt Sentence Examples

[0858] Prompt: Calculate delivery route by facial recognition and geolocation analysis of delivery destination photos

[0859] Data upload: ['photo1.jpg', 'photo2.jpg']

[0860] User ID: '12345'

[0861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0862] Step 1:

[0863] Users take photos of delivery destinations using their smartphones and upload these photos to the server through a dedicated mobile app. The photo data of the delivery destination is provided as input, and is saved on the server as output. Specifically, when the user presses the "upload" button on the app, the photos and related notes (comments and tags) are attached.

[0864] Step 2:

[0865] The server receives uploaded photos and metadata (photo date, location information, person tags) and stores them in cloud storage. The input is the photo data and metadata uploaded by the user, and the output is the data stored in cloud storage. The server verifies the received data, converts it into the required format, and stores it.

[0866] Step 3:

[0867] The server analyzes the metadata from the data on the cloud storage and stores it in a database. In this process, it uses facial recognition technology to analyze the faces of people in the photos. The photo data and metadata from the cloud storage are used as input, and the analyzed metadata is stored in the database as output. Specifically, the server runs a facial recognition algorithm to identify the faces of people in the photos.

[0868] Step 4:

[0869] The server uses the analyzed metadata to classify photos based on time sequence and theme (e.g., "office area," "residential area"). Using the analyzed metadata as input, the server obtains organized photo data as output. Specifically, the server groups the data based on the time of shooting and location information, and then categorizes them into appropriate categories.

[0870] Step 5:

[0871] The server uses the organized data to analyze the location information of the delivery destinations and calculates an efficient delivery route. The organized data including location information is used as input, and the delivery route is obtained as output. The server uses a specific algorithm (e.g., Dijkstra's algorithm) to calculate the optimal order of delivery destinations.

[0872] Step 6:

[0873] The server sends the calculated delivery route information to the device (smartphone) and displays delivery instructions to the user. The calculated delivery route information is used as input, and delivery instructions are obtained as output and displayed on the device. Specifically, the server converts the delivery route information into JSON format and sends it to the device app.

[0874] Step 7:

[0875] Once the delivery is complete, the user uses the device to confirm the delivery completion and, if necessary, send feedback to the server. The completion report and feedback data are provided as input, and the output is saved in cloud storage. Specifically, the user presses the "Delivery Complete" button to send the completion report to the server.

[0876] This process creates a system that provides efficient delivery routes and supports the work of delivery personnel.

[0877] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0878] To implement this invention, the server, the terminal, and the user work together. One of the features of this invention is that it incorporates an emotion engine that recognizes the user's emotions and reflects them in the generation of memorial content. The specific operation of this engine is described below.

[0879] Data Collection Phase

[0880] User upload of data

[0881] Users log in through a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system, adding comments and person tags as needed.

[0882] Receiving and storing data by the server

[0883] The server stores the data uploaded by users in cloud storage. It also analyzes the metadata of each file (such as the date of shooting, location information, and person tags) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0884] Data reduction phase

[0885] Classification by Server

[0886] The server retrieves the data from the cloud storage and categorizes it based on the metadata, such as by the date the photos were taken, or by themes like "family vacation" or "birthday party."

[0887] Application of facial recognition technology

[0888] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts data related to the specific person (deceased person). This allows data organization centered on the deceased person.

[0889] Applying the Emotion Engine

[0890] Recognizing user emotions

[0891] The device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input, and is used to capture the user's feelings when creating memorial content.

[0892] Content Generation Phase

[0893] Automatic generation of memorial content

[0894] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information obtained from the emotion engine. By adding appropriate background music and text comments according to the emotional information, more moving content can be created.

[0895] For example, when a user creates memorial content, the server analyzes the user's facial expressions and voice in real time to recognize feelings of sadness or gratitude. If the user feels sad, the server adds quiet, relaxing music, and if the user expresses gratitude, it inserts a warm comment.

[0896] Delivery Phase

[0897] Preparing content for distribution

[0898] The server generates a link for sharing the generated memorial content online, with specific access permissions configured to make it easy for users to share.

[0899] Send to recipient

[0900] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0901] This process allows for fast and efficient organization and sharing of photos and videos of the deceased, while providing personalized memorial content tailored to the user's emotions.

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and begin uploading. On the upload screen, select the files and add comments and tags as needed.

[0905] Step 2:

[0906] The device sends the photos and videos specified by the user to cloud storage, and once the transfer is complete, it notifies the server of the relevant metadata (file name, file size, etc.).

[0907] Step 3:

[0908] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is then stored in a database.

[0909] Step 4:

[0910] The server uses the metadata in the database to categorize the uploaded photos and videos by timeline or theme, for example, by a specific theme such as "family vacation" or "birthday party."

[0911] Step 5:

[0912] The server uses facial recognition technology to detect human faces in each image or video, analyzes the detected facial features, extracts data related to a specific person (deceased person), and groups them separately.

[0913] Step 6:

[0914] The device uses an emotion engine to recognize the user's emotions in real time, analyzing changes in facial expressions, tone of voice, and text input to determine the user's emotional state (e.g., sadness, gratitude, etc.).

[0915] Step 7:

[0916] The server automatically generates memorial slideshows, videos, and albums based on the collected data and the emotional information obtained using an emotion engine. For example, if the user is feeling sad, it will add calm and quiet background music, and if the user is expressing gratitude, it will insert warm comments.

[0917] Step 8:

[0918] The server generates a link to share the generated memorial content online, which can have specific access permissions and can be easily accessed by those with whom the link is shared.

[0919] Step 9:

[0920] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[0921] The system efficiently organizes photos and videos of the deceased, automatically generates emotionally sensitive memorial content, and allows people to share their memories in a moving way.

[0922] Example 2

[0923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0924] Currently, creating memorial content requires users to manually select and edit photos and videos, which requires a great deal of time and effort. Furthermore, existing systems do not personalize content based on the user's emotions, making it difficult to create moving memorial content. Furthermore, there is a lack of efficient ways to share the generated content. A system that can solve these issues is needed.

[0925] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0926] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for recognizing a user's emotions, means for automatically generating memorial content based on the organized data and emotional information, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient, thereby reducing the burden on the user and enabling the generation and sharing of moving and personalized memorial content.

[0927] "Cloud storage" is a storage system built on remote servers where data can be stored and accessed over the Internet.

[0928] "Data upload" is the operation of sending files such as photos and videos from a user's device to a remote server.

[0929] "Metadata" refers to file attribute information, and includes, for example, the date of shooting, location information, and person tags.

[0930] A "database" is a system that stores data systematically and enables fast and efficient retrieval, storage, and updating.

[0931] "Chronological" refers to a method of arranging data in chronological order, either from most recent to oldest.

[0932] A "theme" is a criterion for classifying data into a specific subject or category, examples of which include "family vacation" or "birthday party."

