system

Abstract

Provide a system. A means for collecting user information; a means for obtaining real-time data; A means of generating individually optimized stories based on collected user information and real-time data; and means for delivering the generated story to a display device; a means of gathering user feedback; a means of adjusting the story generation process based on feedback; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional novel and story delivery systems provide the same content to all users, resulting in insufficient personalization based on each user's interests. Furthermore, content delivery does not take into account the user's current situation or environment (time of day, location, weather, etc.), meaning the content is not optimized for the user's daily experience. This creates the problem of a limited user experience. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] the means by which user information is collected;

[0007] a means for obtaining real-time data;

[0008] A means of generating individually optimized stories based on collected user information and real-time data; and

[0009] means for delivering the generated story to a display device;

[0010] a means of gathering user feedback;

[0011] a means of adjusting the story generation process based on feedback;

[0012] By providing a system that includes these features, it is possible to provide content that is optimized for each user's interests, concerns, current situation, and environment. Furthermore, by reflecting user responses, it is possible to continuously improve the quality of the content.

[0013] "User information" refers to basic data about an individual user, such as the user's name, age, gender, and favorite genres.

[0014] "Real-time data" refers to data that indicates the user's current situation and environment, such as the user's current location, time, and weather information.

[0015] The term "means" refers to a method, device, program, algorithm, etc. for realizing a specific function or operation.

[0016] A "story" is a form of text or narrative provided to users, and is content with a specific scenario or content.

[0017] "Generate" refers to the process of creating new data or content based on specific input information.

[0018] A "display terminal" is an electronic device on which a user can view content, including smartphones, tablets, and personal computers.

[0019] "Distribution" means transmitting the generated content to a display terminal and making it accessible to a user.

[0020] "Feedback" refers to data that indicates opinions and impressions about content, such as reactions, ratings, and comments provided by users.

[0021] A "database" is a system that stores collected information in an organized manner and enables searching and updating. [Brief explanation of the drawings]

[0022] [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 illustrating 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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[0044] System Configuration

[0045] The system includes the following major components:

[0046] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0047] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0048] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0049] System Operation Overview

[0050] 1. Collection of User Information

[0051] When a user uses the app for the first time, they enter information such as their name, age, gender, and favorite genres.

[0052] (Device) sends this information to the server.

[0053] (Server) receives the information and creates and stores the user's profile in a database.

[0054] 2. Obtaining real-time data

[0055] (Device) periodically obtains the user's location, time, and weather information.

[0056] (Device) transmits the acquired real-time data to the server.

[0057] (Server) receives real-time data and stores it in a database.

[0058] 3. Story Generation

[0059] (Device) sends a story request to the server at a specified time every day.

[0060] (Server) receives the request and retrieves the user profile and latest real-time data from the database.

[0061] (The server) uses an artificial intelligence engine to generate stories based on user profiles and real-time data.

[0062] For example, if it determines that a user likes mysteries and is on their way to work in the morning, it will generate a story themed around a mystery incident that occurs on the way to work.

[0063] (Server) sends the generated story to the device.

[0064] 4. Story Distribution and Display

[0065] (Device) notifies the user of the story received from the server.

[0066] (User) opens the app and starts reading a story.

[0067] 5. Gathering Feedback

[0068] (Device) displays an interface to the user requesting a rating and feedback on the story.

[0069] (User) enters a rating (star rating, comment) for the story.

[0070] (Device) sends user feedback to the server.

[0071] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[0072] Examples:

[0073] User: Taro Tanaka, 30 years old, male, loves mystery novels

[0074] Time: 7am, commute time

[0075] Location: Near Tokyo Station

[0076] Weather: Sunny

[0077] 1. Collection of User Information

[0078] (Taro Tanaka) enters "Taro Tanaka," "30 years old," "male," and "likes mysteries."

[0079] (Device) sends this information to the server.

[0080] (Server) receives the information and creates and stores Tanaka's profile in the database.

[0081] 2. Obtaining real-time data

[0082] (Device) periodically obtains Tanaka's current location information (near Tokyo Station) and weather information (sunny).

[0083] (Device) sends location and weather information to the server.

[0084] (Server) receives this information and stores it in a database.

[0085] 3. Story Generation

[0086] (Device) sends a story request to the server at 7am.

[0087] (Server) receives the request and retrieves Tanaka's profile and the latest real-time data.

[0088] (Server) uses an artificial intelligence engine based on the conditions "I like mysteries," "On my way to work," "Tokyo Station," and "Sunny weather" to generate a story called "A mysterious incident that takes place at Tokyo Station on a sunny morning."

[0089] (Server) sends the generated story to Tanaka's device.

[0090] 4. Story Distribution and Display

[0091] (Device) receives the story and notifies Tanaka.

[0092] (Tanaka) opens the app and reads the story.

[0093] 5. Gathering Feedback

[0094] (Device) asks Tanaka to rate and comment on the story.

[0095] (Tanaka) enters feedback such as "5 stars" and "Very interesting!"

[0096] (Device) sends feedback to the server.

[0097] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[0098] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[0099] The processing flow will be explained below.

[0100] Step 1:

[0101] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[0102] Step 2:

[0103] (Device) collects information entered by the user and sends it to the server.

[0104] Step 3:

[0105] (Server) creates a user profile based on the received user information and stores it in the database.

[0106] Step 4:

[0107] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[0108] Step 5:

[0109] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[0110] Step 6:

[0111] (Server) receives real-time data and stores it in the user's status database.

[0112] Step 7:

[0113] (Device) sends a story request to the server at a specified time every day (e.g. 7am).

[0114] Step 8:

[0115] (Server) receives the story request and retrieves the user's profile and the latest real-time data from the database.

[0116] Step 9:

[0117] The (server) launches an artificial intelligence engine and provides the user's profile (e.g., "I like mysteries") and real-time data (e.g., Tokyo Station, sunny weather) as input.

[0118] Step 10:

[0119] (Artificial Intelligence Engine) generates a story based on specified parameters (e.g., a mysterious incident that occurs at Tokyo Station on a sunny morning).

[0120] Step 11:

[0121] The server retrieves the generated story and associates it with the user's profile.

[0122] Step 12:

[0123] (Server) sends the generated story to the user's device.

[0124] Step 13:

[0125] (Device) notifies the user of stories received.

[0126] Step 14:

[0127] (User) sees the notification, opens the app and reads the story.

[0128] Step 15:

[0129] (Device) displays an interface to the user asking them to rate or comment on the story.

[0130] Step 16:

[0131] (User) enters a rating (star rating, comment) for the story.

[0132] Step 17:

[0133] (Device) sends user feedback to the server.

[0134] Step 18:

[0135] (Server) receives the feedback and stores it in a database.

[0136] Step 19:

[0137] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[0138] Example 1

[0139] 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."

[0140] Conventional story generation systems lacked sufficient personalization based on individual user interests and real-time situations, making it difficult to provide content that would capture users' attention. Furthermore, feedback was not collected or reflected efficiently, making it difficult to continuously improve the user experience. This made improving user satisfaction a challenge.

[0141] 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.

[0142] In this invention, the server includes a means for collecting user information, a means for acquiring real-time data, and a means for generating prompt sentences and generating stories using a generative AI model, thereby enabling adaptive story generation based on the user's individual interests and real-time situations.

[0143] "User Information" means data about an individual provided by a User, such as name, age, gender, and interests.

[0144] "Real-time data" refers to data relevant to the current situation, such as the user's location, time, and weather information.

[0145] "Stories" refers to texts and narratives generated based on user profiles and real-time data.

[0146] A "display terminal" is a device operated by a user, including a smartphone, tablet, or PC.

[0147] "Feedback" refers to ratings and comments on stories provided by users, and is data that is reflected in future story generation.

[0148] A "prompt" is a piece of text that is input into a generative AI model, instructing it to generate a story based on specific conditions or settings.

[0149] A "generative AI model" refers to an artificial intelligence algorithm or engine that generates a story based on an input prompt.

[0150] The present invention provides a system for collecting user information, acquiring real-time data, generating individually optimized stories based on the collected information, and providing the stories to users. Specific embodiments of this system are described below.

[0151] System Configuration

[0152] The system mainly includes the following components:

[0153] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0154] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0155] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0156] Collection of User Information

[0157] When a user uses an app for the first time, they enter user information, such as their name, age, gender, and favorite genre. The device sends this information to the server, which then receives it and creates and saves a user profile in a database.

[0158] Real-time data acquisition

[0159] The device periodically obtains the user's location, time, and weather information, allowing it to generate content appropriate to the user's current situation. The device then transmits the obtained real-time data to the server, which then receives the data and stores it in a database.

[0160] Story Generation

[0161] The device sends a story request to the server at a specified time every day. The server receives the request, retrieves the user's profile and the latest real-time data from the database, generates a prompt, and generates a story using a generative AI model.

[0162] As a concrete example, we input the following prompt into the generative AI model:

[0163] "For a 30-year-old man who likes mysteries, please generate a mystery that takes place at Tokyo Station on a sunny morning."

[0164] The server transmits the generated story to the terminal, and the terminal notifies the user of the story.

[0165] Gathering feedback

[0166] After the user finishes reading the story, the device displays an interface asking for feedback. The user enters a rating and comments, and the device sends this feedback to the server. The server receives the feedback and stores it in a database, allowing it to reflect the feedback in future story generation and improve the user experience.

[0167] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1: Collect user information

[0170] The user accesses the app and enters their name, age, gender, and favorite genre. For example, they might enter "Yamada Hanako," "25 years old," "female," and "likes romance novels."

[0171] The device sends the entered information to the server. The sent content includes the entered information along with the user ID.

[0172] Input: Information entered by the user ("Hanako Yamada," "25 years old," "female," "likes romance novels")

[0173] Data processing: Convert user information into JSON format

[0174] Output: HTTPS request to the server

[0175] The server receives the information and stores the user profile in its database, for example as "user5678".

[0176] Input: HTTPS request from the terminal

[0177] Data Calculation: Creating and Saving User Profiles

[0178] Output: New user entry in the database

[0179] Step 2: Getting real-time data

[0180] The device periodically obtains location, time, and weather information. For example, every 10 minutes, it obtains "Shinjuku Station" from the GPS, "12:30 PM" from the system clock, and "rain" from the weather API.

[0181] Input: GPS data, system clock, weather API

[0182] Data processing: Format conversion of acquired data

[0183] Output: Packets of acquired data

[0184] The real-time data acquired by the terminal is transmitted to the server.

[0185] Input: Packets of acquired data

[0186] Data processing: JSON conversion of data packets

[0187] Output: HTTPS request to the server

[0188] The server receives the data and stores it in the database under the corresponding user ID.

[0189] Input: HTTPS request from the terminal

[0190] Data calculation: Real-time data storage

[0191] Output: Real-time data entry into a database

[0192] Step 3: Story Generation

[0193] The device sends a story request to the server at a specified time each day, for example, 7:00 AM.

[0194] Input: User ID and timestamp

[0195] Data processing: Creating a request format

[0196] Output: HTTPS request to the server

[0197] The server retrieves the user's profile and the latest real-time data from the database.

[0198] Input: User ID

[0199] Data Computing: Querying user and real-time databases

[0200] Output: User profile and up-to-date real-time data

[0201] The server generates a prompt and uses a generative AI model to generate a story, for example, "Generate a rainy day romance story at Shinjuku Station for a 25-year-old woman who loves romance novels."

[0202] Input: User profile and up-to-date real-time data

[0203] Data Calculation: Prompt Statement Generation

[0204] Output: Input prompts to the generative AI model

[0205] Input: prompt to generative AI model

[0206] Data Calculation: Story Generation

[0207] Output: Generated stories

[0208] The server sends the generated story to the device.

[0209] Input: Generated Story

[0210] Data processing: Format conversion of story data

[0211] Output: HTTPS request to the terminal

[0212] Step 4: Publish and view your story

[0213] Your device will receive and notify you of the story.

[0214] Input: HTTPS request from the server

[0215] Data processing: Analysis of received data

[0216] Output: Notification generated

[0217] A user opens the app and reads a story, for example, "A Rainy Day Romance Story at Shinjuku Station."

[0218] Input: Notifications and story data

[0219] Data processing: Displaying story data

[0220] Output: Story View in User Interface

[0221] Step 5: Gather feedback

[0222] The device displays an interface to the user requesting a rating and feedback.

[0223] Input: End event of story view

[0224] Data processing: Generating feedback input forms

[0225] Output: Evaluation form displayed in the user interface

[0226] The user enters a rating and comment, for example, "5 stars" and "I'm impressed!"

[0227] Input: User ratings and comments

[0228] Data processing: Generating feedback data

[0229] Output: Completed feedback data

[0230] The device sends the feedback to the server.

[0231] Input: Feedback data

[0232] Data processing: Convert data to JSON

[0233] Output: HTTPS request to the server

[0234] The server receives the feedback and stores it in a database, so that the feedback can be reflected in future story generation.

[0235] Input: HTTPS request from the terminal

[0236] Data calculation: feedback data storage

[0237] Output: Feedback entries in the database

[0238] (Application example 1)

[0239] 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."

[0240] Conventional content distribution services have difficulty providing personalized entertainment content that reflects users' interests and real-time situations, resulting in a lack of improvement in the user experience. Additionally, they have been unable to properly reflect user feedback, limiting improvements to the quality of the content they provide.

[0241] 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.

[0242] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for generating individually optimized stories based on the collected user information and real-time data using a generative AI model, means for delivering the generated stories to a display terminal, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the provision of personalized entertainment content based on the user's interests, current location, time of day, and weather.

[0243] "User Information" means personal information provided by a User, such as name, age, gender, interests, and preferences.

[0244] "Real-time data" refers to up-to-date information that changes over time, specifically the user's current location, time, and weather information.

[0245] A "generative AI model" is an artificial intelligence algorithm for generating personalized entertainment content based on collected data.

[0246] The "story generation means" is a system that automatically creates individually optimized stories based on user information and real-time data.

[0247] A "display device" is a device such as a smartphone, tablet, or computer that is used to display the generated story to the user.

[0248] "Feedback collection means" is a system for collecting opinions and impressions such as ratings and comments on stories from users.

[0249] The "story generation process adjustment means" is a method for improving the content and quality of newly generated stories based on user feedback.

[0250] "Personalized entertainment content" refers to an entertainment experience that is optimized based on a user's individual interests and current situation.

[0251] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[0252] System Configuration

[0253] The system includes the following major components:

[0254] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0255] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0256] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0257] System Operation Overview

[0258] 1. Collection of User Information

[0259] When a user uses the application for the first time, they enter information such as their name, age, gender, favorite genres, etc. The user's device sends this information to the server, which then creates and stores the user's profile in a database.

[0260] 2. Obtaining real-time data

[0261] The user device periodically acquires the user's location, time, and weather information, and sends this real-time data to the server, which then stores the received data in a database.

[0262] 3. Story Generation

[0263] The user device sends a story request to the server at a specified time every day. The server receives the request and retrieves the user's profile and the latest real-time data from the database. The server uses the generative AI model to generate a story based on the user's profile and real-time data, and sends the generated story to the user device.

[0264] 4. Story Distribution and Display

[0265] The user device notifies the user of the story received from the server, and the user opens the application to read the story.

[0266] 5. Gathering Feedback

[0267] The user device displays an interface that requests the user to rate and provide feedback on the story. The user enters a rating (star rating, comments), and the user device sends the feedback to the server. The server receives the feedback, stores it in a database, and reflects it in future story generation.