[0933] "Facial recognition technology" is a technology that detects and individually identifies human faces in images and videos.

[0934] "Emotion recognition" is the process of analyzing and identifying a user's emotional state from facial expressions, tone of voice, and text input.

[0935] "Memorial content" refers to digital media such as slideshows, videos, and albums created to commemorate and share memories of the deceased.

[0936] "Link" refers to a hypertext link or URL that allows access to specific data or content on the Internet.

[0937] "Recipient" refers to the person or group that receives the link to the content that a User wishes to share.

[0938] To implement the present invention, the server, the terminal, and the user work together. The specific operation will be described below.

[0939] First, users register an account using a dedicated web portal or mobile app and log in. At this time, the system authenticates the user through an authentication server. After logging in, users select photos and videos of the deceased from their device and upload them to the system. They can also add comments and person tags when uploading.

[0940] The server receives the uploaded data via HTTP requests and stores it in cloud storage (e.g., Amazon S3). It also uses a metadata analysis library such as ExifTool to analyze the metadata of the received data (e.g., shooting date, location information, person tags) and stores it in a database.

[0941] The server then categorizes the data based on timeline and themes. Specifically, it retrieves metadata from the database using SQL queries and applies classification algorithms. It then uses facial recognition technology (e.g., OpenCV or AWS Rekognition) to detect faces in each image or video and extract data related to specific people. This information is then stored in a database.

[0942] The device uses an emotion engine, such as the Microsoft Azure Emotion API, to recognize the user's emotions. While the user is creating memorial content, the device analyzes video camera and microphone data in real time. This emotional information is then sent to the server.

[0943] Based on the organized data and emotional information, the server automatically generates a memorial slideshow, video, or album. Specifically, it uses personalized audio generation software (e.g., AIVA) and text generation models (e.g., GPT-3) to generate background music and comments. For example, if the user's emotion is sadness, quiet, relaxing music is added, and if the user expresses gratitude, warm comments are inserted.

[0944] Here are some examples of prompts:

[0945] "Select appropriate music based on the user's facial expressions and voice analysis."

[0946] "Generate text comments according to emotional information."

[0947] Finally, the server generates a link for sharing the memorial content and sets specific access permissions. This link is sent to the recipients specified by the user using an email sending API (e.g., SendGrid) or a social networking API, allowing users to easily share memories of the deceased with attendees, relatives, and friends.

[0948] This system reduces the burden on users and enables the creation and efficient sharing of moving and personalized memorial content.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1:

[0951] A user opens a dedicated web portal or mobile app, registers an account, and logs in. As input, the user enters their email address and password, and as output, the authentication server returns the authentication result. Specifically, the user is authenticated via the authentication API.

[0952] Step 2:

[0953] After logging in, the user selects photos and videos of the deceased from their device and uploads them to the system. The input is the photo or video file selected by the user, and the output is the data sent to the server. Specifically, the web portal or mobile app uses a file selection button, and the data is uploaded to the server via an HTTP request.

[0954] Step 3:

[0955] The server receives the uploaded data and stores it in cloud storage. The input is the photo and video data received by the server, and the output is the file stored in cloud storage. The specific operation is to store the data in a cloud storage service such as Amazon S3.

[0956] Step 4:

[0957] The server uses a metadata analysis library such as ExifTool to analyze the metadata of the uploaded data and store it in a database. The uploaded photo or video file is used as input, and the analyzed metadata is used as output and stored in the database. Specifically, the metadata analysis library is called to extract information and then stored in the database using SQL queries.

[0958] Step 5:

[0959] The server retrieves metadata from the database and categorizes the data based on timeline and themes. The input is the retrieved metadata, and the output is the categorized data. Specifically, it retrieves the metadata using SQL queries and applies classification algorithms to organize the data.

[0960] Step 6:

[0961] The server uses facial recognition technology (such as OpenCV or AWS Rekognition) to detect faces in images and videos and extract data related to specific people. Uploaded images and video files are used as input, and recognized face data is used as output, which is then stored in a database. Specifically, the server calls a facial recognition API to perform face detection and stores the results in a database.

[0962] Step 7:

[0963] The device recognizes the user's emotions using an emotion engine (such as the Microsoft Azure Emotion API). It receives real-time data from a video camera and microphone as input, and analyzes emotional data as output. Specifically, it analyzes the user's facial expressions and tone of voice, and sends the information to a server.

[0964] Step 8:

[0965] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information. The input is the organized data and emotional information, and the generated memorial content is obtained as the output. Specifically, the system uses personalized audio generation software (such as AIVA) and text generation models (such as GPT-3).

[0966] Step 9:

[0967] The server generates a link for sharing the generated memorial content and sets specific access permissions. The input is the generated memorial content, and the output is the generated link. Specifically, the server uses the cloud storage's shared link function or API to generate the link and set access permissions.

[0968] Step 10:

[0969] The server sends the generated link to the specified recipient via email or social media. The input is the recipient information specified by the user and the generated link, and the output is the link sent to the recipient. Specific operations use an email sending API (such as SendGrid) or a social media API.

[0970] (Application example 2)

[0971] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0972] Conventional memorial content creation systems generate content without considering the user's emotional state, making it difficult to provide a personalized experience that reflects individual emotions. Furthermore, they are unable to respond to the emotions of event participants in real time, making it difficult to provide appropriate content that reflects their emotions at memorial events.

[0973] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0974] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, means for sending the link to a specified recipient, means for analyzing the user's emotional state, and means for displaying memorial content according to the analyzed emotional information, thereby enabling the generation and display of personalized memorial content in real time in accordance with the emotions of participants.

[0975] "Cloud storage" is a system for storing and managing data on remote servers via the Internet.

[0976] "Metadata" refers to information about the data itself, and examples include the date it was taken, location information, and person tags.

[0977] "Facial recognition technology" is a technology for detecting the faces of people in images or videos and identifying specific people.

[0978] "Memorial content" is a moving combination of photos, video, music, and text used to remember a deceased person.

[0979] A "link" is an online generated URL that allows access to particular content.

[0980] "Emotional state" refers to the user's current feelings and sensations, including sadness, gratitude, joy, and the like.

[0981] "Analysis" is the process of examining data or information in detail and extracting specific features or patterns.

[0982] "Personalization" refers to customizing content and experiences to suit the characteristics and emotions of individual users.

[0983] A "time series" refers to a series of data or events arranged in chronological order.

[0984] A "theme" refers to a category or topic classified based on a certain content or subject.

[0985] A "database" is a system for efficiently storing, searching, and managing data, and is a collection of structured data.

[0986] "Analyzed emotion information" refers to specific emotional information of a user that has been analyzed using the user's emotion engine.

[0987] To implement this invention, the server, terminal, and user work together. First, the user logs in through a dedicated web portal or mobile app, selects photos and videos of the deceased, and uploads them to the system. The user can also add comments and person tags as needed.

[0988] The server stores the data uploaded by users in cloud storage. It analyzes the metadata of the stored data (photography date, location information, person tags, etc.) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[0989] The server then retrieves the data from the cloud storage and categorizes it by timeline and theme based on the metadata. It also uses facial recognition technology to detect people in each image and video, extracting data related to a specific person (the deceased). This allows data organization to be focused on the deceased.

[0990] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The emotion engine is used to capture the user's feelings when creating memorial content. Based on the analyzed emotion information, the user's device generates memorial content with appropriate background music and text comments.

[0991] The server automatically generates memorial content in real time based on the organized data and user emotion information acquired from the emotion engine. The generated content is displayed to participants using smart glasses, for example.

[0992] The hardware used includes a client-server network system and smart glasses, and the software used includes cloud storage services (e.g., Amazon S3), face recognition technology (e.g., OpenCV), an emotion engine (EmotionEngine), a content generation module (MediaGenerator), and a display management module (DisplayManager).

[0993] As a concrete example, consider a scenario at a memorial event. As participants wear smart glasses and view photos of the deceased, the emotion engine analyzes the user's emotional state. If the system determines that the participant is sad, it displays a remembrance message about the deceased accompanied by quiet, soothing music.

[0994] An example of a prompt is as follows:

[0995] The user is feeling sad when looking at a photo of the deceased. Generate memorial content that matches this emotion (e.g., calming music and a message).

[0996] As described above, the present invention makes it possible to provide personalized memorial content in real time according to the user's emotions.

[0997] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0998] Step 1:

[0999] Users log in via a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system. The input is the photos and videos selected by the user, and the output is the data uploaded to the server. Specifically, the user presses the file selection button, and then presses the upload button after selection to send the data to the server.

[1000] Step 2:

[1001] The server stores the uploaded data in cloud storage. The input is the data uploaded by the user, and the output is the data stored in cloud storage. Specifically, the data is stored in a cloud storage service (e.g., Amazon S3) via an API.

[1002] Step 3:

[1003] The server analyzes the metadata of the stored data and stores it in a database. The input is the data stored in cloud storage, and the output is the metadata stored in the database. Specifically, the server extracts the shooting date, location information, and person tags from the EXIF ​​information of the image file and stores them in the database.

[1004] Step 4:

[1005] The server retrieves data from cloud storage and classifies it according to time series or theme based on metadata. The input is the data stored in cloud storage and its metadata, and the output is the classified data. Specific operations include sorting the data by the date it was taken, or separating the data by theme, such as "family trip" or "birthday party."

[1006] Step 5:

[1007] The server uses facial recognition technology to detect people in each image or video and extract data related to the specific person (deceased person). The input is classified data, and the output is data related to the specific person. Specifically, it uses OpenCV to perform facial recognition and filters the data based on specific facial features.

[1008] Step 6:

[1009] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The input is the user's facial expressions, tone of voice, and text input, and the output is the user's emotional information. Specifically, the Emotion Engine is used to analyze video and audio data from the user's device in real time.

[1010] Step 7:

[1011] The server generates memorial content from the organized data based on the user's emotional information obtained from the emotion engine. The input is the user's emotional information and the organized data, and the output is personalized memorial content. Specifically, it uses a MediaGenerator to generate content with background music and text comments added according to the user's emotions.

[1012] Step 8:

[1013] The server generates a link for sharing the generated memorial content online. The input is the generated memorial content, and the output is a sharing link. Specific operations include generating a URL and setting specific access permissions.

[1014] Step 9:

[1015] The server sends the generated link to the specified recipient. The input is the shared link and the specified recipient's contact information, and the output is the link sent to the recipient. The specific operation is to call an API to send the link via email or social media.

[1016] Step 10:

[1017] The user's device displays memorial content to the participants via smart glasses. The input is the participant's emotional information and memorial content, and the output is personalized memorial content displayed on the smart glasses. Specifically, the content is displayed on the smart glasses' display using DisplayManager.

[1018] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

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

[1021] [Fourth embodiment]

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

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

[1024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1025] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[1027] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1029] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1030] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1031] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[1035] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[1036] Data Collection Phase

[1037] User upload of data

[1038] Users use a dedicated web portal or mobile app to select and upload photos and videos of the deceased, with the option to add comments and tags.

[1039] Receiving and storing data by the server

[1040] The server receives data uploaded by users and stores it in cloud storage. At this time, it analyzes the metadata of each file (e.g., shooting date, location information, person tags) and stores this information in a database. This enables efficient classification in the subsequent data organization phase.

[1041] Data reduction phase

[1042] Classification by Server

[1043] The server retrieves data from cloud storage and organizes it chronologically or by theme based on metadata, for example, sorting photos by date taken or event, or grouping photos and videos that feature a particular person.

[1044] Application of facial recognition technology

[1045] The server uses facial recognition technology to analyze and identify the faces of people in each image or video, extracting data related to that specific person and grouping them separately. This process facilitates the creation of memorial content centered around the deceased.

[1046] Content Generation Phase

[1047] Automatic generation of memorial content

[1048] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[1049] Delivery Phase

[1050] Preparing content for distribution

[1051] The server will generate a link to share the generated memorial content online, which will be set up for easy access by users.

[1052] Send to recipient

[1053] The server then sends the generated link to the recipients specified by the user via email or social media, allowing many people to share memories of the deceased.

[1054] Consider the following scenario: When a user uploads photos and videos of a deceased person, the server stores them in cloud storage, analyzes the metadata, and saves them in a database. The server then organizes the data based on the date it was taken or a specific theme (e.g., "family vacation" or "birthday party"). Using facial recognition technology, it extracts images and videos related to a specific person (the deceased), and automatically generates a memorial slideshow or video from these. This content can then be shared with attendees, relatives, and friends via an online link.

[1055] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and are then shown a screen for uploading this data. Users select the files and click the upload button.

[1059] Step 2:

[1060] The device sends the photos and videos selected by the user to the specified cloud storage. Once the upload is complete, it notifies the server of the relevant information (e.g., file name and file size).

[1061] Step 3:

[1062] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is stored in a database.

[1063] Step 4:

[1064] The server uses the metadata stored in the database to categorize uploaded photos and videos by timeline or theme, for example, sorting them by the date they were taken, or grouping them by theme, such as "family vacation" or "birthday party."

[1065] Step 5:

[1066] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts images and videos related to a specific person (the deceased). This allows for organization of the images and videos with a focus on the deceased.

[1067] Step 6:

[1068] The server automatically generates memorial slideshows, videos, and albums based on the organized data, adding background music and text comments to create moving content.

[1069] Step 7:

[1070] The server generates a link for sharing the generated memorial content online, which can have specific access permissions and can be configured to make it easy for users to share.

[1071] Step 8:

[1072] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[1073] This process allows for quick and efficient organization and sharing of photos and videos of the deceased.