[0268] Hardware and Software Configuration

[0269] Hardware: User devices (smartphones, tablets, PCs) and servers

[0270] software:

[0271] Python: a programming language

[0272] Requests: Used to retrieve data from the weather API

[0273] Geopy: A library for obtaining geolocation information

[0274] AI story generation library (hypothetical)

[0275] Specific examples

[0276] Consider a scenario where the user is a 30-year-old man who likes mystery novels. The user's device sends a story request at 8:00 a.m., sending real-time data to the server indicating that the user's current location is Tokyo Station and the weather is sunny. The server uses a generative AI model to generate a story titled "A mysterious incident unfolds at Tokyo Station on a sunny morning" and provides it to the user.

[0277] Prompt Sentence Examples

[0278] Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny

[0279] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0280] Step 1:

[0281] Collection of User Information

[0282] Input: Information that the user enters into the application for the first time, such as name, age, gender, and favorite genres

[0283] Processing: After the user downloads and installs the application, they enter their user information when they first launch it. The device then sends the entered information to the server.

[0284] Data processing: The server stores the received user information in a database and creates a user profile.

[0285] Output: Created user profile

[0286] Specifically, the user enters their name, age, gender, and favorite genre on the application screen, and the device sends this information to the server, which then receives the information and stores it in a database as a user profile.

[0287] Step 2:

[0288] Real-time data acquisition

[0289] Input: Location, current time, and weather information obtained by the device

[0290] Processing: Periodically, the device retrieves location, time, and weather information using the Geopy library to get location information and the Requests library to get weather information.

[0291] Data processing: The acquired information is converted into an appropriate format and sent to the server.

[0292] Output: Real-time data (location, time, weather information)

[0293] Specifically, the device uses GPS to obtain its current location and the current time from the clock. Weather information is obtained from an external weather API. This data is then sent to the server, which stores it in a database.

[0294] Step 3:

[0295] Request a story

[0296] Input: Periodic requests from the user's device

[0297] Processing: The user device sends a story generation request to the server at the specified time.

[0298] Data processing: Receive the request and retrieve the corresponding user profile and real-time data from the database.

[0299] Output: Story generation request with profile and real-time data

[0300] Specifically, the device sends a request to the server at 8 a.m., and the server receives it and retrieves the user profile and real-time data from the database.

[0301] Step 4:

[0302] Story Generation

[0303] Inputs: User profiles, real-time data, generative AI models

[0304] Processing: The server generates prompts based on the user profile and real-time data, and inputs them into the generative AI model. For example, it generates a prompt like "Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny."

[0305] Data processing: The AI ​​model generates a story based on the given prompts.

[0306] Output: Generated stories

[0307] Specifically, the server creates prompts based on the user profile and real-time data, and then inputs them into a generative AI model to generate a story.

[0308] Step 5:

[0309] Distributing and viewing stories

[0310] Input: Generated stories

[0311] Processing: The server sends the generated story to the device. The device receives the story and notifies the user.

[0312] Data processing: Converting the story into a data format for display on the device.

[0313] Output: The story displayed on the user's device

[0314] Specifically, the server sends the generated story to the terminal via an HTTP request, and the terminal receives it and displays it on the screen.

[0315] Step 6:

[0316] Gathering feedback

[0317] Input: User's rating and comments on the story

[0318] Processing: After the device finishes reading the story, it displays an interface asking the user to rate it. The user enters a rating and comments. The device then sends the entered information to the server.

[0319] Data processing: The server stores the received feedback in a database. The feedback data is analyzed and reflected in future story generation.

[0320] Output: Saved feedback data

[0321] Specifically, after a user finishes reading a story, a rating interface will appear on the screen, and the user can enter a star rating and comments. The device will then send the information to the server, which will then store it in a database.

[0322] 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.

[0323] This invention is a system that generates individually optimized stories based on user information and real-time data, and combines this with an emotion engine that recognizes the user's emotions to provide a more personalized experience. The specific system configuration and operation method are explained below.

[0324] System Configuration

[0325] The system includes the following major components:

[0326] User device: A device operated by a user, including a smartphone, tablet, or PC. It inputs user information, acquires real-time data, collects emotional data, receives and displays stories, and sends feedback.

[0327] Server: The central system that processes data and generates stories. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[0328] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated stories, and feedback.

[0329] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize the user's emotions.

[0330] System Operation Overview

[0331] 1. Collection of User Information

[0332] When a user uses the app for the first time, they enter basic information such as their name, age, gender, and favorite genres.

[0333] (Device) sends this information to the server.

[0334] (Server) receives the information and creates and stores the user profile in a database.

[0335] 2. Obtaining real-time data

[0336] (Device) periodically retrieves the user's current location, time, and weather information.

[0337] (Device) transmits the acquired real-time data to the server.

[0338] (Server) receives real-time data and stores it in a database.

[0339] 3. Collecting Emotional Data

[0340] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[0341] (Emotion engine) analyzes this data and recognizes the user's emotions.

[0342] The emotion engine sends the recognized emotion data to the server.

[0343] (Server) stores the emotion data in a database.

[0344] 4. Story Generation

[0345] (Device) sends a story request to the server at a specified time every day.

[0346] (Server) receives the request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[0347] (Server) uses an artificial intelligence engine to generate stories based on user profiles, real-time data, and emotional data.

[0348] For example, if a user likes mysteries and is aware that they are in a state of psychological tension during their morning commute, a mystery-themed story will be generated to heighten the sense of tension on the way to work.

[0349] (Server) sends the generated story to the device.

[0350] 5. Story Distribution and Display

[0351] (Device) notifies the user of the story received from the server.

[0352] (User) sees the notification, opens the app and starts reading the story.

[0353] 6. Gathering Feedback

[0354] (Device) displays an interface asking the user to rate the story and provide feedback.

[0355] (User) enters a rating (star rating, comment) for the story.

[0356] (Device) sends user feedback to the server.

[0357] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[0358] Examples:

[0359] User: Suzuki Ichiro, 35 years old, male, sci-fi novel lover

[0360] Time: 9pm, relaxation time

[0361] Location: Home

[0362] Weather: Rain

[0363] Emotional state: Feeling stressed

[0364] 1. Collection of User Information

[0365] (Ichiro Suzuki) enters "Ichiro Suzuki," "35 years old," "male," and "likes science fiction."

[0366] (Device) sends this information to the server.

[0367] (Server) receives the information and creates and stores Suzuki's profile in the database.

[0368] 2. Obtaining real-time data

[0369] (Device) periodically obtains Suzuki's current location (home) and weather information (rain).

[0370] (Device) sends location and weather information to the server.

[0371] (Server) receives this information and stores it in a database.

[0372] 3. Collecting Emotional Data

[0373] (Device) sends Suzuki's facial expression data to the emotion engine.

[0374] (Emotion engine) analyzes facial expression data and recognizes that the person is feeling stressed.

[0375] (Emotion engine) sends emotion data to the server.

[0376] (Server) stores the emotion data in a database.

[0377] 4. Story Generation

[0378] (Device) sends a story request to the server at 9:00 PM.

[0379] (Server) receives the request and retrieves Suzuki's profile, latest real-time data, and emotion data.

[0380] The server uses an artificial intelligence engine to generate a "science fiction story to read while relaxing at home on a rainy night" based on the conditions "I like science fiction," "I'm at home," "It's raining," and "I'm feeling stressed."

[0381] (Server) sends the generated story to Suzuki's device.

[0382] 5. Story Distribution and Display

[0383] (Device) receives the story and notifies Suzuki.

[0384] (Suzuki) opens the app and reads the story.

[0385] 6. Gathering Feedback

[0386] (Device) asks Suzuki to rate and comment on the story.

[0387] (Mr. Suzuki) enters feedback as "5 stars" and "Very relaxing!"

[0388] (Device) sends feedback to the server.

[0389] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[0390] In this way, a system is realized that provides personalized stories that take into account the user's emotions.

[0391] The processing flow will be explained below.

[0392] Step 1:

[0393] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[0394] Step 2:

[0395] (Device) collects information entered by the user and sends it to the server.

[0396] Step 3:

[0397] (Server) creates a user profile based on the received user information and stores it in the database.

[0398] Step 4:

[0399] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[0400] Step 5:

[0401] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[0402] Step 6:

[0403] (Server) receives real-time data and stores it in the user's status database.

[0404] Step 7:

[0405] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[0406] Step 8:

[0407] (Emotion engine) analyzes the received data and recognizes the user's emotions.

[0408] Step 9:

[0409] The emotion engine sends the recognized emotion data to the server.

[0410] Step 10:

[0411] (Server) receives the emotion data and stores it in a database.

[0412] Step 11:

[0413] (Device) sends a story request to the server at a specified time every day (e.g. 9:00 PM).

[0414] Step 12:

[0415] (Server) receives the story request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[0416] Step 13:

[0417] The (server) starts an artificial intelligence engine and passes as input the user's profile (e.g., I like science fiction), the latest real-time data (e.g., I'm at home, it's raining), and emotional data (e.g., I'm stressed).

[0418] Step 14:

[0419] (Artificial Intelligence Engine) generates a story based on the parameters you specify (e.g., a sci-fi story to read while relaxing at home on a rainy night).

[0420] Step 15:

[0421] The server retrieves the generated story and associates it with the user's profile.

[0422] Step 16:

[0423] (Server) sends the generated story to the user's device.

[0424] Step 17:

[0425] (Device) notifies the user of stories received.

[0426] Step 18:

[0427] (User) sees the notification, opens the app and reads the story.

[0428] Step 19:

[0429] (Device) displays an interface to the user asking them to rate or comment on the story.

[0430] Step 20:

[0431] (User) enters a rating (star rating, comment) for the story.

[0432] Step 21:

[0433] (Device) sends user feedback to the server.

[0434] Step 22:

[0435] (Server) receives the feedback and stores it in a database.

[0436] Step 23:

[0437] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[0438] Example 2

[0439] 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."

[0440] Existing story generation systems have difficulty providing optimized content based on a user's individual preferences, real-time situation, and emotions. Furthermore, they lack a built-in story generation process that reflects the user's emotions in real time, resulting in an insufficiently personalized user experience.

[0441] 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.

[0442] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for recognizing emotions by analyzing facial expression data, voice data, and text data, means for generating individually optimized stories based on the collected user information, real-time data, and emotional data, means for delivering the generated stories to user terminals, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the generation and delivery of personalized stories based on the individual information and emotional state of the user.

[0443] "User Information" refers to basic data provided by a user when they start using an application, and specifically includes information such as their name, age, gender, and favorite genres.

[0444] "Real-time data" is data obtained at a specific time and place, and includes location information, time, weather information, and the like.

[0445] "Emotional data" refers to data that indicates the emotional state of a user obtained by analyzing their facial expressions, voice, and text data.

[0446] A "user profile" is a data set for an individual user that is generated based on user information and stored in a database.

[0447] "Story generation" is the process of creating individually optimized stories based on collected user information, real-time data, and sentiment data.

[0448] "Feedback" refers to response data provided by users, such as ratings and comments on stories.

[0449] A "generative AI model" is a system that includes algorithms that automatically generate stories and content based on input data, for example, using natural language processing or machine learning techniques.

[0450] A "prompt" is an instruction entered into a generative AI model to specify the content and direction of the story to be generated.

[0451] This invention is a system that generates individually optimized stories based on user information and real-time data. The system incorporates an emotion engine that recognizes user emotions, enabling it to provide a more personalized experience. The system is primarily composed of components such as a user terminal, a server, a database, and an emotion engine.

[0452] System configuration:

[0453] User device:

[0454] A device operated by a user, such as a smartphone, tablet, or PC, that inputs user information, obtains real-time data, collects emotional data, receives and displays stories, and sends feedback.

[0455] Examples:

[0456] The user installs the application the first time they use it, and it uses the device's camera and microphone to collect facial expression and voice data. It also uses the GPS function to obtain location information. For example, it is commonly used as a smartphone application.

[0457] server:

[0458] This is the central system for data processing and story generation. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[0459] Examples:

[0460] We build a backend API using frameworks such as Python and Django to manage and analyze incoming data. We use OpenAI's GPT-3 as a generative AI model and generate stories by inputting prompt sentences.

[0461] Database:

[0462] It is installed on a server and stores data such as user information, real-time data, emotional data, generated stories, and feedback.

[0463] Examples:

[0464] Data is stored and retrieved using SQL queries in database management systems such as MySQL® or PostgreSQL. User profiles and feedback are used to generate future stories.

[0465] Emotion Engine:

[0466] This is a system that analyzes a user's facial expression data, voice data, and text data to recognize the user's emotions.

[0467] Examples:

[0468] The collected data is analyzed using Google® Cloud Vision API and IBM Watson® sentiment analysis API to generate emotional data, which is then sent to a server and stored in a database.

[0469] Examples and prompts:

[0470] Examples:

[0471] User: 35-year-old male, sci-fi fan

[0472] Real-time data: At home, time is 9pm, weather is raining

[0473] Emotional state: Stressed

[0474] Example prompt sentence:

[0475] "Generate a relaxing sci-fi story for this user who is a 35-year-old male who likes sci-fi, is currently at home, and is feeling stressed because it's raining outside."

[0476] In this way, a system is realized that takes into account user emotions and real-time data to generate individually optimized stories and provide users with a personalized experience.

[0477] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0478] Step 1:

[0479] Collection of User Information

[0480] Input: Basic information entered by the user, such as name, age, gender, and favorite genres

[0481] Processing: When a user first uses the app, they enter basic information such as their name, age, gender, and favorite genres. The device then sends this information to the server.

[0482] Output: The server stores the received user information in a database and creates a user profile.

[0483] How it works: When a user enters information into the input form displayed on the app screen and presses the submit button, the device sends the data to the backend API, and the server executes an SQL query to store the received data in the database.

[0484] Step 2:

[0485] Real-time data acquisition

[0486] Input: User's current location, time, and weather information

[0487] Processing: The device periodically obtains the user's location, time, and weather information and sends it to the server.

[0488] Output: The server stores the received real-time data in a database.

[0489] Specific operation: Obtain location information from the device's GPS sensor and time information from the system clock. Obtain weather information using an external API (e.g., OpenWeatherMap). Send this data to a backend API on a regular timer, and the server executes an SQL query to store the received data in a database.

[0490] Step 3:

[0491] Collecting Emotional Data

[0492] Input: User's facial expression data, voice data, text data

[0493] Processing: The device sends this data to the emotion engine and receives the analysis results.

[0494] Output: The emotion engine sends the analyzed emotion data to the server, which stores it in a database.

[0495] How it works: It uses the device's camera, microphone, and input devices to collect facial expression, voice, and text data. This data is then sent to the emotion engine's API, which uses image processing and natural language processing algorithms to determine the user's emotional state. The result is then sent to the backend API, and the server executes an SQL query to store the received data in a database.

[0496] Step 4:

[0497] Story Generation

[0498] Inputs: User profile data, up-to-date real-time data, sentiment data

[0499] Processing: The server receives a story request from the device at the specified time, retrieves the user profile, real-time data, and emotion data from the database, and then inputs the prompt sentence into the generative AI model to generate the story.

[0500] Output: Send the generated story from the server to the device.

[0501] Specific operation: At the specified time, the device sends a story request to the backend API. The server executes an SQL query to obtain the necessary data and inputs the following prompt statement into the generative AI model (e.g., GPT-3): "This user is a 35-year-old male who likes science fiction. He is currently at home and feeling stressed because it's raining outside. Please generate a relaxing science fiction story for him." The generated story is then sent to the device via the backend API.

[0502] Step 5:

[0503] Distributing and viewing stories

[0504] Input: Story sent from the server

[0505] Processing: The device notifies the user of the received story, and the user opens the app to read the story.

[0506] Output: The story is displayed to the user.

[0507] What happens: The device sends a push notification to the user to let them know that a story has been delivered. The user taps the app to launch it and view the received story.