[1074] Example 1

[1075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1076] In recent years, commemorating the deceased and sharing memories through digital media has become widespread, but many users spend a great deal of time and effort organizing vast amounts of data and creating content. Furthermore, manual classification, facial recognition, and other processes require specialized knowledge, making them a time-consuming process for many. Furthermore, the lack of a consistent system for efficiently performing these tasks increases the burden on users. The purpose of this invention is to solve these problems and provide a system that allows users to easily create and share content for commemorating the deceased.

[1077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1078] In this invention, the server includes means for users to upload data, means for saving the data in cloud storage, means for analyzing the metadata of the uploaded data and saving it in a database, means for classifying the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient. This allows users to easily upload data, and the server automatically organizes, classifies, creates, and shares the data, making it possible to provide efficient and moving memorial content.

[1079] "User data uploading means" refers to a means by which a user can use a dedicated web portal or mobile app to select photos and videos of the deceased and send that data to the server.

[1080] "Means for storing data in cloud storage" refers to a means by which a server receives data uploaded by a user and stores it in a distributed data storage system.

[1081] "Means for analyzing the metadata of uploaded data and storing it in a database" refers to a means for analyzing the metadata (e.g., shooting date, location information, person tag) of the data received by the server and storing this information in a relational database.

[1082] "Means for classifying data based on timeline or theme" refers to a means by which the server acquires data based on metadata and organizes the data according to a specific time or specific theme (e.g., photo date, event name).

[1083] "Means for extracting data related to a specific person using facial recognition technology" refers to a means in which a server uses facial recognition technology to analyze faces contained in images or videos, and automatically identify and extract data related to a specific person.

[1084] "Means for automatically generating memorial content from organized data" refers to a means by which a server automatically creates memorial content such as slideshows, videos, and albums based on organized data.

[1085] The "means for generating a link to share the generated content online" is a means for a server to host the generated memorial content and create a URL link to access the content.

[1086] The "means for sending the link to the designated recipient" refers to a means for sending the link generated by the server to the recipient's email address or SNS account designated by the user.

[1087] To implement the present invention, three parties, namely, a server, a terminal, and a user, work together. The specific operation will be described below.

[1088] First, users use a dedicated web portal or mobile app to select photos and videos of the deceased and upload them to the server. The dedicated web portal is built using front-end frameworks such as ReactJS and Angular, while the mobile app uses Flutter and React Native. Users can add comments and tags as needed. For example, they can add event tags such as "family trip" or "birthday party."

[1089] The server receives the uploaded data and stores it in cloud storage, such as Google Cloud Storage or Amazon S3. The server analyzes the stored data and extracts metadata for each file (e.g., photo date, location information, person tags). The metadata is extracted using Python scripts and libraries such as ExifTool. The extracted metadata is stored in a relational database such as MySQL or PostgreSQL.

[1090] The server then retrieves the stored data from the cloud storage and categorizes it based on time series or themes. This categorization can be done using the Python Pandas library. For example, photos and videos can be organized based on the date they were taken or the name of an event. In particular, categorization based on themes such as "family vacation" or "birthday party" is performed.

[1091] The server then uses facial recognition technology to extract data related to specific individuals. This process utilizes Google Vision API and Amazon Rekognition. Facial recognition technology identifies specific individuals in images and videos and extracts related data. For example, it extracts all images and videos containing the face of a deceased person and groups them separately. This process is important for easily generating memorial content centered around the deceased.

[1092] The server then automatically generates memorial slideshows, videos, and albums based on the organized data, editing the content using image processing software (e.g., FFmpeg or OpenCV), adding background music and text comments, and rendering the automatically generated content into its final format and saving it to cloud storage.

[1093] The server then generates a link for sharing the memorial online. This link can be provided as a URL shortener or a dedicated portal page. The prepared link is then sent to the recipient specified by the user via email or social media. This is done using the mail server SendGrid or Amazon SES, and also using the APIs of social media.

[1094] For example, when a user uploads a photo or video of a deceased person, the following prompt text could be used:

[1095] "Upload photos and videos from the deceased's family vacation and create a touching slideshow."

[1096] In this way, the present invention realizes a system that provides efficient and moving memorial content by allowing users to easily upload data and having the server automatically organize, classify, generate content, and even share the data.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] Program processing steps

[1099] Step 1:

[1100] User upload of data

[1101] Specific behavior:

[1102] Users log in to a dedicated web portal or mobile app.

[1103] Users click the "Select Photo" button and select photos or videos of the deceased from their device.

[1104] After making your selection, press the "Upload" button to send the data to the server.

[1105] Input: User-selected photo and video files, comments and tags.

[1106] Output: The uploaded data sent to the server.

[1107] Step 2:

[1108] Receiving and storing data by the server

[1109] Specific behavior:

[1110] The server receives data uploaded by users.

[1111] Save the received files to cloud storage (such as Google Cloud Storage or Amazon S3).

[1112] At the same time, a script is run to analyze the metadata of the received file.

[1113] Input: User-submitted photos and videos, along with accompanying metadata.

[1114] Output: Files stored in cloud storage and parsed metadata.

[1115] Step 3:

[1116] Server parsing and storing metadata

[1117] Specific behavior:

[1118] The analysis script extracts metadata (Exif information, user-added tags) for each file.

[1119] Store the extracted metadata in a relational database (e.g., MySQL or PostgreSQL).

[1120] Input: A file stored in cloud storage.

[1121] Output: Metadata stored in a database.

[1122] Step 4:

[1123] Data classification by server

[1124] Specific behavior:

[1125] The server retrieves the data from the cloud storage.

[1126] Based on the data obtained, we use Python's Pandas library to classify the data based on time series and themes.

[1127] The classification results are stored in a database.

[1128] Input: Metadata stored in a database.

[1129] Output: Data categorized by theme.

[1130] Step 5:

[1131] Extracting data of specific individuals using facial recognition technology

[1132] Specific behavior:

[1133] The server calls the Google Vision API or Amazon Rekognition.

[1134] Use the API to detect faces in images and videos.

[1135] Identify specific people (deceased) and extract and group relevant data into a new dataset.

[1136] Input: Data retrieved from cloud storage.

[1137] Output: A group of data related to a particular person.

[1138] Step 6:

[1139] Automatic generation of memorial content

[1140] Specific behavior:

[1141] Based on the organized data, the server uses image processing software (FFmpeg or OpenCV) to create slideshows and video templates.

[1142] Automatically add background music and text comments and render to final format.

[1143] Input: Classified data and data relating to specific people.

[1144] Output: Auto-generated slideshows and videos.

[1145] Step 7:

[1146] Generated content is linked and ready to share

[1147] Specific behavior:

[1148] The server stores the generated content in cloud storage.

[1149] Generate links to share online, create URL shorteners and dedicated portal pages.

[1150] Input: Auto-generated memorial content.

[1151] Output: A shareable link.