[0508] Step 6:

[0509] Gathering feedback

[0510] Input: User's rating and comments on the story

[0511] Processing: The device collects feedback from the user and sends it to the server.

[0512] Output: The server receives the feedback and stores it in a database. The feedback is reflected in future story generation.

[0513] Specific operation: After reading the story, the device displays a rating form, and the user enters their rating and comments. The input data is sent to the backend API by pressing the submit button, and the server stores the received data in a database and uses it for the next story generation.

[0514] (Application example 2)

[0515] 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."

[0516] Conventional ad delivery systems could only provide ads based on basic user attribute information, and were unable to provide personalized ads that took into account the user's momentary emotions and real-time circumstances. This limited the effectiveness of ads and made it difficult to attract user interest. Furthermore, they lacked a mechanism for effectively utilizing feedback to improve the quality of ads.

[0517] 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.

[0518] In this invention, the server includes: means for collecting user information; means for acquiring real-time data; means for generating individually optimized advertising stories based on the collected user information and real-time data; means including an emotion engine for collecting and analyzing user emotion data; means for delivering the generated advertising stories to a display terminal; means for collecting user feedback; and means for adjusting the advertising story generation process based on the feedback. This allows for the generation of advertisements that take into account the user's momentary emotions and real-time situations, enabling more effective and personalized advertisement delivery. Furthermore, the quality of advertisements can be continuously improved by utilizing the feedback.

[0519] "Means for collecting user information" refers to hardware or software used to collect basic user attribute information (e.g., name, age, gender, interests, etc.).

[0520] "Means for obtaining real-time data" refers to hardware or software for periodically obtaining the latest data such as the user's current location, time, and weather information.

[0521] "Means for generating individually optimized advertising stories based on collected user information and real-time data" refers to hardware or software that has algorithms or generative AI models for analyzing collected user information and real-time data and generating advertising content optimized for each individual user.

[0522] An "emotion engine that collects and analyzes user emotional data" is hardware or software that includes an AI engine that analyzes a user's facial expressions, voice, text data, etc., and recognizes their emotional state.

[0523] "Means for delivering the generated advertising story to a display device" refers to hardware or software for transmitting and displaying the generated advertising story on a display device such as a user's smartphone, tablet, or PC.

[0524] "Means for collecting user feedback" means hardware or software for collecting ratings and comments made by users on advertising stories.

[0525] A "means for adjusting the advertising story generation process based on feedback" is hardware or software for analyzing user feedback and, based thereon, improving how future advertising stories are generated.

[0526] This invention is a system that generates individually optimized advertising stories based on user information, real-time data, and emotional data, and provides users with a personalized advertising experience. Specific methods for implementing this system are described below.

[0527] System configuration

[0528] The system includes the following major components:

[0529] User devices: smartphones, tablets, computers, etc. User information is entered, real-time data is acquired, emotion data is collected, advertising stories are received and displayed, and feedback is sent.

[0530] Server: The central system that processes data and generates advertising stories. It manages user information, real-time data, and emotional data, generates advertising stories, and receives and analyzes feedback.

[0531] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated advertising stories, and feedback.

[0532] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize emotions.

[0533] System Operation Overview

[0534] 1. Collection of User Information

[0535] When a user uses the system for the first time, they enter basic information such as their name, age, gender, and areas of interest. This information is sent to the server via the user's device and stored in a database.

[0536] 2. Obtaining real-time data

[0537] The user device periodically obtains the current location, time, and weather information and sends it to the server, which stores this real-time data in a database.

[0538] 3. Collecting Emotional Data

[0539] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database.

[0540] 4. Advertising story generation

[0541] The user device sends a request to the server to generate an advertising story at a specified time. The server obtains the user's profile, the latest real-time data, and emotional data, and uses an artificial intelligence engine to generate the optimal advertising story based on this. The generated advertising story is then sent to the user device.

[0542] 5. Delivery and display of advertising stories

[0543] The user terminal notifies the user of the advertising story received from the server and displays it through the application.

[0544] 6. Gathering Feedback

[0545] The user device displays an interface that asks the user to rate and provide feedback on the advertising story. The user's feedback is sent to the server and stored in a database. The feedback information is reflected in future advertising story generation.

[0546] Specific examples

[0547] For example, for a user with the following profile:

[0548] User: Taro Yamada, 45 years old, male, loves technology

[0549] Time: 3pm, office

[0550] Location: Office building

[0551] Weather: Sunny

[0552] Emotional state: Focused

[0553] For this user, we generate an advertising story for "latest technology products" that will help them stay focused in an office environment.

[0554] Prompt Sentence Examples

[0555] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[0556] This system makes it possible to provide personalized ads that reflect the user's momentary emotions and real-time situations, and also to use feedback to improve the quality of ads.

[0557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0558] Step 1:

[0559] Collection of User Information

[0560] When a user first uses the system, they enter basic information such as their name, age, gender, and areas of interest. The entered information is sent by the user's device to a server, which receives the information and stores it in a database. In this process, the entered data is used to generate a basic profile to understand the user's interests.

[0561] Step 2:

[0562] Real-time data acquisition

[0563] The user device periodically obtains the user's current location, time, and weather information. This information is updated in real time and sent to the server. The server stores this real-time data in a database. Using the real-time data (location, time, and weather) as input, the server processes the data to understand the user's current situation.

[0564] Step 3:

[0565] Collecting Emotional Data

[0566] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database. The input data is analyzed to output the user's emotional state and store it as emotion data.

[0567] Step 4:

[0568] Advertising story generation

[0569] Each day, a user device sends a request to the server at a specified time to generate an advertising story. The server receives the request and retrieves the user's profile, the latest real-time data, and sentiment data from the database. The generative AI model generates an advertising story based on this data. The profile data, real-time data, and sentiment data are analyzed and integrated to generate an individually optimized advertising story as output.

[0570] Step 5:

[0571] Ad story distribution and display

[0572] The server sends the generated advertising story to the user terminal, which receives the advertising story, notifies the user, and displays it through the application, receiving the generated advertising story as input and outputting it as notification and display data.

[0573] Step 6:

[0574] Gathering feedback

[0575] The user device displays an interface that requests the user to rate and provide feedback on the advertising story. The user device sends the feedback to the server, which receives the feedback and stores it in a database. The feedback information is collected as input for use in generating the next advertising story, and stored as output in the feedback database.

[0576] Examples include the following:

[0577] For example, if the user is "Taro Yamada, 45 years old, male, loves technology" and is concentrating in a sunny office building at 3 p.m., an advertising story for a "personalized technology product" will be automatically generated and delivered to the user.

[0578] Prompt Sentence Examples

[0579] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Second embodiment]

[0584] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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."

[0596] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[0597] System Configuration

[0598] The system includes the following major components:

[0599] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0600] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0601] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0602] System Operation Overview

[0603] 1. Collection of User Information

[0604] When a user uses the app for the first time, they enter information such as their name, age, gender, and favorite genres.

[0605] (Device) sends this information to the server.

[0606] (Server) receives the information and creates and stores the user's profile in a database.

[0607] 2. Obtaining real-time data

[0608] (Device) periodically obtains the user's location, time, and weather information.

[0609] (Device) transmits the acquired real-time data to the server.

[0610] (Server) receives real-time data and stores it in a database.

[0611] 3. Story Generation

[0612] (Device) sends a story request to the server at a specified time every day.

[0613] (Server) receives the request and retrieves the user profile and latest real-time data from the database.

[0614] (The server) uses an artificial intelligence engine to generate stories based on user profiles and real-time data.

[0615] For example, if it determines that a user likes mysteries and is on their way to work in the morning, it will generate a story themed around a mystery incident that occurs on the way to work.

[0616] (Server) sends the generated story to the device.

[0617] 4. Story Distribution and Display

[0618] (Device) notifies the user of the story received from the server.

[0619] (User) opens the app and starts reading a story.

[0620] 5. Gathering Feedback

[0621] (Device) displays an interface to the user requesting a rating and feedback on the story.

[0622] (User) enters a rating (star rating, comment) for the story.

[0623] (Device) sends user feedback to the server.

[0624] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[0625] Examples:

[0626] User: Taro Tanaka, 30 years old, male, loves mystery novels

[0627] Time: 7am, commute time

[0628] Location: Near Tokyo Station

[0629] Weather: Sunny

[0630] 1. Collection of User Information

[0631] (Taro Tanaka) enters "Taro Tanaka," "30 years old," "male," and "likes mysteries."

[0632] (Device) sends this information to the server.

[0633] (Server) receives the information and creates and stores Tanaka's profile in the database.

[0634] 2. Obtaining real-time data

[0635] (Device) periodically obtains Tanaka's current location information (near Tokyo Station) and weather information (sunny).

[0636] (Device) sends location and weather information to the server.

[0637] (Server) receives this information and stores it in a database.

[0638] 3. Story Generation

[0639] (Device) sends a story request to the server at 7am.

[0640] (Server) receives the request and retrieves Tanaka's profile and the latest real-time data.

[0641] (Server) uses an artificial intelligence engine based on the conditions "I like mysteries," "On my way to work," "Tokyo Station," and "Sunny weather" to generate a story called "A mysterious incident that takes place at Tokyo Station on a sunny morning."

[0642] (Server) sends the generated story to Tanaka's device.

[0643] 4. Story Distribution and Display

[0644] (Device) receives the story and notifies Tanaka.

[0645] (Tanaka) opens the app and reads the story.

[0646] 5. Gathering Feedback

[0647] (Device) asks Tanaka to rate and comment on the story.

[0648] (Tanaka) enters feedback such as "5 stars" and "Very interesting!"

[0649] (Device) sends feedback to the server.

[0650] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[0651] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[0655] Step 2:

[0656] (Device) collects information entered by the user and sends it to the server.

[0657] Step 3:

[0658] (Server) creates a user profile based on the received user information and stores it in the database.

[0659] Step 4:

[0660] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[0661] Step 5:

[0662] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[0663] Step 6:

[0664] (Server) receives real-time data and stores it in the user's status database.

[0665] Step 7:

[0666] (Device) sends a story request to the server at a specified time every day (e.g. 7am).

[0667] Step 8:

[0668] (Server) receives the story request and retrieves the user's profile and the latest real-time data from the database.

[0669] Step 9:

[0670] The (server) launches an artificial intelligence engine and provides the user's profile (e.g., "I like mysteries") and real-time data (e.g., Tokyo Station, sunny weather) as input.

[0671] Step 10:

[0672] (Artificial Intelligence Engine) generates a story based on specified parameters (e.g., a mysterious incident that occurs at Tokyo Station on a sunny morning).

[0673] Step 11:

[0674] The server retrieves the generated story and associates it with the user's profile.

[0675] Step 12:

[0676] (Server) sends the generated story to the user's device.

[0677] Step 13:

[0678] (Device) notifies the user of stories received.

[0679] Step 14:

[0680] (User) sees the notification, opens the app and reads the story.

[0681] Step 15:

[0682] (Device) displays an interface to the user asking them to rate or comment on the story.

[0683] Step 16:

[0684] (User) enters a rating (star rating, comment) for the story.

[0685] Step 17:

[0686] (Device) sends user feedback to the server.

[0687] Step 18:

[0688] (Server) receives the feedback and stores it in a database.

[0689] Step 19:

[0690] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[0691] Example 1

[0692] 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."

[0693] Conventional story generation systems lacked sufficient personalization based on individual user interests and real-time situations, making it difficult to provide content that would capture users' attention. Furthermore, feedback was not collected or reflected efficiently, making it difficult to continuously improve the user experience. This made improving user satisfaction a challenge.

[0694] 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.

[0695] In this invention, the server includes a means for collecting user information, a means for acquiring real-time data, and a means for generating prompt sentences and generating stories using a generative AI model, thereby enabling adaptive story generation based on the user's individual interests and real-time situations.

[0696] "User Information" means data about an individual provided by a User, such as name, age, gender, and interests.

[0697] "Real-time data" refers to data relevant to the current situation, such as the user's location, time, and weather information.

[0698] "Stories" refers to texts and narratives generated based on user profiles and real-time data.

[0699] A "display terminal" is a device operated by a user, including a smartphone, tablet, or PC.

[0700] "Feedback" refers to ratings and comments on stories provided by users, and is data that is reflected in future story generation.

[0701] A "prompt" is a piece of text that is input into a generative AI model, instructing it to generate a story based on specific conditions or settings.

[0702] A "generative AI model" refers to an artificial intelligence algorithm or engine that generates a story based on an input prompt.

[0703] The present invention provides a system for collecting user information, acquiring real-time data, generating individually optimized stories based on the collected information, and providing the stories to users. Specific embodiments of this system are described below.

[0704] System Configuration

[0705] The system mainly includes the following components:

[0706] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0707] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0708] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0709] Collection of User Information

[0710] When a user uses an app for the first time, they enter user information, such as their name, age, gender, and favorite genre. The device sends this information to the server, which then receives it and creates and saves a user profile in a database.

[0711] Real-time data acquisition

[0712] The device periodically obtains the user's location, time, and weather information, allowing it to generate content appropriate to the user's current situation. The device then transmits the obtained real-time data to the server, which then receives the data and stores it in a database.

[0713] Story Generation

[0714] The device sends a story request to the server at a specified time every day. The server receives the request, retrieves the user's profile and the latest real-time data from the database, generates a prompt, and generates a story using a generative AI model.

[0715] As a concrete example, we input the following prompt into the generative AI model:

[0716] "For a 30-year-old man who likes mysteries, please generate a mystery that takes place at Tokyo Station on a sunny morning."

[0717] The server transmits the generated story to the terminal, and the terminal notifies the user of the story.

[0718] Gathering feedback

[0719] After the user finishes reading the story, the device displays an interface asking for feedback. The user enters a rating and comments, and the device sends this feedback to the server. The server receives the feedback and stores it in a database, allowing it to reflect the feedback in future story generation and improve the user experience.

[0720] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[0721] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0722] Step 1: Collect user information

[0723] The user accesses the app and enters their name, age, gender, and favorite genre. For example, they might enter "Yamada Hanako," "25 years old," "female," and "likes romance novels."

[0724] The device sends the entered information to the server. The sent content includes the entered information along with the user ID.

[0725] Input: Information entered by the user ("Hanako Yamada," "25 years old," "female," "likes romance novels")

[0726] Data processing: Convert user information into JSON format

[0727] Output: HTTPS request to the server

[0728] The server receives the information and stores the user profile in its database, for example as "user5678".

[0729] Input: HTTPS request from the terminal

[0730] Data Calculation: Creating and Saving User Profiles

[0731] Output: New user entry in the database

[0732] Step 2: Getting real-time data

[0733] The device periodically obtains location, time, and weather information. For example, every 10 minutes, it obtains "Shinjuku Station" from the GPS, "12:30 PM" from the system clock, and "rain" from the weather API.

[0734] Input: GPS data, system clock, weather API

[0735] Data processing: Format conversion of acquired data

[0736] Output: Packets of acquired data

[0737] The real-time data acquired by the terminal is transmitted to the server.

[0738] Input: Packets of acquired data

[0739] Data processing: JSON conversion of data packets

[0740] Output: HTTPS request to the server

[0741] The server receives the data and stores it in the database under the corresponding user ID.

[0742] Input: HTTPS request from the terminal

[0743] Data calculation: Real-time data storage

[0744] Output: Real-time data entry into a database

[0745] Step 3: Story Generation

[0746] The device sends a story request to the server at a specified time each day, for example, 7:00 AM.

[0747] Input: User ID and timestamp

[0748] Data processing: Creating a request format

[0749] Output: HTTPS request to the server

[0750] The server retrieves the user's profile and the latest real-time data from the database.