[1152] Step 8:

[1153] Send link to designated recipient

[1154] Specific behavior:

[1155] The server retrieves contact information for the recipient specified by the user.

[1156] Share the link using a mail server (SendGrid or Amazon SES) or SNS API.

[1157] Log the sending status and send a confirmation email to the user.

[1158] Input: Share link and recipient's contact information.

[1159] Output: Link sent to recipient and confirmation email to user.

[1160] (Application example 1)

[1161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1162] In today's world, delivery services play an important role in the logistics industry, but there is a growing demand for improved delivery efficiency and customer satisfaction. In particular, there are many challenges in efficiently reaching multiple delivery destinations for delivery personnel and providing services tailored to the characteristics of each customer. Furthermore, there is a lack of effective methods for utilizing data such as accurate location information of delivery destinations and facial recognition of customers. To address these challenges, efficient delivery support through delivery route optimization and data analysis is required.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1164] In this invention, the server includes a means for uploading data to cloud storage, a means for analyzing metadata of the uploaded data and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes, which enables delivery personnel to reach multiple delivery destinations quickly and efficiently, improving customer satisfaction.

[1165] "Cloud storage" is an online storage system for storing and managing data via the Internet.

[1166] "Data upload" is the process of sending and storing data from a local device to cloud storage.

[1167] "Metadata" is attribute information of the data itself, and includes, for example, the date of shooting, location information, person tags, and the like.

[1168] A "database" is a structured collection of data that allows for efficient management and manipulation of data.

[1169] "Time series" is a method of organizing and classifying data along the flow of time.

[1170] A "theme" is a concept that organizes and classifies data based on a specific content or topic.

[1171] "Facial recognition technology" is a technology that detects and identifies the faces of people contained in images and videos.

[1172] "Data extraction" is the process of extracting the necessary information from data.

[1173] "Automatic content generation" means mechanically creating content based on input data.

[1174] An "online sharing link" is a URL link that allows access to content over the Internet.

[1175] "Location information of delivery destination" is data indicating the geographical location of the delivery destination.

[1176] "Delivery route calculation" is the process of calculating the optimal route to efficiently reach multiple delivery destinations.

[1177] To implement this invention, a server, a terminal, and a user mainly work together. The server includes a means for uploading data to cloud storage, a means for analyzing metadata and storing it in a database, and a means for analyzing location information of delivery destinations and calculating efficient delivery routes. The terminal includes a smartphone or other mobile device, which a user uses to upload photos and notes of delivery destinations.

[1178] Data Collection Phase

[1179] Users take photos of delivery locations using their smartphones and upload them through a dedicated mobile app. Users can add comments and tags (for example, specific customer needs or points of caution) as needed. The server receives the uploaded photos and stores them in cloud storage.

[1180] Metadata Analysis Phase

[1181] The server analyzes the uploaded photo's metadata, which includes the date it was taken, location information, person tags, etc. This information is stored in a database and used later in the data organization and delivery route calculation phases.

[1182] Data reduction phase

[1183] The server retrieves data from the cloud storage and categorizes the photos based on the collected metadata, for example, by timeline or theme (e.g., "office area," "residential area"), and can also use facial recognition technology to extract and categorize photos related to specific customers.

[1184] Delivery route calculation phase

[1185] The server then uses the organized data and location information to efficiently calculate delivery routes, taking into account distances between specific destinations and traffic conditions. The results are then sent back to the driver's device and displayed.

[1186] Content Generation Phase

[1187] The server uses this organized data to automatically generate delivery schedules and service proposals tailored to each customer's characteristics, using AI technology to take into account the customer's past purchase history and feedback.

[1188] Example

[1189] For example, when a user uploads a "photo of a delivery destination," the server stores it in cloud storage, analyzes the metadata, and saves it in a database. The server then organizes the photos based on the date and location of the photo, and uses facial recognition technology to extract data related to specific customers. Finally, the server calculates an efficient delivery route and notifies the delivery person. This process improves delivery efficiency and customer satisfaction.

[1190] Prompt Sentence Examples

[1191] Prompt: Calculate delivery route by facial recognition and geolocation analysis of delivery destination photos

[1192] Data upload: ['photo1.jpg', 'photo2.jpg']

[1193] User ID: '12345'

[1194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1195] Step 1:

[1196] Users take photos of delivery destinations using their smartphones and upload these photos to the server through a dedicated mobile app. The photo data of the delivery destination is provided as input, and is saved on the server as output. Specifically, when the user presses the "upload" button on the app, the photos and related notes (comments and tags) are attached.

[1197] Step 2:

[1198] The server receives uploaded photos and metadata (photo date, location information, person tags) and stores them in cloud storage. The input is the photo data and metadata uploaded by the user, and the output is the data stored in cloud storage. The server verifies the received data, converts it into the required format, and stores it.

[1199] Step 3:

[1200] The server analyzes the metadata from the data on the cloud storage and stores it in a database. In this process, it uses facial recognition technology to analyze the faces of people in the photos. The photo data and metadata from the cloud storage are used as input, and the analyzed metadata is stored in the database as output. Specifically, the server runs a facial recognition algorithm to identify the faces of people in the photos.

[1201] Step 4:

[1202] The server uses the analyzed metadata to classify photos based on time sequence and theme (e.g., "office area," "residential area"). Using the analyzed metadata as input, the server obtains organized photo data as output. Specifically, the server groups the data based on the time of shooting and location information, and then categorizes them into appropriate categories.

[1203] Step 5:

[1204] The server uses the organized data to analyze the location information of the delivery destinations and calculates an efficient delivery route. The organized data including location information is used as input, and the delivery route is obtained as output. The server uses a specific algorithm (e.g., Dijkstra's algorithm) to calculate the optimal order of delivery destinations.

[1205] Step 6:

[1206] The server sends the calculated delivery route information to the device (smartphone) and displays delivery instructions to the user. The calculated delivery route information is used as input, and delivery instructions are obtained as output and displayed on the device. Specifically, the server converts the delivery route information into JSON format and sends it to the device app.

[1207] Step 7:

[1208] Once the delivery is complete, the user uses the device to confirm the delivery completion and, if necessary, send feedback to the server. The completion report and feedback data are provided as input, and the output is saved in cloud storage. Specifically, the user presses the "Delivery Complete" button to send the completion report to the server.

[1209] This process creates a system that provides efficient delivery routes and supports the work of delivery personnel.

[1210] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1211] To implement this invention, the server, the terminal, and the user work together. One of the features of this invention is that it incorporates an emotion engine that recognizes the user's emotions and reflects them in the generation of memorial content. The specific operation of this engine is described below.

[1212] Data Collection Phase

[1213] User upload of data

[1214] Users log in through a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system, adding comments and person tags as needed.