[0751] Input: User ID

[0752] Data Computing: Querying user and real-time databases

[0753] Output: User profile and up-to-date real-time data

[0754] The server generates a prompt and uses a generative AI model to generate a story, for example, "Generate a rainy day romance story at Shinjuku Station for a 25-year-old woman who loves romance novels."

[0755] Input: User profile and up-to-date real-time data

[0756] Data Calculation: Prompt Statement Generation

[0757] Output: Input prompts to the generative AI model

[0758] Input: prompt to generative AI model

[0759] Data Calculation: Story Generation

[0760] Output: Generated stories

[0761] The server sends the generated story to the device.

[0762] Input: Generated Story

[0763] Data processing: Format conversion of story data

[0764] Output: HTTPS request to the terminal

[0765] Step 4: Publish and view your story

[0766] Your device will receive and notify you of the story.

[0767] Input: HTTPS request from the server

[0768] Data processing: Analysis of received data

[0769] Output: Notification generated

[0770] A user opens the app and reads a story, for example, "A Rainy Day Romance Story at Shinjuku Station."

[0771] Input: Notifications and story data

[0772] Data processing: Displaying story data

[0773] Output: Story View in User Interface

[0774] Step 5: Gather feedback

[0775] The device displays an interface to the user requesting a rating and feedback.

[0776] Input: End event of story view

[0777] Data processing: Generating feedback input forms

[0778] Output: Evaluation form displayed in the user interface

[0779] The user enters a rating and comment, for example, "5 stars" and "I'm impressed!"

[0780] Input: User ratings and comments

[0781] Data processing: Generating feedback data

[0782] Output: Completed feedback data

[0783] The device sends the feedback to the server.

[0784] Input: Feedback data

[0785] Data processing: Convert data to JSON

[0786] Output: HTTPS request to the server

[0787] The server receives the feedback and stores it in a database, so that the feedback can be reflected in future story generation.

[0788] Input: HTTPS request from the terminal

[0789] Data calculation: feedback data storage

[0790] Output: Feedback entries in the database

[0791] (Application example 1)

[0792] 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."

[0793] Conventional content distribution services have difficulty providing personalized entertainment content that reflects users' interests and real-time situations, resulting in a lack of improvement in the user experience. Additionally, they have been unable to properly reflect user feedback, limiting improvements to the quality of the content they provide.

[0794] 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.

[0795] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for generating individually optimized stories based on the collected user information and real-time data using a generative AI model, means for delivering the generated stories to a display terminal, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the provision of personalized entertainment content based on the user's interests, current location, time of day, and weather.

[0796] "User Information" means personal information provided by a User, such as name, age, gender, interests, and preferences.

[0797] "Real-time data" refers to up-to-date information that changes over time, specifically the user's current location, time, and weather information.

[0798] A "generative AI model" is an artificial intelligence algorithm for generating personalized entertainment content based on collected data.

[0799] The "story generation means" is a system that automatically creates individually optimized stories based on user information and real-time data.

[0800] A "display device" is a device such as a smartphone, tablet, or computer that is used to display the generated story to the user.

[0801] "Feedback collection means" is a system for collecting opinions and impressions such as ratings and comments on stories from users.

[0802] The "story generation process adjustment means" is a method for improving the content and quality of newly generated stories based on user feedback.

[0803] "Personalized entertainment content" refers to an entertainment experience that is optimized based on a user's individual interests and current situation.

[0804] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[0805] System Configuration

[0806] The system includes the following major components:

[0807] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[0808] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[0809] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[0810] System Operation Overview

[0811] 1. Collection of User Information

[0812] When a user uses the application for the first time, they enter information such as their name, age, gender, favorite genres, etc. The user's device sends this information to the server, which then creates and stores the user's profile in a database.

[0813] 2. Obtaining real-time data

[0814] The user device periodically acquires the user's location, time, and weather information, and sends this real-time data to the server, which then stores the received data in a database.

[0815] 3. Story Generation

[0816] The user device sends a story request to the server at a specified time every day. The server receives the request and retrieves the user's profile and the latest real-time data from the database. The server uses the generative AI model to generate a story based on the user's profile and real-time data, and sends the generated story to the user device.

[0817] 4. Story Distribution and Display

[0818] The user device notifies the user of the story received from the server, and the user opens the application to read the story.

[0819] 5. Gathering Feedback

[0820] The user device displays an interface that requests the user to rate and provide feedback on the story. The user enters a rating (star rating, comments), and the user device sends the feedback to the server. The server receives the feedback, stores it in a database, and reflects it in future story generation.

[0821] Hardware and Software Configuration

[0822] Hardware: User devices (smartphones, tablets, PCs) and servers

[0823] software:

[0824] Python: a programming language

[0825] Requests: Used to retrieve data from the weather API

[0826] Geopy: A library for obtaining geolocation information

[0827] AI story generation library (hypothetical)

[0828] Specific examples

[0829] Consider a scenario where the user is a 30-year-old man who likes mystery novels. The user's device sends a story request at 8:00 a.m., sending real-time data to the server indicating that the user's current location is Tokyo Station and the weather is sunny. The server uses a generative AI model to generate a story titled "A mysterious incident unfolds at Tokyo Station on a sunny morning" and provides it to the user.

[0830] Prompt Sentence Examples

[0831] Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny

[0832] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0833] Step 1:

[0834] Collection of User Information

[0835] Input: Information that the user enters into the application for the first time, such as name, age, gender, and favorite genres

[0836] Processing: After the user downloads and installs the application, they enter their user information when they first launch it. The device then sends the entered information to the server.

[0837] Data processing: The server stores the received user information in a database and creates a user profile.

[0838] Output: Created user profile

[0839] Specifically, the user enters their name, age, gender, and favorite genre on the application screen, and the device sends this information to the server, which then receives the information and stores it in a database as a user profile.

[0840] Step 2:

[0841] Real-time data acquisition

[0842] Input: Location, current time, and weather information obtained by the device

[0843] Processing: Periodically, the device retrieves location, time, and weather information using the Geopy library to get location information and the Requests library to get weather information.

[0844] Data processing: The acquired information is converted into an appropriate format and sent to the server.

[0845] Output: Real-time data (location, time, weather information)

[0846] Specifically, the device uses GPS to obtain its current location and the current time from the clock. Weather information is obtained from an external weather API. This data is then sent to the server, which stores it in a database.

[0847] Step 3:

[0848] Request a story

[0849] Input: Periodic requests from the user's device

[0850] Processing: The user device sends a story generation request to the server at the specified time.

[0851] Data processing: Receive the request and retrieve the corresponding user profile and real-time data from the database.

[0852] Output: Story generation request with profile and real-time data

[0853] Specifically, the device sends a request to the server at 8 a.m., and the server receives it and retrieves the user profile and real-time data from the database.

[0854] Step 4:

[0855] Story Generation

[0856] Inputs: User profiles, real-time data, generative AI models

[0857] Processing: The server generates prompts based on the user profile and real-time data, and inputs them into the generative AI model. For example, it generates a prompt like "Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny."

[0858] Data processing: The AI ​​model generates a story based on the given prompts.

[0859] Output: Generated stories

[0860] Specifically, the server creates prompts based on the user profile and real-time data, and then inputs them into a generative AI model to generate a story.

[0861] Step 5:

[0862] Distributing and viewing stories

[0863] Input: Generated stories

[0864] Processing: The server sends the generated story to the device. The device receives the story and notifies the user.

[0865] Data processing: Converting the story into a data format for display on the device.

[0866] Output: The story displayed on the user's device

[0867] Specifically, the server sends the generated story to the terminal via an HTTP request, and the terminal receives it and displays it on the screen.

[0868] Step 6:

[0869] Gathering feedback

[0870] Input: User's rating and comments on the story

[0871] Processing: After the device finishes reading the story, it displays an interface asking the user to rate it. The user enters a rating and comments. The device then sends the entered information to the server.

[0872] Data processing: The server stores the received feedback in a database. The feedback data is analyzed and reflected in future story generation.

[0873] Output: Saved feedback data

[0874] Specifically, after a user finishes reading a story, a rating interface will appear on the screen, and the user can enter a star rating and comments. The device will then send the information to the server, which will then store it in a database.

[0875] 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.

[0876] This invention is a system that generates individually optimized stories based on user information and real-time data, and combines this with an emotion engine that recognizes the user's emotions to provide a more personalized experience. The specific system configuration and operation method are explained below.

[0877] System Configuration

[0878] The system includes the following major components:

[0879] User device: A device operated by a user, including a smartphone, tablet, or PC. It inputs user information, acquires real-time data, collects emotional data, receives and displays stories, and sends feedback.

[0880] Server: The central system that processes data and generates stories. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[0881] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated stories, and feedback.

[0882] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize the user's emotions.

[0883] System Operation Overview

[0884] 1. Collection of User Information

[0885] When a user uses the app for the first time, they enter basic information such as their name, age, gender, and favorite genres.

[0886] (Device) sends this information to the server.

[0887] (Server) receives the information and creates and stores the user profile in a database.

[0888] 2. Obtaining real-time data

[0889] (Device) periodically retrieves the user's current location, time, and weather information.

[0890] (Device) transmits the acquired real-time data to the server.

[0891] (Server) receives real-time data and stores it in a database.

[0892] 3. Collecting Emotional Data

[0893] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[0894] (Emotion engine) analyzes this data and recognizes the user's emotions.

[0895] The emotion engine sends the recognized emotion data to the server.

[0896] (Server) stores the emotion data in a database.

[0897] 4. Story Generation

[0898] (Device) sends a story request to the server at a specified time every day.

[0899] (Server) receives the request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[0900] (Server) uses an artificial intelligence engine to generate stories based on user profiles, real-time data, and emotional data.

[0901] For example, if a user likes mysteries and is aware that they are in a state of psychological tension during their morning commute, a mystery-themed story will be generated to heighten the sense of tension on the way to work.

[0902] (Server) sends the generated story to the device.

[0903] 5. Story Distribution and Display

[0904] (Device) notifies the user of the story received from the server.

[0905] (User) sees the notification, opens the app and starts reading the story.

[0906] 6. Gathering Feedback

[0907] (Device) displays an interface asking the user to rate the story and provide feedback.

[0908] (User) enters a rating (star rating, comment) for the story.

[0909] (Device) sends user feedback to the server.

[0910] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[0911] Examples:

[0912] User: Suzuki Ichiro, 35 years old, male, sci-fi novel lover

[0913] Time: 9pm, relaxation time

[0914] Location: Home

[0915] Weather: Rain

[0916] Emotional state: Feeling stressed

[0917] 1. Collection of User Information

[0918] (Ichiro Suzuki) enters "Ichiro Suzuki," "35 years old," "male," and "likes science fiction."

[0919] (Device) sends this information to the server.

[0920] (Server) receives the information and creates and stores Suzuki's profile in the database.

[0921] 2. Obtaining real-time data

[0922] (Device) periodically obtains Suzuki's current location (home) and weather information (rain).

[0923] (Device) sends location and weather information to the server.

[0924] (Server) receives this information and stores it in a database.

[0925] 3. Collecting Emotional Data

[0926] (Device) sends Suzuki's facial expression data to the emotion engine.

[0927] (Emotion engine) analyzes facial expression data and recognizes that the person is feeling stressed.

[0928] (Emotion engine) sends emotion data to the server.

[0929] (Server) stores the emotion data in a database.

[0930] 4. Story Generation

[0931] (Device) sends a story request to the server at 9:00 PM.

[0932] (Server) receives the request and retrieves Suzuki's profile, latest real-time data, and emotion data.

[0933] The server uses an artificial intelligence engine to generate a "science fiction story to read while relaxing at home on a rainy night" based on the conditions "I like science fiction," "I'm at home," "It's raining," and "I'm feeling stressed."

[0934] (Server) sends the generated story to Suzuki's device.

[0935] 5. Story Distribution and Display

[0936] (Device) receives the story and notifies Suzuki.

[0937] (Suzuki) opens the app and reads the story.

[0938] 6. Gathering Feedback

[0939] (Device) asks Suzuki to rate and comment on the story.

[0940] (Mr. Suzuki) enters feedback as "5 stars" and "Very relaxing!"

[0941] (Device) sends feedback to the server.

[0942] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[0943] In this way, a system is realized that provides personalized stories that take into account the user's emotions.

[0944] The processing flow will be explained below.

[0945] Step 1:

[0946] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[0947] Step 2:

[0948] (Device) collects information entered by the user and sends it to the server.

[0949] Step 3:

[0950] (Server) creates a user profile based on the received user information and stores it in the database.

[0951] Step 4:

[0952] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[0953] Step 5:

[0954] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[0955] Step 6:

[0956] (Server) receives real-time data and stores it in the user's status database.

[0957] Step 7:

[0958] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[0959] Step 8:

[0960] (Emotion engine) analyzes the received data and recognizes the user's emotions.

[0961] Step 9:

[0962] The emotion engine sends the recognized emotion data to the server.

[0963] Step 10:

[0964] (Server) receives the emotion data and stores it in a database.

[0965] Step 11:

[0966] (Device) sends a story request to the server at a specified time every day (e.g. 9:00 PM).

[0967] Step 12:

[0968] (Server) receives the story request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[0969] Step 13:

[0970] The (server) starts an artificial intelligence engine and passes as input the user's profile (e.g., I like science fiction), the latest real-time data (e.g., I'm at home, it's raining), and emotional data (e.g., I'm stressed).

[0971] Step 14:

[0972] (Artificial Intelligence Engine) generates a story based on the parameters you specify (e.g., a sci-fi story to read while relaxing at home on a rainy night).

[0973] Step 15:

[0974] The server retrieves the generated story and associates it with the user's profile.

[0975] Step 16:

[0976] (Server) sends the generated story to the user's device.

[0977] Step 17:

[0978] (Device) notifies the user of stories received.

[0979] Step 18:

[0980] (User) sees the notification, opens the app and reads the story.

[0981] Step 19:

[0982] (Device) displays an interface to the user asking them to rate or comment on the story.

[0983] Step 20:

[0984] (User) enters a rating (star rating, comment) for the story.

[0985] Step 21:

[0986] (Device) sends user feedback to the server.

[0987] Step 22:

[0988] (Server) receives the feedback and stores it in a database.

[0989] Step 23:

[0990] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[0991] Example 2

[0992] 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."

[0993] Existing story generation systems have difficulty providing optimized content based on a user's individual preferences, real-time situation, and emotions. Furthermore, they lack a built-in story generation process that reflects the user's emotions in real time, resulting in an insufficiently personalized user experience.

[0994] 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.

[0995] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for recognizing emotions by analyzing facial expression data, voice data, and text data, means for generating individually optimized stories based on the collected user information, real-time data, and emotional data, means for delivering the generated stories to user terminals, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the generation and delivery of personalized stories based on the individual information and emotional state of the user.

[0996] "User Information" refers to basic data provided by a user when they start using an application, and specifically includes information such as their name, age, gender, and favorite genres.

[0997] "Real-time data" is data obtained at a specific time and place, and includes location information, time, weather information, and the like.

[0998] "Emotional data" refers to data that indicates the emotional state of a user obtained by analyzing their facial expressions, voice, and text data.

[0999] A "user profile" is a data set for an individual user that is generated based on user information and stored in a database.

[1000] "Story generation" is the process of creating individually optimized stories based on collected user information, real-time data, and sentiment data.

[1001] "Feedback" refers to response data provided by users, such as ratings and comments on stories.

[1002] A "generative AI model" is a system that includes algorithms that automatically generate stories and content based on input data, for example, using natural language processing or machine learning techniques.

[1003] A "prompt" is an instruction entered into a generative AI model to specify the content and direction of the story to be generated.

[1004] This invention is a system that generates individually optimized stories based on user information and real-time data. The system incorporates an emotion engine that recognizes user emotions, enabling it to provide a more personalized experience. The system is primarily composed of components such as a user terminal, a server, a database, and an emotion engine.