[1215] Receiving and storing data by the server

[1216] The server stores the data uploaded by users in cloud storage. It also analyzes the metadata of each file (such as the date of shooting, location information, and person tags) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[1217] Data reduction phase

[1218] Classification by Server

[1219] The server retrieves the data from the cloud storage and categorizes it based on the metadata, such as by the date the photos were taken, or by themes like "family vacation" or "birthday party."

[1220] Application of facial recognition technology

[1221] The server uses facial recognition technology to detect the faces of people in each image and video. It then analyzes the features of the detected faces and extracts data related to the specific person (deceased person). This allows data organization centered on the deceased person.

[1222] Applying the Emotion Engine

[1223] Recognizing user emotions

[1224] The device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input, and is used to capture the user's feelings when creating memorial content.

[1225] Content Generation Phase

[1226] Automatic generation of memorial content

[1227] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information obtained from the emotion engine. By adding appropriate background music and text comments according to the emotional information, more moving content can be created.

[1228] For example, when a user creates memorial content, the server analyzes the user's facial expressions and voice in real time to recognize feelings of sadness or gratitude. If the user feels sad, the server adds quiet, relaxing music, and if the user expresses gratitude, it inserts a warm comment.

[1229] Delivery Phase

[1230] Preparing content for distribution

[1231] The server generates a link for sharing the generated memorial content online, with specific access permissions configured to make it easy for users to share.

[1232] Send to recipient

[1233] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[1234] This process allows for fast and efficient organization and sharing of photos and videos of the deceased, while providing personalized memorial content tailored to the user's emotions.

[1235] The processing flow will be explained below.

[1236] Step 1:

[1237] Users log in to a dedicated web portal or mobile app, select photos and videos of the deceased, and begin uploading. On the upload screen, select the files and add comments and tags as needed.

[1238] Step 2:

[1239] The device sends the photos and videos specified by the user to cloud storage, and once the transfer is complete, it notifies the server of the relevant metadata (file name, file size, etc.).

[1240] Step 3:

[1241] The server receives the data uploaded to the cloud storage and analyzes the metadata of each file (such as the date of shooting, location information, and person tags). The analyzed metadata is then stored in a database.

[1242] Step 4:

[1243] The server uses the metadata in the database to categorize the uploaded photos and videos by timeline or theme, for example, by a specific theme such as "family vacation" or "birthday party."

[1244] Step 5:

[1245] The server uses facial recognition technology to detect human faces in each image or video, analyzes the detected facial features, extracts data related to a specific person (deceased person), and groups them separately.

[1246] Step 6:

[1247] The device uses an emotion engine to recognize the user's emotions in real time, analyzing changes in facial expressions, tone of voice, and text input to determine the user's emotional state (e.g., sadness, gratitude, etc.).

[1248] Step 7:

[1249] The server automatically generates memorial slideshows, videos, and albums based on the collected data and the emotional information obtained using an emotion engine. For example, if the user is feeling sad, it will add calm and quiet background music, and if the user is expressing gratitude, it will insert warm comments.

[1250] Step 8:

[1251] The server generates a link to share the generated memorial content online, which can have specific access permissions and can be easily accessed by those with whom the link is shared.

[1252] Step 9:

[1253] The server then sends the generated link to the recipient specified by the user via email or social media, allowing users to efficiently share memories of the deceased with attendees, relatives, and friends.

[1254] The system efficiently organizes photos and videos of the deceased, automatically generates emotionally sensitive memorial content, and allows people to share their memories in a moving way.

[1255] Example 2

[1256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1257] Currently, creating memorial content requires users to manually select and edit photos and videos, which requires a great deal of time and effort. Furthermore, existing systems do not personalize content based on the user's emotions, making it difficult to create moving memorial content. Furthermore, there is a lack of efficient ways to share the generated content. A system that can solve these issues is needed.

[1258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1259] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for recognizing a user's emotions, means for automatically generating memorial content based on the organized data and emotional information, means for generating a link for sharing the generated content online, and means for sending the link to a specified recipient, thereby reducing the burden on the user and enabling the generation and sharing of moving and personalized memorial content.

[1260] "Cloud storage" is a storage system built on remote servers where data can be stored and accessed over the Internet.

[1261] "Data upload" is the operation of sending files such as photos and videos from a user's device to a remote server.

[1262] "Metadata" refers to file attribute information, and includes, for example, the date of shooting, location information, and person tags.

[1263] A "database" is a system that stores data systematically and enables fast and efficient retrieval, storage, and updating.

[1264] "Chronological" refers to a method of arranging data in chronological order, either from most recent to oldest.

[1265] A "theme" is a criterion for classifying data into a specific subject or category, examples of which include "family vacation" or "birthday party."

[1266] "Facial recognition technology" is a technology that detects and individually identifies human faces in images and videos.

[1267] "Emotion recognition" is the process of analyzing and identifying a user's emotional state from facial expressions, tone of voice, and text input.

[1268] "Memorial content" refers to digital media such as slideshows, videos, and albums created to commemorate and share memories of the deceased.

[1269] "Link" refers to a hypertext link or URL that allows access to specific data or content on the Internet.

[1270] "Recipient" refers to the person or group that receives the link to the content that a User wishes to share.

[1271] To implement the present invention, the server, the terminal, and the user work together. The specific operation will be described below.

[1272] First, users register an account using a dedicated web portal or mobile app and log in. At this time, the system authenticates the user through an authentication server. After logging in, users select photos and videos of the deceased from their device and upload them to the system. They can also add comments and person tags when uploading.

[1273] The server receives the uploaded data via HTTP requests and stores it in cloud storage (e.g., Amazon S3). It also uses a metadata analysis library such as ExifTool to analyze the metadata of the received data (e.g., shooting date, location information, person tags) and stores it in a database.

[1274] The server then categorizes the data based on timeline and themes. Specifically, it retrieves metadata from the database using SQL queries and applies classification algorithms. It then uses facial recognition technology (e.g., OpenCV or AWS Rekognition) to detect faces in each image or video and extract data related to specific people. This information is then stored in a database.

[1275] The device uses an emotion engine, such as the Microsoft Azure Emotion API, to recognize the user's emotions. While the user is creating memorial content, the device analyzes video camera and microphone data in real time. This emotional information is then sent to the server.

[1276] Based on the organized data and emotional information, the server automatically generates a memorial slideshow, video, or album. Specifically, it uses personalized audio generation software (e.g., AIVA) and text generation models (e.g., GPT-3) to generate background music and comments. For example, if the user's emotion is sadness, quiet, relaxing music is added, and if the user expresses gratitude, warm comments are inserted.

[1277] Here are some examples of prompts:

[1278] "Select appropriate music based on the user's facial expressions and voice analysis."

[1279] "Generate text comments according to emotional information."

[1280] Finally, the server generates a link for sharing the memorial content and sets specific access permissions. This link is sent to the recipients specified by the user using an email sending API (e.g., SendGrid) or a social networking API, allowing users to easily share memories of the deceased with attendees, relatives, and friends.