[1005] System configuration:

[1006] User device:

[1007] A device operated by a user, such as a smartphone, tablet, or PC, that inputs user information, obtains real-time data, collects emotional data, receives and displays stories, and sends feedback.

[1008] Examples:

[1009] The user installs the application the first time they use it, and it uses the device's camera and microphone to collect facial expression and voice data. It also uses the GPS function to obtain location information. For example, it is commonly used as a smartphone application.

[1010] server:

[1011] This is the central system for data processing and story generation. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[1012] Examples:

[1013] We use frameworks such as Python and Django to build a backend API to manage and analyze incoming data. We use OpenAI's GPT-3 as a generative AI model to generate stories by inputting prompt sentences.

[1014] Database:

[1015] It is installed on a server and stores data such as user information, real-time data, emotional data, generated stories, and feedback.

[1016] Examples:

[1017] Data is stored and retrieved using SQL queries in database management systems such as MySQL or PostgreSQL. User profiles and feedback are used to inform future story generation.

[1018] Emotion Engine:

[1019] This is a system that analyzes a user's facial expression data, voice data, and text data to recognize the user's emotions.

[1020] Examples:

[1021] Using Google Cloud Vision API and IBM Watson's sentiment analysis API, the collected data is analyzed to generate emotion data, which is then sent to a server and stored in a database.

[1022] Examples and prompts:

[1023] Examples:

[1024] User: 35-year-old male, sci-fi fan

[1025] Real-time data: At home, time is 9pm, weather is raining

[1026] Emotional state: Stressed

[1027] Example prompt sentence:

[1028] "Generate a relaxing sci-fi story for this user who is a 35-year-old male who likes sci-fi, is currently at home, and is feeling stressed because it's raining outside."

[1029] In this way, a system is realized that takes into account user emotions and real-time data to generate individually optimized stories and provide users with a personalized experience.

[1030] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1031] Step 1:

[1032] Collection of User Information

[1033] Input: Basic information entered by the user, such as name, age, gender, and favorite genres

[1034] Processing: When a user first uses the app, they enter basic information such as their name, age, gender, and favorite genres. The device then sends this information to the server.

[1035] Output: The server stores the received user information in a database and creates a user profile.

[1036] How it works: When a user enters information into the input form displayed on the app screen and presses the submit button, the device sends the data to the backend API, and the server executes an SQL query to store the received data in the database.

[1037] Step 2:

[1038] Real-time data acquisition

[1039] Input: User's current location, time, and weather information

[1040] Processing: The device periodically obtains the user's location, time, and weather information and sends it to the server.

[1041] Output: The server stores the received real-time data in a database.

[1042] Specific operation: Obtain location information from the device's GPS sensor and time information from the system clock. Obtain weather information using an external API (e.g., OpenWeatherMap). Send this data to a backend API on a regular timer, and the server executes an SQL query to store the received data in a database.

[1043] Step 3:

[1044] Collecting Emotional Data

[1045] Input: User's facial expression data, voice data, text data

[1046] Processing: The device sends this data to the emotion engine and receives the analysis results.

[1047] Output: The emotion engine sends the analyzed emotion data to the server, which stores it in a database.

[1048] How it works: It uses the device's camera, microphone, and input devices to collect facial expression, voice, and text data. This data is then sent to the emotion engine's API, which uses image processing and natural language processing algorithms to determine the user's emotional state. The result is then sent to the backend API, and the server executes an SQL query to store the received data in a database.

[1049] Step 4:

[1050] Story Generation

[1051] Inputs: User profile data, up-to-date real-time data, sentiment data

[1052] Processing: The server receives a story request from the device at the specified time, retrieves the user profile, real-time data, and emotion data from the database, and then inputs the prompt sentence into the generative AI model to generate the story.

[1053] Output: Send the generated story from the server to the device.

[1054] Specific operation: At the specified time, the device sends a story request to the backend API. The server executes an SQL query to obtain the necessary data and inputs the following prompt statement into the generative AI model (e.g., GPT-3): "This user is a 35-year-old male who likes science fiction. He is currently at home and feeling stressed because it's raining outside. Please generate a relaxing science fiction story for him." The generated story is then sent to the device via the backend API.

[1055] Step 5:

[1056] Distributing and viewing stories

[1057] Input: Story sent from the server

[1058] Processing: The device notifies the user of the received story, and the user opens the app to read the story.

[1059] Output: The story is displayed to the user.

[1060] What happens: The device sends a push notification to the user to let them know that a story has been delivered. The user taps the app to launch it and view the received story.

[1061] Step 6:

[1062] Gathering feedback

[1063] Input: User's rating and comments on the story

[1064] Processing: The device collects feedback from the user and sends it to the server.

[1065] Output: The server receives the feedback and stores it in a database. The feedback is reflected in future story generation.

[1066] Specific operation: After reading the story, the device displays a rating form, and the user enters their rating and comments. The input data is sent to the backend API by pressing the submit button, and the server stores the received data in a database and uses it for the next story generation.

[1067] (Application example 2)

[1068] 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."

[1069] Conventional ad delivery systems could only provide ads based on basic user attribute information, and were unable to provide personalized ads that took into account the user's momentary emotions and real-time circumstances. This limited the effectiveness of ads and made it difficult to attract user interest. Furthermore, they lacked a mechanism for effectively utilizing feedback to improve the quality of ads.

[1070] 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.

[1071] In this invention, the server includes: means for collecting user information; means for acquiring real-time data; means for generating individually optimized advertising stories based on the collected user information and real-time data; means including an emotion engine for collecting and analyzing user emotion data; means for delivering the generated advertising stories to a display terminal; means for collecting user feedback; and means for adjusting the advertising story generation process based on the feedback. This allows for the generation of advertisements that take into account the user's momentary emotions and real-time situations, enabling more effective and personalized advertisement delivery. Furthermore, the quality of advertisements can be continuously improved by utilizing the feedback.

[1072] "Means for collecting user information" refers to hardware or software used to collect basic user attribute information (e.g., name, age, gender, interests, etc.).

[1073] "Means for obtaining real-time data" refers to hardware or software for periodically obtaining the latest data such as the user's current location, time, and weather information.

[1074] "Means for generating individually optimized advertising stories based on collected user information and real-time data" refers to hardware or software that has algorithms or generative AI models for analyzing collected user information and real-time data and generating advertising content optimized for each individual user.

[1075] An "emotion engine that collects and analyzes user emotional data" is hardware or software that includes an AI engine that analyzes a user's facial expressions, voice, text data, etc., and recognizes their emotional state.

[1076] "Means for delivering the generated advertising story to a display device" refers to hardware or software for transmitting and displaying the generated advertising story on a display device such as a user's smartphone, tablet, or PC.

[1077] "Means for collecting user feedback" means hardware or software for collecting ratings and comments made by users on advertising stories.

[1078] A "means for adjusting the advertising story generation process based on feedback" is hardware or software for analyzing user feedback and, based thereon, improving how future advertising stories are generated.

[1079] This invention is a system that generates individually optimized advertising stories based on user information, real-time data, and emotional data, and provides users with a personalized advertising experience. Specific methods for implementing this system are described below.

[1080] System configuration

[1081] The system includes the following major components:

[1082] User devices: smartphones, tablets, computers, etc. User information is entered, real-time data is acquired, emotion data is collected, advertising stories are received and displayed, and feedback is sent.

[1083] Server: The central system that processes data and generates advertising stories. It manages user information, real-time data, and emotional data, generates advertising stories, and receives and analyzes feedback.

[1084] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated advertising stories, and feedback.

[1085] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize emotions.

[1086] System Operation Overview

[1087] 1. Collection of User Information

[1088] When a user uses the system for the first time, they enter basic information such as their name, age, gender, and areas of interest. This information is sent to the server via the user's device and stored in a database.

[1089] 2. Obtaining real-time data

[1090] The user device periodically obtains the current location, time, and weather information and sends it to the server, which stores this real-time data in a database.

[1091] 3. Collecting Emotional Data

[1092] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database.

[1093] 4. Advertising story generation

[1094] The user device sends a request to the server to generate an advertising story at a specified time. The server obtains the user's profile, the latest real-time data, and emotional data, and uses an artificial intelligence engine to generate the optimal advertising story based on this. The generated advertising story is then sent to the user device.

[1095] 5. Delivery and display of advertising stories

[1096] The user terminal notifies the user of the advertising story received from the server and displays it through the application.

[1097] 6. Gathering Feedback

[1098] The user device displays an interface that asks the user to rate and provide feedback on the advertising story. The user's feedback is sent to the server and stored in a database. The feedback information is reflected in future advertising story generation.

[1099] Specific examples

[1100] For example, for a user with the following profile:

[1101] User: Taro Yamada, 45 years old, male, loves technology

[1102] Time: 3pm, office

[1103] Location: Office building

[1104] Weather: Sunny

[1105] Emotional state: Focused

[1106] For this user, we generate an advertising story for "latest technology products" that will help them stay focused in an office environment.

[1107] Prompt Sentence Examples

[1108] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[1109] This system makes it possible to provide personalized ads that reflect the user's momentary emotions and real-time situations, and also to use feedback to improve the quality of ads.

[1110] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1111] Step 1:

[1112] Collection of User Information

[1113] When a user first uses the system, they enter basic information such as their name, age, gender, and areas of interest. The entered information is sent by the user's device to a server, which receives the information and stores it in a database. In this process, the entered data is used to generate a basic profile to understand the user's interests.

[1114] Step 2:

[1115] Real-time data acquisition

[1116] The user device periodically obtains the user's current location, time, and weather information. This information is updated in real time and sent to the server. The server stores this real-time data in a database. Using the real-time data (location, time, and weather) as input, the server processes the data to understand the user's current situation.

[1117] Step 3:

[1118] Collecting Emotional Data

[1119] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database. The input data is analyzed to output the user's emotional state and store it as emotion data.

[1120] Step 4:

[1121] Advertising story generation

[1122] Each day, a user device sends a request to the server at a specified time to generate an advertising story. The server receives the request and retrieves the user's profile, the latest real-time data, and sentiment data from the database. The generative AI model generates an advertising story based on this data. The profile data, real-time data, and sentiment data are analyzed and integrated to generate an individually optimized advertising story as output.

[1123] Step 5:

[1124] Ad story distribution and display

[1125] The server sends the generated advertising story to the user terminal, which receives the advertising story, notifies the user, and displays it through the application, receiving the generated advertising story as input and outputting it as notification and display data.

[1126] Step 6:

[1127] Gathering feedback

[1128] The user device displays an interface that requests the user to rate and provide feedback on the advertising story. The user device sends the feedback to the server, which receives the feedback and stores it in a database. The feedback information is collected as input for use in generating the next advertising story, and stored as output in the feedback database.

[1129] Examples include the following:

[1130] For example, if the user is "Taro Yamada, 45 years old, male, loves technology" and is concentrating in a sunny office building at 3 p.m., an advertising story for a "personalized technology product" will be automatically generated and delivered to the user.

[1131] Prompt Sentence Examples

[1132] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[1133] 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.

[1134] 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.

[1135] 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.

[1136] [Third embodiment]

[1137] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1138] 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.

[1139] 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).

[1140] 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.

[1141] 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.

[1142] 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).

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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."

[1149] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[1150] System Configuration

[1151] The system includes the following major components:

[1152] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1153] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1154] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1155] System Operation Overview

[1156] 1. Collection of User Information

[1157] When a user uses the app for the first time, they enter information such as their name, age, gender, and favorite genres.

[1158] (Device) sends this information to the server.

[1159] (Server) receives the information and creates and stores the user's profile in a database.

[1160] 2. Obtaining real-time data

[1161] (Device) periodically obtains the user's location, time, and weather information.

[1162] (Device) transmits the acquired real-time data to the server.

[1163] (Server) receives real-time data and stores it in a database.

[1164] 3. Story Generation

[1165] (Device) sends a story request to the server at a specified time every day.

[1166] (Server) receives the request and retrieves the user profile and latest real-time data from the database.

[1167] (The server) uses an artificial intelligence engine to generate stories based on user profiles and real-time data.

[1168] For example, if it determines that a user likes mysteries and is on their way to work in the morning, it will generate a story themed around a mystery incident that occurs on the way to work.

[1169] (Server) sends the generated story to the device.

[1170] 4. Story Distribution and Display

[1171] (Device) notifies the user of the story received from the server.

[1172] (User) opens the app and starts reading a story.

[1173] 5. Gathering Feedback

[1174] (Device) displays an interface to the user requesting a rating and feedback on the story.

[1175] (User) enters a rating (star rating, comment) for the story.

[1176] (Device) sends user feedback to the server.

[1177] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[1178] Examples:

[1179] User: Taro Tanaka, 30 years old, male, loves mystery novels

[1180] Time: 7am, commute time

[1181] Location: Near Tokyo Station

[1182] Weather: Sunny

[1183] 1. Collection of User Information

[1184] (Taro Tanaka) enters "Taro Tanaka," "30 years old," "male," and "likes mysteries."

[1185] (Device) sends this information to the server.

[1186] (Server) receives the information and creates and stores Tanaka's profile in the database.

[1187] 2. Obtaining real-time data

[1188] (Device) periodically obtains Tanaka's current location information (near Tokyo Station) and weather information (sunny).

[1189] (Device) sends location and weather information to the server.

[1190] (Server) receives this information and stores it in a database.

[1191] 3. Story Generation

[1192] (Device) sends a story request to the server at 7am.

[1193] (Server) receives the request and retrieves Tanaka's profile and the latest real-time data.

[1194] (Server) uses an artificial intelligence engine based on the conditions "I like mysteries," "On my way to work," "Tokyo Station," and "Sunny weather" to generate a story called "A mysterious incident that takes place at Tokyo Station on a sunny morning."

[1195] (Server) sends the generated story to Tanaka's device.

[1196] 4. Story Distribution and Display

[1197] (Device) receives the story and notifies Tanaka.

[1198] (Tanaka) opens the app and reads the story.

[1199] 5. Gathering Feedback

[1200] (Device) asks Tanaka to rate and comment on the story.

[1201] (Tanaka) enters feedback such as "5 stars" and "Very interesting!"

[1202] (Device) sends feedback to the server.

[1203] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[1204] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[1208] Step 2:

[1209] (Device) collects information entered by the user and sends it to the server.

[1210] Step 3:

[1211] (Server) creates a user profile based on the received user information and stores it in the database.

[1212] Step 4:

[1213] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[1214] Step 5:

[1215] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[1216] Step 6:

[1217] (Server) receives real-time data and stores it in the user's status database.

[1218] Step 7:

[1219] (Device) sends a story request to the server at a specified time every day (e.g. 7am).

[1220] Step 8:

[1221] (Server) receives the story request and retrieves the user's profile and the latest real-time data from the database.

[1222] Step 9:

[1223] The (server) launches an artificial intelligence engine and provides the user's profile (e.g., "I like mysteries") and real-time data (e.g., Tokyo Station, sunny weather) as input.

[1224] Step 10:

[1225] (Artificial Intelligence Engine) generates a story based on specified parameters (e.g., a mysterious incident that occurs at Tokyo Station on a sunny morning).

[1226] Step 11:

[1227] The server retrieves the generated story and associates it with the user's profile.

[1228] Step 12:

[1229] (Server) sends the generated story to the user's device.

[1230] Step 13:

[1231] (Device) notifies the user of stories received.

[1232] Step 14:

[1233] (User) sees the notification, opens the app and reads the story.

[1234] Step 15:

[1235] (Device) displays an interface to the user asking them to rate or comment on the story.

[1236] Step 16:

[1237] (User) enters a rating (star rating, comment) for the story.

[1238] Step 17:

[1239] (Device) sends user feedback to the server.