[1281] This system reduces the burden on users and enables the creation and efficient sharing of moving and personalized memorial content.

[1282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] A user opens a dedicated web portal or mobile app, registers an account, and logs in. As input, the user enters their email address and password, and as output, the authentication server returns the authentication result. Specifically, the user is authenticated via the authentication API.

[1285] Step 2:

[1286] After logging in, the user selects photos and videos of the deceased from their device and uploads them to the system. The input is the photo or video file selected by the user, and the output is the data sent to the server. Specifically, the web portal or mobile app uses a file selection button, and the data is uploaded to the server via an HTTP request.

[1287] Step 3:

[1288] The server receives the uploaded data and stores it in cloud storage. The input is the photo and video data received by the server, and the output is the file stored in cloud storage. The specific operation is to store the data in a cloud storage service such as Amazon S3.

[1289] Step 4:

[1290] The server uses a metadata analysis library such as ExifTool to analyze the metadata of the uploaded data and store it in a database. The uploaded photo or video file is used as input, and the analyzed metadata is used as output and stored in the database. Specifically, the metadata analysis library is called to extract information and then stored in the database using SQL queries.

[1291] Step 5:

[1292] The server retrieves metadata from the database and categorizes the data based on timeline and themes. The input is the retrieved metadata, and the output is the categorized data. Specifically, it retrieves the metadata using SQL queries and applies classification algorithms to organize the data.

[1293] Step 6:

[1294] The server uses facial recognition technology (such as OpenCV or AWS Rekognition) to detect faces in images and videos and extract data related to specific people. Uploaded images and video files are used as input, and recognized face data is used as output, which is then stored in a database. Specifically, the server calls a facial recognition API to perform face detection and stores the results in a database.

[1295] Step 7:

[1296] The device recognizes the user's emotions using an emotion engine (such as the Microsoft Azure Emotion API). It receives real-time data from a video camera and microphone as input, and analyzes emotional data as output. Specifically, it analyzes the user's facial expressions and tone of voice, and sends the information to a server.

[1297] Step 8:

[1298] The server automatically generates memorial slideshows, videos, and albums based on the organized data and the user's emotional information. The input is the organized data and emotional information, and the generated memorial content is obtained as the output. Specifically, the system uses personalized audio generation software (such as AIVA) and text generation models (such as GPT-3).

[1299] Step 9:

[1300] The server generates a link for sharing the generated memorial content and sets specific access permissions. The input is the generated memorial content, and the output is the generated link. Specifically, the server uses the cloud storage's shared link function or API to generate the link and set access permissions.

[1301] Step 10:

[1302] The server sends the generated link to the specified recipient via email or social media. The input is the recipient information specified by the user and the generated link, and the output is the link sent to the recipient. Specific operations use an email sending API (such as SendGrid) or a social media API.

[1303] (Application example 2)

[1304] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1305] Conventional memorial content creation systems generate content without considering the user's emotional state, making it difficult to provide a personalized experience that reflects individual emotions. Furthermore, they are unable to respond to the emotions of event participants in real time, making it difficult to provide appropriate content that reflects their emotions at memorial events.

[1306] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1307] In this invention, the server includes means for uploading data to cloud storage, means for analyzing metadata of the uploaded data and storing it in a database, means for categorizing the data based on time series and themes, means for extracting data related to a specific person using facial recognition technology, means for automatically generating memorial content from the organized data, means for generating a link for sharing the generated content online, means for sending the link to a specified recipient, means for analyzing the user's emotional state, and means for displaying memorial content according to the analyzed emotional information, thereby enabling the generation and display of personalized memorial content in real time in accordance with the emotions of participants.

[1308] "Cloud storage" is a system for storing and managing data on remote servers via the Internet.

[1309] "Metadata" refers to information about the data itself, and examples include the date it was taken, location information, and person tags.

[1310] "Facial recognition technology" is a technology for detecting the faces of people in images or videos and identifying specific people.

[1311] "Memorial content" is a moving combination of photos, video, music, and text used to remember a deceased person.

[1312] A "link" is an online generated URL that allows access to particular content.

[1313] "Emotional state" refers to the user's current feelings and sensations, including sadness, gratitude, joy, and the like.

[1314] "Analysis" is the process of examining data or information in detail and extracting specific features or patterns.

[1315] "Personalization" refers to customizing content and experiences to suit the characteristics and emotions of individual users.

[1316] A "time series" refers to a series of data or events arranged in chronological order.

[1317] A "theme" refers to a category or topic classified based on a certain content or subject.

[1318] A "database" is a system for efficiently storing, searching, and managing data, and is a collection of structured data.

[1319] "Analyzed emotion information" refers to specific emotional information of a user that has been analyzed using the user's emotion engine.

[1320] To implement this invention, the server, terminal, and user work together. First, the user logs in through a dedicated web portal or mobile app, selects photos and videos of the deceased, and uploads them to the system. The user can also add comments and person tags as needed.

[1321] The server stores the data uploaded by users in cloud storage. It analyzes the metadata of the stored data (photography date, location information, person tags, etc.) and stores it in a database. This analyzed metadata is used in the subsequent data organization phase.

[1322] The server then retrieves the data from the cloud storage and categorizes it by timeline and theme based on the metadata. It also uses facial recognition technology to detect people in each image and video, extracting data related to a specific person (the deceased). This allows data organization to be focused on the deceased.

[1323] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The emotion engine is used to capture the user's feelings when creating memorial content. Based on the analyzed emotion information, the user's device generates memorial content with appropriate background music and text comments.

[1324] The server automatically generates memorial content in real time based on the organized data and user emotion information acquired from the emotion engine. The generated content is displayed to participants using smart glasses, for example.

[1325] The hardware used includes a client-server network system and smart glasses, and the software used includes cloud storage services (e.g., Amazon S3), face recognition technology (e.g., OpenCV), an emotion engine (EmotionEngine), a content generation module (MediaGenerator), and a display management module (DisplayManager).

[1326] As a concrete example, consider a scenario at a memorial event. As participants wear smart glasses and view photos of the deceased, the emotion engine analyzes the user's emotional state. If the system determines that the participant is sad, it displays a remembrance message about the deceased accompanied by quiet, soothing music.

[1327] An example of a prompt is as follows:

[1328] The user is feeling sad when looking at a photo of the deceased. Generate memorial content that matches this emotion (e.g., calming music and a message).

[1329] As described above, the present invention makes it possible to provide personalized memorial content in real time according to the user's emotions.

[1330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1331] Step 1:

[1332] Users log in via a dedicated web portal or mobile app, select photos and videos of the deceased, and upload them to the system. The input is the photos and videos selected by the user, and the output is the data uploaded to the server. Specifically, the user presses the file selection button, and then presses the upload button after selection to send the data to the server.

[1333] Step 2:

[1334] The server stores the uploaded data in cloud storage. The input is the data uploaded by the user, and the output is the data stored in cloud storage. Specifically, the data is stored in a cloud storage service (e.g., Amazon S3) via an API.

[1335] Step 3:

[1336] The server analyzes the metadata of the stored data and stores it in a database. The input is the data stored in cloud storage, and the output is the metadata stored in the database. Specifically, the server extracts the shooting date, location information, and person tags from the EXIF ​​information of the image file and stores them in the database.

[1337] Step 4:

[1338] The server retrieves data from cloud storage and classifies it according to time series or theme based on metadata. The input is the data stored in cloud storage and its metadata, and the output is the classified data. Specific operations include sorting the data by the date it was taken, or separating the data by theme, such as "family trip" or "birthday party."

[1339] Step 5:

[1340] The server uses facial recognition technology to detect people in each image or video and extract data related to the specific person (deceased person). The input is classified data, and the output is data related to the specific person. Specifically, it uses OpenCV to perform facial recognition and filters the data based on specific facial features.

[1341] Step 6:

[1342] The user's device recognizes the user's emotional state using an emotion engine that analyzes the user's facial expressions, tone of voice, and text input. The input is the user's facial expressions, tone of voice, and text input, and the output is the user's emotional information. Specifically, the Emotion Engine is used to analyze video and audio data from the user's device in real time.

[1343] Step 7:

[1344] The server generates memorial content from the organized data based on the user's emotional information obtained from the emotion engine. The input is the user's emotional information and the organized data, and the output is personalized memorial content. Specifically, it uses a MediaGenerator to generate content with background music and text comments added according to the user's emotions.

[1345] Step 8:

[1346] The server generates a link for sharing the generated memorial content online. The input is the generated memorial content, and the output is a sharing link. Specific operations include generating a URL and setting specific access permissions.

[1347] Step 9:

[1348] The server sends the generated link to the specified recipient. The input is the shared link and the specified recipient's contact information, and the output is the link sent to the recipient. The specific operation is to call an API to send the link via email or social media.

[1349] Step 10:

[1350] The user's device displays memorial content to the participants via smart glasses. The input is the participant's emotional information and memorial content, and the output is personalized memorial content displayed on the smart glasses. Specifically, the content is displayed on the smart glasses' display using DisplayManager.

[1351] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1352] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1353] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1354] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1355] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1356] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1357] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1358] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1359] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1360] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1361] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1362] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1363] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1364] 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.

[1365] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1366] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1367] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1368] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1369] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1370] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1371] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1372] The following is further disclosed regarding the above embodiment.

[1373] (Claim 1)

[1374] A means for uploading data to cloud storage;

[1375] A means for parsing the metadata of the uploaded data and storing it in a database;

[1376] a means of categorizing data chronologically and thematically;

[1377] A means for extracting data relating to a particular person using facial recognition technology;

[1378] A means to automatically generate memorial content from organized data;

[1379] means for generating a link for sharing the generated content online;

[1380] means for transmitting said link to a designated recipient;

[1381] A system including:

[1382] (Claim 2)

[1383] 2. The system of claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

[1384] (Claim 3)

[1385] 2. The system of claim 1, wherein the means for automatically generating memorial content generates slideshows, videos, and albums.

[1386] "Example 1"

[1387] (Claim 1)

[1388] a means for users to upload data;

[1389] A means for storing data in cloud storage;

[1390] A means for parsing the metadata of the uploaded data and storing it in a database;

[1391] a means of categorizing data chronologically and thematically;

[1392] A means for extracting data relating to a particular person using facial recognition technology;

[1393] A means to automatically generate memorial content from organized data;

[1394] means for generating a link for sharing the generated content online;

[1395] means for transmitting said link to a designated recipient;

[1396] A system including:

[1397] (Claim 2)

[1398] 2. The system of claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

[1399] (Claim 3)

[1400] 2. The system of claim 1, wherein the means for automatically generating memorial content generates slideshows, videos, and albums.

[1401] "Application Example 1"

[1402] (Claim 1)

[1403] A means for uploading data to cloud storage;

[1404] A means for parsing the metadata of the uploaded data and storing it in a database;

[1405] a means of categorizing data chronologically and thematically;

[1406] A means for extracting data relating to a particular person using facial recognition technology;

[1407] A means of automatically generating content from organized data,

[1408] means for generating a link for sharing the generated content online;

[1409] means for transmitting said link to a designated recipient;

[1410] A means for analyzing location information of delivery destinations and calculating efficient delivery routes;

[1411] A system including:

[1412] (Claim 2)

[1413] 2. The system of claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

[1414] (Claim 3)

[1415] 2. The system according to claim 1, wherein the automatically generated content generates slideshows, videos, and albums.

[1416] "Example 2: Combining Emotion Engines"

[1417] (Claim 1)

[1418] A means for uploading data to cloud storage;

[1419] A means for parsing the metadata of the uploaded data and storing it in a database;

[1420] a means of categorizing data chronologically and thematically;

[1421] A means for extracting data relating to a particular person using facial recognition technology;

[1422] means for recognizing a user's emotion;

[1423] A method for automatically generating memorial content based on organized data and emotional information, and

[1424] means for generating a link for sharing the generated content online;

[1425] means for transmitting said link to a designated recipient;

[1426] A system including:

[1427] (Claim 2)

[1428] 2. The system of claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

[1429] (Claim 3)

[1430] 2. The system of claim 1, wherein the means for automatically generating memorial content generates slideshows, videos, and albums.

[1431] "Application example 2 when combining emotion engines"

[1432] (Claim 1)

[1433] A means for uploading data to cloud storage;

[1434] A means for parsing the metadata of the uploaded data and storing it in a database;

[1435] a means of categorizing data chronologically and thematically;

[1436] A means for extracting data relating to a particular person using facial recognition technology;

[1437] A means to automatically generate memorial content from organized data;

[1438] means for generating a link for sharing the generated content online;

[1439] means for transmitting said link to a designated recipient;

[1440] means for analyzing the emotional state of a user;

[1441] a means for displaying memorial content in response to the analyzed emotional information;

[1442] A system including:

[1443] (Claim 2)

[1444] 2. The system of claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

[1445] (Claim 3)

[1446] 2. The system of claim 1, wherein the means for automatically generating memorial content generates slideshows, videos, and albums. [Explanation of symbols]

[1447] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for uploading data to cloud storage; A means for parsing the metadata of the uploaded data and storing it in a database; a means of categorizing data chronologically and thematically; A means for extracting data relating to a particular person using facial recognition technology; A means to automatically generate memorial content from organized data; means for generating a link for sharing the generated content online; means for transmitting said link to a designated recipient; A system including:

2. 2. The system according to claim 1, wherein the means for analyzing metadata from the uploaded data analyzes the date of shooting, location information, and person tags.

3. The system of claim 1, wherein the means for automatically generating memorial content generates a slideshow, a video, and an album.

Images