[1240] Step 18:

[1241] (Server) receives the feedback and stores it in a database.

[1242] Step 19:

[1243] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[1244] Example 1

[1245] 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."

[1246] Conventional story generation systems lacked sufficient personalization based on individual user interests and real-time situations, making it difficult to provide content that would capture users' attention. Furthermore, feedback was not collected or reflected efficiently, making it difficult to continuously improve the user experience. This made improving user satisfaction a challenge.

[1247] 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.

[1248] In this invention, the server includes a means for collecting user information, a means for acquiring real-time data, and a means for generating prompt sentences and generating stories using a generative AI model, thereby enabling adaptive story generation based on the user's individual interests and real-time situations.

[1249] "User Information" means data about an individual provided by a User, such as name, age, gender, and interests.

[1250] "Real-time data" refers to data relevant to the current situation, such as the user's location, time, and weather information.

[1251] "Stories" refers to texts and narratives generated based on user profiles and real-time data.

[1252] A "display terminal" is a device operated by a user, including a smartphone, tablet, or PC.

[1253] "Feedback" refers to ratings and comments on stories provided by users, and is data that is reflected in future story generation.

[1254] A "prompt" is a piece of text that is input into a generative AI model, instructing it to generate a story based on specific conditions or settings.

[1255] A "generative AI model" refers to an artificial intelligence algorithm or engine that generates a story based on an input prompt.

[1256] The present invention provides a system for collecting user information, acquiring real-time data, generating individually optimized stories based on the collected information, and providing the stories to users. Specific embodiments of this system are described below.

[1257] System Configuration

[1258] The system mainly includes the following components:

[1259] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1260] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1261] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1262] Collection of User Information

[1263] When a user uses an app for the first time, they enter user information, such as their name, age, gender, and favorite genre. The device sends this information to the server, which then receives it and creates and saves a user profile in a database.

[1264] Real-time data acquisition

[1265] The device periodically obtains the user's location, time, and weather information, allowing it to generate content appropriate to the user's current situation. The device then transmits the obtained real-time data to the server, which then receives the data and stores it in a database.

[1266] Story Generation

[1267] The device sends a story request to the server at a specified time every day. The server receives the request, retrieves the user's profile and the latest real-time data from the database, generates a prompt, and generates a story using a generative AI model.

[1268] As a concrete example, we input the following prompt into the generative AI model:

[1269] "For a 30-year-old man who likes mysteries, please generate a mystery that takes place at Tokyo Station on a sunny morning."

[1270] The server transmits the generated story to the terminal, and the terminal notifies the user of the story.

[1271] Gathering feedback

[1272] After the user finishes reading the story, the device displays an interface asking for feedback. The user enters a rating and comments, and the device sends this feedback to the server. The server receives the feedback and stores it in a database, allowing it to reflect the feedback in future story generation and improve the user experience.

[1273] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[1274] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1275] Step 1: Collect user information

[1276] The user accesses the app and enters their name, age, gender, and favorite genre. For example, they might enter "Yamada Hanako," "25 years old," "female," and "likes romance novels."

[1277] The device sends the entered information to the server. The sent content includes the entered information along with the user ID.

[1278] Input: Information entered by the user ("Hanako Yamada," "25 years old," "female," "likes romance novels")

[1279] Data processing: Convert user information into JSON format

[1280] Output: HTTPS request to the server

[1281] The server receives the information and stores the user profile in its database, for example as "user5678".

[1282] Input: HTTPS request from the terminal

[1283] Data Calculation: Creating and Saving User Profiles

[1284] Output: New user entry in the database

[1285] Step 2: Getting real-time data

[1286] The device periodically obtains location, time, and weather information. For example, every 10 minutes, it obtains "Shinjuku Station" from the GPS, "12:30 PM" from the system clock, and "rain" from the weather API.

[1287] Input: GPS data, system clock, weather API

[1288] Data processing: Format conversion of acquired data

[1289] Output: Packets of acquired data

[1290] The real-time data acquired by the terminal is transmitted to the server.

[1291] Input: Packets of acquired data

[1292] Data processing: JSON conversion of data packets

[1293] Output: HTTPS request to the server

[1294] The server receives the data and stores it in the database under the corresponding user ID.

[1295] Input: HTTPS request from the terminal

[1296] Data calculation: Real-time data storage

[1297] Output: Real-time data entry into a database

[1298] Step 3: Story Generation

[1299] The device sends a story request to the server at a specified time each day, for example, 7:00 AM.

[1300] Input: User ID and timestamp

[1301] Data processing: Creating a request format

[1302] Output: HTTPS request to the server

[1303] The server retrieves the user's profile and the latest real-time data from the database.

[1304] Input: User ID

[1305] Data Computing: Querying user and real-time databases

[1306] Output: User profile and up-to-date real-time data

[1307] The server generates a prompt and uses a generative AI model to generate a story, for example, "Generate a rainy day romance story at Shinjuku Station for a 25-year-old woman who loves romance novels."

[1308] Input: User profile and up-to-date real-time data

[1309] Data Calculation: Prompt Statement Generation

[1310] Output: Input prompts to the generative AI model

[1311] Input: prompt to generative AI model

[1312] Data Calculation: Story Generation

[1313] Output: Generated stories

[1314] The server sends the generated story to the device.

[1315] Input: Generated Story

[1316] Data processing: Format conversion of story data

[1317] Output: HTTPS request to the terminal

[1318] Step 4: Publish and view your story

[1319] Your device will receive and notify you of the story.

[1320] Input: HTTPS request from the server

[1321] Data processing: Analysis of received data

[1322] Output: Notification generated

[1323] A user opens the app and reads a story, for example, "A Rainy Day Romance Story at Shinjuku Station."

[1324] Input: Notifications and story data

[1325] Data processing: Displaying story data

[1326] Output: Story View in User Interface

[1327] Step 5: Gather feedback

[1328] The device displays an interface to the user requesting a rating and feedback.

[1329] Input: End event of story view

[1330] Data processing: Generating feedback input forms

[1331] Output: Evaluation form displayed in the user interface

[1332] The user enters a rating and comment, for example, "5 stars" and "I'm impressed!"

[1333] Input: User ratings and comments

[1334] Data processing: Generating feedback data

[1335] Output: Completed feedback data

[1336] The device sends the feedback to the server.

[1337] Input: Feedback data

[1338] Data processing: Convert data to JSON

[1339] Output: HTTPS request to the server

[1340] The server receives the feedback and stores it in a database, so that the feedback can be reflected in future story generation.

[1341] Input: HTTPS request from the terminal

[1342] Data calculation: feedback data storage

[1343] Output: Feedback entries in the database

[1344] (Application example 1)

[1345] 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."

[1346] Conventional content distribution services have difficulty providing personalized entertainment content that reflects users' interests and real-time situations, resulting in a lack of improvement in the user experience. Additionally, they have been unable to properly reflect user feedback, limiting improvements to the quality of the content they provide.

[1347] 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.

[1348] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for generating individually optimized stories based on the collected user information and real-time data using a generative AI model, means for delivering the generated stories to a display terminal, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the provision of personalized entertainment content based on the user's interests, current location, time of day, and weather.

[1349] "User Information" means personal information provided by a User, such as name, age, gender, interests, and preferences.

[1350] "Real-time data" refers to up-to-date information that changes over time, specifically the user's current location, time, and weather information.

[1351] A "generative AI model" is an artificial intelligence algorithm for generating personalized entertainment content based on collected data.

[1352] The "story generation means" is a system that automatically creates individually optimized stories based on user information and real-time data.

[1353] A "display device" is a device such as a smartphone, tablet, or computer that is used to display the generated story to the user.

[1354] "Feedback collection means" is a system for collecting opinions and impressions such as ratings and comments on stories from users.

[1355] The "story generation process adjustment means" is a method for improving the content and quality of newly generated stories based on user feedback.

[1356] "Personalized entertainment content" refers to an entertainment experience that is optimized based on a user's individual interests and current situation.

[1357] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[1358] System Configuration

[1359] The system includes the following major components:

[1360] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1361] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1362] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1363] System Operation Overview

[1364] 1. Collection of User Information

[1365] When a user uses the application for the first time, they enter information such as their name, age, gender, favorite genres, etc. The user's device sends this information to the server, which then creates and stores the user's profile in a database.

[1366] 2. Obtaining real-time data

[1367] The user device periodically acquires the user's location, time, and weather information, and sends this real-time data to the server, which then stores the received data in a database.

[1368] 3. Story Generation

[1369] The user device sends a story request to the server at a specified time every day. The server receives the request and retrieves the user's profile and the latest real-time data from the database. The server uses the generative AI model to generate a story based on the user's profile and real-time data, and sends the generated story to the user device.

[1370] 4. Story Distribution and Display

[1371] The user device notifies the user of the story received from the server, and the user opens the application to read the story.

[1372] 5. Gathering Feedback

[1373] The user device displays an interface that requests the user to rate and provide feedback on the story. The user enters a rating (star rating, comments), and the user device sends the feedback to the server. The server receives the feedback, stores it in a database, and reflects it in future story generation.

[1374] Hardware and Software Configuration

[1375] Hardware: User devices (smartphones, tablets, PCs) and servers

[1376] software:

[1377] Python: a programming language

[1378] Requests: Used to retrieve data from the weather API

[1379] Geopy: A library for obtaining geolocation information

[1380] AI story generation library (hypothetical)

[1381] Specific examples

[1382] Consider a scenario where the user is a 30-year-old man who likes mystery novels. The user's device sends a story request at 8:00 a.m., sending real-time data to the server indicating that the user's current location is Tokyo Station and the weather is sunny. The server uses a generative AI model to generate a story titled "A mysterious incident unfolds at Tokyo Station on a sunny morning" and provides it to the user.

[1383] Prompt Sentence Examples

[1384] Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny

[1385] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1386] Step 1:

[1387] Collection of User Information

[1388] Input: Information that the user enters into the application for the first time, such as name, age, gender, and favorite genres

[1389] Processing: After the user downloads and installs the application, they enter their user information when they first launch it. The device then sends the entered information to the server.

[1390] Data processing: The server stores the received user information in a database and creates a user profile.

[1391] Output: Created user profile

[1392] Specifically, the user enters their name, age, gender, and favorite genre on the application screen, and the device sends this information to the server, which then receives the information and stores it in a database as a user profile.

[1393] Step 2:

[1394] Real-time data acquisition

[1395] Input: Location, current time, and weather information obtained by the device

[1396] Processing: Periodically, the device retrieves location, time, and weather information using the Geopy library to get location information and the Requests library to get weather information.

[1397] Data processing: The acquired information is converted into an appropriate format and sent to the server.

[1398] Output: Real-time data (location, time, weather information)

[1399] Specifically, the device uses GPS to obtain its current location and the current time from the clock. Weather information is obtained from an external weather API. This data is then sent to the server, which stores it in a database.

[1400] Step 3:

[1401] Request a story

[1402] Input: Periodic requests from the user's device

[1403] Processing: The user device sends a story generation request to the server at the specified time.

[1404] Data processing: Receive the request and retrieve the corresponding user profile and real-time data from the database.

[1405] Output: Story generation request with profile and real-time data

[1406] Specifically, the device sends a request to the server at 8 a.m., and the server receives it and retrieves the user profile and real-time data from the database.

[1407] Step 4:

[1408] Story Generation

[1409] Inputs: User profiles, real-time data, generative AI models

[1410] Processing: The server generates prompts based on the user profile and real-time data, and inputs them into the generative AI model. For example, it generates a prompt like "Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny."

[1411] Data processing: The AI ​​model generates a story based on the given prompts.

[1412] Output: Generated stories

[1413] Specifically, the server creates prompts based on the user profile and real-time data, and then inputs them into a generative AI model to generate a story.

[1414] Step 5:

[1415] Distributing and viewing stories

[1416] Input: Generated stories

[1417] Processing: The server sends the generated story to the device. The device receives the story and notifies the user.

[1418] Data processing: Converting the story into a data format for display on the device.

[1419] Output: The story displayed on the user's device

[1420] Specifically, the server sends the generated story to the terminal via an HTTP request, and the terminal receives it and displays it on the screen.

[1421] Step 6:

[1422] Gathering feedback

[1423] Input: User's rating and comments on the story

[1424] Processing: After the device finishes reading the story, it displays an interface asking the user to rate it. The user enters a rating and comments. The device then sends the entered information to the server.

[1425] Data processing: The server stores the received feedback in a database. The feedback data is analyzed and reflected in future story generation.

[1426] Output: Saved feedback data

[1427] Specifically, after a user finishes reading a story, a rating interface will appear on the screen, and the user can enter a star rating and comments. The device will then send the information to the server, which will then store it in a database.

[1428] 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.

[1429] This invention is a system that generates individually optimized stories based on user information and real-time data, and combines this with an emotion engine that recognizes the user's emotions to provide a more personalized experience. The specific system configuration and operation method are explained below.

[1430] System Configuration

[1431] The system includes the following major components:

[1432] User device: A device operated by a user, including a smartphone, tablet, or PC. It inputs user information, acquires real-time data, collects emotional data, receives and displays stories, and sends feedback.

[1433] Server: The central system that processes data and generates stories. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[1434] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated stories, and feedback.

[1435] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize the user's emotions.

[1436] System Operation Overview

[1437] 1. Collection of User Information

[1438] When a user uses the app for the first time, they enter basic information such as their name, age, gender, and favorite genres.

[1439] (Device) sends this information to the server.

[1440] (Server) receives the information and creates and stores the user profile in a database.

[1441] 2. Obtaining real-time data

[1442] (Device) periodically retrieves the user's current location, time, and weather information.

[1443] (Device) transmits the acquired real-time data to the server.

[1444] (Server) receives real-time data and stores it in a database.

[1445] 3. Collecting Emotional Data

[1446] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[1447] (Emotion engine) analyzes this data and recognizes the user's emotions.

[1448] The emotion engine sends the recognized emotion data to the server.

[1449] (Server) stores the emotion data in a database.

[1450] 4. Story Generation

[1451] (Device) sends a story request to the server at a specified time every day.

[1452] (Server) receives the request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[1453] (Server) uses an artificial intelligence engine to generate stories based on user profiles, real-time data, and emotional data.

[1454] For example, if a user likes mysteries and is aware that they are in a state of psychological tension during their morning commute, a mystery-themed story will be generated to heighten the sense of tension on the way to work.

[1455] (Server) sends the generated story to the device.

[1456] 5. Story Distribution and Display

[1457] (Device) notifies the user of the story received from the server.

[1458] (User) sees the notification, opens the app and starts reading the story.

[1459] 6. Gathering Feedback

[1460] (Device) displays an interface asking the user to rate the story and provide feedback.

[1461] (User) enters a rating (star rating, comment) for the story.

[1462] (Device) sends user feedback to the server.

[1463] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[1464] Examples:

[1465] User: Suzuki Ichiro, 35 years old, male, sci-fi novel lover

[1466] Time: 9pm, relaxation time

[1467] Location: Home

[1468] Weather: Rain

[1469] Emotional state: Feeling stressed

[1470] 1. Collection of User Information

[1471] (Ichiro Suzuki) enters "Ichiro Suzuki," "35 years old," "male," and "likes science fiction."

[1472] (Device) sends this information to the server.

[1473] (Server) receives the information and creates and stores Suzuki's profile in the database.

[1474] 2. Obtaining real-time data

[1475] (Device) periodically obtains Suzuki's current location (home) and weather information (rain).

[1476] (Device) sends location and weather information to the server.

[1477] (Server) receives this information and stores it in a database.

[1478] 3. Collecting Emotional Data

[1479] (Device) sends Suzuki's facial expression data to the emotion engine.

[1480] (Emotion engine) analyzes facial expression data and recognizes that the person is feeling stressed.

[1481] (Emotion engine) sends emotion data to the server.

[1482] (Server) stores the emotion data in a database.

[1483] 4. Story Generation

[1484] (Device) sends a story request to the server at 9:00 PM.

[1485] (Server) receives the request and retrieves Suzuki's profile, latest real-time data, and emotion data.

[1486] The server uses an artificial intelligence engine to generate a "science fiction story to read while relaxing at home on a rainy night" based on the conditions "I like science fiction," "I'm at home," "It's raining," and "I'm feeling stressed."

[1487] (Server) sends the generated story to Suzuki's device.

[1488] 5. Story Distribution and Display

[1489] (Device) receives the story and notifies Suzuki.

[1490] (Suzuki) opens the app and reads the story.

[1491] 6. Gathering Feedback

[1492] (Device) asks Suzuki to rate and comment on the story.

[1493] (Mr. Suzuki) enters feedback as "5 stars" and "Very relaxing!"

[1494] (Device) sends feedback to the server.

[1495] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[1496] In this way, a system is realized that provides personalized stories that take into account the user's emotions.

[1497] The processing flow will be explained below.

[1498] Step 1:

[1499] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[1500] Step 2:

[1501] (Device) collects information entered by the user and sends it to the server.

[1502] Step 3:

[1503] (Server) creates a user profile based on the received user information and stores it in the database.

[1504] Step 4:

[1505] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[1506] Step 5:

[1507] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[1508] Step 6:

[1509] (Server) receives real-time data and stores it in the user's status database.

[1510] Step 7:

[1511] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[1512] Step 8:

[1513] (Emotion engine) analyzes the received data and recognizes the user's emotions.

[1514] Step 9:

[1515] The emotion engine sends the recognized emotion data to the server.

[1516] Step 10:

[1517] (Server) receives the emotion data and stores it in a database.

[1518] Step 11:

[1519] (Device) sends a story request to the server at a specified time every day (e.g. 9:00 PM).

[1520] Step 12:

[1521] (Server) receives the story request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[1522] Step 13:

[1523] The (server) starts an artificial intelligence engine and passes as input the user's profile (e.g., I like science fiction), the latest real-time data (e.g., I'm at home, it's raining), and emotional data (e.g., I'm stressed).

[1524] Step 14:

[1525] (Artificial Intelligence Engine) generates a story based on the parameters you specify (e.g., a sci-fi story to read while relaxing at home on a rainy night).

[1526] Step 15:

[1527] The server retrieves the generated story and associates it with the user's profile.

[1528] Step 16:

[1529] (Server) sends the generated story to the user's device.

[1530] Step 17:

[1531] (Device) notifies the user of stories received.

[1532] Step 18:

[1533] (User) sees the notification, opens the app and reads the story.

[1534] Step 19:

[1535] (Device) displays an interface to the user asking them to rate or comment on the story.

[1536] Step 20:

[1537] (User) enters a rating (star rating, comment) for the story.

[1538] Step 21:

[1539] (Device) sends user feedback to the server.

[1540] Step 22:

[1541] (Server) receives the feedback and stores it in a database.

[1542] Step 23:

[1543] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[1544] Example 2

[1545] 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."

[1546] Existing story generation systems have difficulty providing optimized content based on a user's individual preferences, real-time situation, and emotions. Furthermore, they lack a built-in story generation process that reflects the user's emotions in real time, resulting in an insufficiently personalized user experience.

[1547] 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.

[1548] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for recognizing emotions by analyzing facial expression data, voice data, and text data, means for generating individually optimized stories based on the collected user information, real-time data, and emotional data, means for delivering the generated stories to user terminals, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the generation and delivery of personalized stories based on the individual information and emotional state of the user.

[1549] "User Information" refers to basic data provided by a user when they start using an application, and specifically includes information such as their name, age, gender, and favorite genres.

[1550] "Real-time data" is data obtained at a specific time and place, and includes location information, time, weather information, and the like.

[1551] "Emotional data" refers to data that indicates the emotional state of a user obtained by analyzing their facial expressions, voice, and text data.

[1552] A "user profile" is a data set for an individual user that is generated based on user information and stored in a database.

[1553] "Story generation" is the process of creating individually optimized stories based on collected user information, real-time data, and sentiment data.

[1554] "Feedback" refers to response data provided by users, such as ratings and comments on stories.

[1555] A "generative AI model" is a system that includes algorithms that automatically generate stories and content based on input data, for example, using natural language processing or machine learning techniques.

[1556] A "prompt" is an instruction entered into a generative AI model to specify the content and direction of the story to be generated.

[1557] This invention is a system that generates individually optimized stories based on user information and real-time data. The system incorporates an emotion engine that recognizes user emotions, enabling it to provide a more personalized experience. The system is primarily composed of components such as a user terminal, a server, a database, and an emotion engine.

[1558] System configuration:

[1559] User device:

[1560] A device operated by a user, such as a smartphone, tablet, or PC, that inputs user information, obtains real-time data, collects emotional data, receives and displays stories, and sends feedback.

[1561] Examples:

[1562] The user installs the application the first time they use it, and it uses the device's camera and microphone to collect facial expression and voice data. It also uses the GPS function to obtain location information. For example, it is commonly used as a smartphone application.

[1563] server:

[1564] This is the central system for data processing and story generation. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[1565] Examples:

[1566] We use frameworks such as Python and Django to build a backend API to manage and analyze incoming data. We use OpenAI's GPT-3 as a generative AI model to generate stories by inputting prompt sentences.

[1567] Database:

[1568] It is installed on a server and stores data such as user information, real-time data, emotional data, generated stories, and feedback.

[1569] Examples:

[1570] Data is stored and retrieved using SQL queries in database management systems such as MySQL or PostgreSQL. User profiles and feedback are used to inform future story generation.

[1571] Emotion Engine:

[1572] This is a system that analyzes a user's facial expression data, voice data, and text data to recognize the user's emotions.

[1573] Examples:

[1574] Using Google Cloud Vision API and IBM Watson's sentiment analysis API, the collected data is analyzed to generate emotion data, which is then sent to a server and stored in a database.

[1575] Examples and prompts:

[1576] Examples:

[1577] User: 35-year-old male, sci-fi fan

[1578] Real-time data: At home, time is 9pm, weather is raining

[1579] Emotional state: Stressed

[1580] Example prompt sentence:

[1581] "Generate a relaxing sci-fi story for this user who is a 35-year-old male who likes sci-fi, is currently at home, and is feeling stressed because it's raining outside."

[1582] In this way, a system is realized that takes into account user emotions and real-time data to generate individually optimized stories and provide users with a personalized experience.

[1583] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] Collection of User Information

[1586] Input: Basic information entered by the user, such as name, age, gender, and favorite genres

[1587] Processing: When a user first uses the app, they enter basic information such as their name, age, gender, and favorite genres. The device then sends this information to the server.

[1588] Output: The server stores the received user information in a database and creates a user profile.

[1589] How it works: When a user enters information into the input form displayed on the app screen and presses the submit button, the device sends the data to the backend API, and the server executes an SQL query to store the received data in the database.

[1590] Step 2:

[1591] Real-time data acquisition

[1592] Input: User's current location, time, and weather information

[1593] Processing: The device periodically obtains the user's location, time, and weather information and sends it to the server.

[1594] Output: The server stores the received real-time data in a database.

[1595] Specific operation: Obtain location information from the device's GPS sensor and time information from the system clock. Obtain weather information using an external API (e.g., OpenWeatherMap). Send this data to a backend API on a regular timer, and the server executes an SQL query to store the received data in a database.

[1596] Step 3:

[1597] Collecting Emotional Data

[1598] Input: User's facial expression data, voice data, text data

[1599] Processing: The device sends this data to the emotion engine and receives the analysis results.

[1600] Output: The emotion engine sends the analyzed emotion data to the server, which stores it in a database.

[1601] How it works: It uses the device's camera, microphone, and input devices to collect facial expression, voice, and text data. This data is then sent to the emotion engine's API, which uses image processing and natural language processing algorithms to determine the user's emotional state. The result is then sent to the backend API, and the server executes an SQL query to store the received data in a database.

[1602] Step 4:

[1603] Story Generation

[1604] Inputs: User profile data, up-to-date real-time data, sentiment data

[1605] Processing: The server receives a story request from the device at the specified time, retrieves the user profile, real-time data, and emotion data from the database, and then inputs the prompt sentence into the generative AI model to generate the story.

[1606] Output: Send the generated story from the server to the device.

[1607] Specific operation: At the specified time, the device sends a story request to the backend API. The server executes an SQL query to obtain the necessary data and inputs the following prompt statement into the generative AI model (e.g., GPT-3): "This user is a 35-year-old male who likes science fiction. He is currently at home and feeling stressed because it's raining outside. Please generate a relaxing science fiction story for him." The generated story is then sent to the device via the backend API.

[1608] Step 5:

[1609] Distributing and viewing stories

[1610] Input: Story sent from the server

[1611] Processing: The device notifies the user of the received story, and the user opens the app to read the story.

[1612] Output: The story is displayed to the user.

[1613] What happens: The device sends a push notification to the user to let them know that a story has been delivered. The user taps the app to launch it and view the received story.

[1614] Step 6:

[1615] Gathering feedback

[1616] Input: User's rating and comments on the story

[1617] Processing: The device collects feedback from the user and sends it to the server.

[1618] Output: The server receives the feedback and stores it in a database. The feedback is reflected in future story generation.

[1619] Specific operation: After reading the story, the device displays a rating form, and the user enters their rating and comments. The input data is sent to the backend API by pressing the submit button, and the server stores the received data in a database and uses it for the next story generation.

[1620] (Application example 2)

[1621] 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."

[1622] Conventional ad delivery systems could only provide ads based on basic user attribute information, and were unable to provide personalized ads that took into account the user's momentary emotions and real-time circumstances. This limited the effectiveness of ads and made it difficult to attract user interest. Furthermore, they lacked a mechanism for effectively utilizing feedback to improve the quality of ads.

[1623] 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.

[1624] In this invention, the server includes: means for collecting user information; means for acquiring real-time data; means for generating individually optimized advertising stories based on the collected user information and real-time data; means including an emotion engine for collecting and analyzing user emotion data; means for delivering the generated advertising stories to a display terminal; means for collecting user feedback; and means for adjusting the advertising story generation process based on the feedback. This allows for the generation of advertisements that take into account the user's momentary emotions and real-time situations, enabling more effective and personalized advertisement delivery. Furthermore, the quality of advertisements can be continuously improved by utilizing the feedback.

[1625] "Means for collecting user information" refers to hardware or software used to collect basic user attribute information (e.g., name, age, gender, interests, etc.).

[1626] "Means for obtaining real-time data" refers to hardware or software for periodically obtaining the latest data such as the user's current location, time, and weather information.

[1627] "Means for generating individually optimized advertising stories based on collected user information and real-time data" refers to hardware or software that has algorithms or generative AI models for analyzing collected user information and real-time data and generating advertising content optimized for each individual user.

[1628] An "emotion engine that collects and analyzes user emotional data" is hardware or software that includes an AI engine that analyzes a user's facial expressions, voice, text data, etc., and recognizes their emotional state.

[1629] "Means for delivering the generated advertising story to a display device" refers to hardware or software for transmitting and displaying the generated advertising story on a display device such as a user's smartphone, tablet, or PC.

[1630] "Means for collecting user feedback" means hardware or software for collecting ratings and comments made by users on advertising stories.

[1631] A "means for adjusting the advertising story generation process based on feedback" is hardware or software for analyzing user feedback and, based thereon, improving how future advertising stories are generated.

[1632] This invention is a system that generates individually optimized advertising stories based on user information, real-time data, and emotional data, and provides users with a personalized advertising experience. Specific methods for implementing this system are described below.

[1633] System configuration

[1634] The system includes the following major components:

[1635] User devices: smartphones, tablets, computers, etc. User information is entered, real-time data is acquired, emotion data is collected, advertising stories are received and displayed, and feedback is sent.

[1636] Server: The central system that processes data and generates advertising stories. It manages user information, real-time data, and emotional data, generates advertising stories, and receives and analyzes feedback.

[1637] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated advertising stories, and feedback.

[1638] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize emotions.

[1639] System Operation Overview

[1640] 1. Collection of User Information

[1641] When a user uses the system for the first time, they enter basic information such as their name, age, gender, and areas of interest. This information is sent to the server via the user's device and stored in a database.

[1642] 2. Obtaining real-time data

[1643] The user device periodically obtains the current location, time, and weather information and sends it to the server, which stores this real-time data in a database.

[1644] 3. Collecting Emotional Data

[1645] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database.

[1646] 4. Advertising story generation

[1647] The user device sends a request to the server to generate an advertising story at a specified time. The server obtains the user's profile, the latest real-time data, and emotional data, and uses an artificial intelligence engine to generate the optimal advertising story based on this. The generated advertising story is then sent to the user device.

[1648] 5. Delivery and display of advertising stories

[1649] The user terminal notifies the user of the advertising story received from the server and displays it through the application.

[1650] 6. Gathering Feedback

[1651] The user device displays an interface that asks the user to rate and provide feedback on the advertising story. The user's feedback is sent to the server and stored in a database. The feedback information is reflected in future advertising story generation.

[1652] Specific examples

[1653] For example, for a user with the following profile:

[1654] User: Taro Yamada, 45 years old, male, loves technology

[1655] Time: 3pm, office

[1656] Location: Office building

[1657] Weather: Sunny

[1658] Emotional state: Focused

[1659] For this user, we generate an advertising story for "latest technology products" that will help them stay focused in an office environment.

[1660] Prompt Sentence Examples

[1661] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[1662] This system makes it possible to provide personalized ads that reflect the user's momentary emotions and real-time situations, and also to use feedback to improve the quality of ads.

[1663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1664] Step 1:

[1665] Collection of User Information

[1666] When a user first uses the system, they enter basic information such as their name, age, gender, and areas of interest. The entered information is sent by the user's device to a server, which receives the information and stores it in a database. In this process, the entered data is used to generate a basic profile to understand the user's interests.

[1667] Step 2:

[1668] Real-time data acquisition

[1669] The user device periodically obtains the user's current location, time, and weather information. This information is updated in real time and sent to the server. The server stores this real-time data in a database. Using the real-time data (location, time, and weather) as input, the server processes the data to understand the user's current situation.

[1670] Step 3:

[1671] Collecting Emotional Data

[1672] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database. The input data is analyzed to output the user's emotional state and store it as emotion data.

[1673] Step 4:

[1674] Advertising story generation

[1675] Each day, a user device sends a request to the server at a specified time to generate an advertising story. The server receives the request and retrieves the user's profile, the latest real-time data, and sentiment data from the database. The generative AI model generates an advertising story based on this data. The profile data, real-time data, and sentiment data are analyzed and integrated to generate an individually optimized advertising story as output.

[1676] Step 5:

[1677] Ad story distribution and display

[1678] The server sends the generated advertising story to the user terminal, which receives the advertising story, notifies the user, and displays it through the application, receiving the generated advertising story as input and outputting it as notification and display data.

[1679] Step 6:

[1680] Gathering feedback

[1681] The user device displays an interface that requests the user to rate and provide feedback on the advertising story. The user device sends the feedback to the server, which receives the feedback and stores it in a database. The feedback information is collected as input for use in generating the next advertising story, and stored as output in the feedback database.

[1682] Examples include the following:

[1683] For example, if the user is "Taro Yamada, 45 years old, male, loves technology" and is concentrating in a sunny office building at 3 p.m., an advertising story for a "personalized technology product" will be automatically generated and delivered to the user.

[1684] Prompt Sentence Examples

[1685] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[1686] 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.

[1687] 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.

[1688] 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.

[1689] [Fourth embodiment]

[1690] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1691] 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.

[1692] 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).

[1693] 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.

[1694] 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.

[1695] 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).

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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."

[1703] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[1704] System Configuration

[1705] The system includes the following major components:

[1706] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1707] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1708] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1709] System Operation Overview

[1710] 1. Collection of User Information

[1711] When a user uses the app for the first time, they enter information such as their name, age, gender, and favorite genres.

[1712] (Device) sends this information to the server.

[1713] (Server) receives the information and creates and stores the user's profile in a database.

[1714] 2. Obtaining real-time data

[1715] (Device) periodically obtains the user's location, time, and weather information.

[1716] (Device) transmits the acquired real-time data to the server.

[1717] (Server) receives real-time data and stores it in a database.

[1718] 3. Story Generation

[1719] (Device) sends a story request to the server at a specified time every day.

[1720] (Server) receives the request and retrieves the user profile and latest real-time data from the database.

[1721] (The server) uses an artificial intelligence engine to generate stories based on user profiles and real-time data.

[1722] For example, if it determines that a user likes mysteries and is on their way to work in the morning, it will generate a story themed around a mystery incident that occurs on the way to work.

[1723] (Server) sends the generated story to the device.

[1724] 4. Story Distribution and Display

[1725] (Device) notifies the user of the story received from the server.

[1726] (User) opens the app and starts reading a story.

[1727] 5. Gathering Feedback

[1728] (Device) displays an interface to the user requesting a rating and feedback on the story.

[1729] (User) enters a rating (star rating, comment) for the story.

[1730] (Device) sends user feedback to the server.

[1731] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[1732] Examples:

[1733] User: Taro Tanaka, 30 years old, male, loves mystery novels

[1734] Time: 7am, commute time

[1735] Location: Near Tokyo Station

[1736] Weather: Sunny

[1737] 1. Collection of User Information

[1738] (Taro Tanaka) enters "Taro Tanaka," "30 years old," "male," and "likes mysteries."

[1739] (Device) sends this information to the server.

[1740] (Server) receives the information and creates and stores Tanaka's profile in the database.

[1741] 2. Obtaining real-time data

[1742] (Device) periodically obtains Tanaka's current location information (near Tokyo Station) and weather information (sunny).

[1743] (Device) sends location and weather information to the server.

[1744] (Server) receives this information and stores it in a database.

[1745] 3. Story Generation

[1746] (Device) sends a story request to the server at 7am.

[1747] (Server) receives the request and retrieves Tanaka's profile and the latest real-time data.

[1748] (Server) uses an artificial intelligence engine based on the conditions "I like mysteries," "On my way to work," "Tokyo Station," and "Sunny weather" to generate a story called "A mysterious incident that takes place at Tokyo Station on a sunny morning."

[1749] (Server) sends the generated story to Tanaka's device.

[1750] 4. Story Distribution and Display

[1751] (Device) receives the story and notifies Tanaka.

[1752] (Tanaka) opens the app and reads the story.

[1753] 5. Gathering Feedback

[1754] (Device) asks Tanaka to rate and comment on the story.

[1755] (Tanaka) enters feedback such as "5 stars" and "Very interesting!"

[1756] (Device) sends feedback to the server.

[1757] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[1758] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[1759] The processing flow will be explained below.

[1760] Step 1:

[1761] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[1762] Step 2:

[1763] (Device) collects information entered by the user and sends it to the server.

[1764] Step 3:

[1765] (Server) creates a user profile based on the received user information and stores it in the database.

[1766] Step 4:

[1767] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[1768] Step 5:

[1769] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[1770] Step 6:

[1771] (Server) receives real-time data and stores it in the user's status database.

[1772] Step 7:

[1773] (Device) sends a story request to the server at a specified time every day (e.g. 7am).

[1774] Step 8:

[1775] (Server) receives the story request and retrieves the user's profile and the latest real-time data from the database.

[1776] Step 9:

[1777] The (server) launches an artificial intelligence engine and provides the user's profile (e.g., "I like mysteries") and real-time data (e.g., Tokyo Station, sunny weather) as input.

[1778] Step 10:

[1779] (Artificial Intelligence Engine) generates a story based on specified parameters (e.g., a mysterious incident that occurs at Tokyo Station on a sunny morning).

[1780] Step 11:

[1781] The server retrieves the generated story and associates it with the user's profile.

[1782] Step 12:

[1783] (Server) sends the generated story to the user's device.

[1784] Step 13:

[1785] (Device) notifies the user of stories received.

[1786] Step 14:

[1787] (User) sees the notification, opens the app and reads the story.

[1788] Step 15:

[1789] (Device) displays an interface to the user asking them to rate or comment on the story.

[1790] Step 16:

[1791] (User) enters a rating (star rating, comment) for the story.

[1792] Step 17:

[1793] (Device) sends user feedback to the server.

[1794] Step 18:

[1795] (Server) receives the feedback and stores it in a database.

[1796] Step 19:

[1797] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[1798] Example 1

[1799] 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."

[1800] Conventional story generation systems lacked sufficient personalization based on individual user interests and real-time situations, making it difficult to provide content that would capture users' attention. Furthermore, feedback was not collected or reflected efficiently, making it difficult to continuously improve the user experience. This made improving user satisfaction a challenge.

[1801] 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.

[1802] In this invention, the server includes a means for collecting user information, a means for acquiring real-time data, and a means for generating prompt sentences and generating stories using a generative AI model, thereby enabling adaptive story generation based on the user's individual interests and real-time situations.

[1803] "User Information" means data about an individual provided by a User, such as name, age, gender, and interests.

[1804] "Real-time data" refers to data relevant to the current situation, such as the user's location, time, and weather information.

[1805] "Stories" refers to texts and narratives generated based on user profiles and real-time data.

[1806] A "display terminal" is a device operated by a user, including a smartphone, tablet, or PC.

[1807] "Feedback" refers to ratings and comments on stories provided by users, and is data that is reflected in future story generation.

[1808] A "prompt" is a piece of text that is input into a generative AI model, instructing it to generate a story based on specific conditions or settings.

[1809] A "generative AI model" refers to an artificial intelligence algorithm or engine that generates a story based on an input prompt.

[1810] The present invention provides a system for collecting user information, acquiring real-time data, generating individually optimized stories based on the collected information, and providing the stories to users. Specific embodiments of this system are described below.

[1811] System Configuration

[1812] The system mainly includes the following components:

[1813] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1814] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1815] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1816] Collection of User Information

[1817] When a user uses an app for the first time, they enter user information, such as their name, age, gender, and favorite genre. The device sends this information to the server, which then receives it and creates and saves a user profile in a database.

[1818] Real-time data acquisition

[1819] The device periodically obtains the user's location, time, and weather information, allowing it to generate content appropriate to the user's current situation. The device then transmits the obtained real-time data to the server, which then receives the data and stores it in a database.

[1820] Story Generation

[1821] The device sends a story request to the server at a specified time every day. The server receives the request, retrieves the user's profile and the latest real-time data from the database, generates a prompt, and generates a story using a generative AI model.

[1822] As a concrete example, we input the following prompt into the generative AI model:

[1823] "For a 30-year-old man who likes mysteries, please generate a mystery that takes place at Tokyo Station on a sunny morning."

[1824] The server transmits the generated story to the terminal, and the terminal notifies the user of the story.

[1825] Gathering feedback

[1826] After the user finishes reading the story, the device displays an interface asking for feedback. The user enters a rating and comments, and the device sends this feedback to the server. The server receives the feedback and stores it in a database, allowing it to reflect the feedback in future story generation and improve the user experience.

[1827] In this way, a system is realized that provides personalized stories that correspond to each user's interests and real-time situations.

[1828] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1829] Step 1: Collect user information

[1830] The user accesses the app and enters their name, age, gender, and favorite genre. For example, they might enter "Yamada Hanako," "25 years old," "female," and "likes romance novels."

[1831] The device sends the entered information to the server. The sent content includes the entered information along with the user ID.

[1832] Input: Information entered by the user ("Hanako Yamada," "25 years old," "female," "likes romance novels")

[1833] Data processing: Convert user information into JSON format

[1834] Output: HTTPS request to the server

[1835] The server receives the information and stores the user profile in its database, for example as "user5678".

[1836] Input: HTTPS request from the terminal

[1837] Data Calculation: Creating and Saving User Profiles

[1838] Output: New user entry in the database

[1839] Step 2: Getting real-time data

[1840] The device periodically obtains location, time, and weather information. For example, every 10 minutes, it obtains "Shinjuku Station" from the GPS, "12:30 PM" from the system clock, and "rain" from the weather API.

[1841] Input: GPS data, system clock, weather API

[1842] Data processing: Format conversion of acquired data

[1843] Output: Packets of acquired data

[1844] The real-time data acquired by the terminal is transmitted to the server.

[1845] Input: Packets of acquired data

[1846] Data processing: JSON conversion of data packets

[1847] Output: HTTPS request to the server

[1848] The server receives the data and stores it in the database under the corresponding user ID.

[1849] Input: HTTPS request from the terminal

[1850] Data calculation: Real-time data storage

[1851] Output: Real-time data entry into a database

[1852] Step 3: Story Generation

[1853] The device sends a story request to the server at a specified time each day, for example, 7:00 AM.

[1854] Input: User ID and timestamp

[1855] Data processing: Creating a request format

[1856] Output: HTTPS request to the server

[1857] The server retrieves the user's profile and the latest real-time data from the database.

[1858] Input: User ID

[1859] Data Computing: Querying user and real-time databases

[1860] Output: User profile and up-to-date real-time data

[1861] The server generates a prompt and uses a generative AI model to generate a story, for example, "Generate a rainy day romance story at Shinjuku Station for a 25-year-old woman who loves romance novels."

[1862] Input: User profile and up-to-date real-time data

[1863] Data Calculation: Prompt Statement Generation

[1864] Output: Input prompts to the generative AI model

[1865] Input: prompt to generative AI model

[1866] Data Calculation: Story Generation

[1867] Output: Generated stories

[1868] The server sends the generated story to the device.

[1869] Input: Generated Story

[1870] Data processing: Format conversion of story data

[1871] Output: HTTPS request to the terminal

[1872] Step 4: Publish and view your story

[1873] Your device will receive and notify you of the story.

[1874] Input: HTTPS request from the server

[1875] Data processing: Analysis of received data

[1876] Output: Notification generated

[1877] A user opens the app and reads a story, for example, "A Rainy Day Romance Story at Shinjuku Station."

[1878] Input: Notifications and story data

[1879] Data processing: Displaying story data

[1880] Output: Story View in User Interface

[1881] Step 5: Gather feedback

[1882] The device displays an interface to the user requesting a rating and feedback.

[1883] Input: End event of story view

[1884] Data processing: Generating feedback input forms

[1885] Output: Evaluation form displayed in the user interface

[1886] The user enters a rating and comment, for example, "5 stars" and "I'm impressed!"

[1887] Input: User ratings and comments

[1888] Data processing: Generating feedback data

[1889] Output: Completed feedback data

[1890] The device sends the feedback to the server.

[1891] Input: Feedback data

[1892] Data processing: Convert data to JSON

[1893] Output: HTTPS request to the server

[1894] The server receives the feedback and stores it in a database, so that the feedback can be reflected in future story generation.

[1895] Input: HTTPS request from the terminal

[1896] Data calculation: feedback data storage

[1897] Output: Feedback entries in the database

[1898] (Application example 1)

[1899] 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."

[1900] Conventional content distribution services have difficulty providing personalized entertainment content that reflects users' interests and real-time situations, resulting in a lack of improvement in the user experience. Additionally, they have been unable to properly reflect user feedback, limiting improvements to the quality of the content they provide.

[1901] 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.

[1902] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for generating individually optimized stories based on the collected user information and real-time data using a generative AI model, means for delivering the generated stories to a display terminal, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the provision of personalized entertainment content based on the user's interests, current location, time of day, and weather.

[1903] "User Information" means personal information provided by a User, such as name, age, gender, interests, and preferences.

[1904] "Real-time data" refers to up-to-date information that changes over time, specifically the user's current location, time, and weather information.

[1905] A "generative AI model" is an artificial intelligence algorithm for generating personalized entertainment content based on collected data.

[1906] The "story generation means" is a system that automatically creates individually optimized stories based on user information and real-time data.

[1907] A "display device" is a device such as a smartphone, tablet, or computer that is used to display the generated story to the user.

[1908] "Feedback collection means" is a system for collecting opinions and impressions such as ratings and comments on stories from users.

[1909] The "story generation process adjustment means" is a method for improving the content and quality of newly generated stories based on user feedback.

[1910] "Personalized entertainment content" refers to an entertainment experience that is optimized based on a user's individual interests and current situation.

[1911] The system for implementing this invention collects user information, acquires real-time data, and generates individually optimized stories based on that information, which are then provided to users. The specific system configuration and operation method are described below.

[1912] System Configuration

[1913] The system includes the following major components:

[1914] User device: A device operated by a user, such as a smartphone, tablet, or PC. It is used to input user information, obtain real-time data, receive and display stories, and send feedback.

[1915] Server: The central system for processing data and generating stories. It manages user information and real-time data, generates stories, and receives and analyzes feedback.

[1916] Database: Located on the server, it stores data such as user information, real-time data, generated stories, and feedback.

[1917] System Operation Overview

[1918] 1. Collection of User Information

[1919] When a user uses the application for the first time, they enter information such as their name, age, gender, favorite genres, etc. The user's device sends this information to the server, which then creates and stores the user's profile in a database.

[1920] 2. Obtaining real-time data

[1921] The user device periodically acquires the user's location, time, and weather information, and sends this real-time data to the server, which then stores the received data in a database.

[1922] 3. Story Generation

[1923] The user device sends a story request to the server at a specified time every day. The server receives the request and retrieves the user's profile and the latest real-time data from the database. The server uses the generative AI model to generate a story based on the user's profile and real-time data, and sends the generated story to the user device.

[1924] 4. Story Distribution and Display

[1925] The user device notifies the user of the story received from the server, and the user opens the application to read the story.

[1926] 5. Gathering Feedback

[1927] The user device displays an interface that requests the user to rate and provide feedback on the story. The user enters a rating (star rating, comments), and the user device sends the feedback to the server. The server receives the feedback, stores it in a database, and reflects it in future story generation.

[1928] Hardware and Software Configuration

[1929] Hardware: User devices (smartphones, tablets, PCs) and servers

[1930] software:

[1931] Python: a programming language

[1932] Requests: Used to retrieve data from the weather API

[1933] Geopy: A library for obtaining geolocation information

[1934] AI story generation library (hypothetical)

[1935] Specific examples

[1936] Consider a scenario where the user is a 30-year-old man who likes mystery novels. The user's device sends a story request at 8:00 a.m., sending real-time data to the server indicating that the user's current location is Tokyo Station and the weather is sunny. The server uses a generative AI model to generate a story titled "A mysterious incident unfolds at Tokyo Station on a sunny morning" and provides it to the user.

[1937] Prompt Sentence Examples

[1938] Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny

[1939] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1940] Step 1:

[1941] Collection of User Information

[1942] Input: Information that the user enters into the application for the first time, such as name, age, gender, and favorite genres

[1943] Processing: After the user downloads and installs the application, they enter their user information when they first launch it. The device then sends the entered information to the server.

[1944] Data processing: The server stores the received user information in a database and creates a user profile.

[1945] Output: Created user profile

[1946] Specifically, the user enters their name, age, gender, and favorite genre on the application screen, and the device sends this information to the server, which then receives the information and stores it in a database as a user profile.

[1947] Step 2:

[1948] Real-time data acquisition

[1949] Input: Location, current time, and weather information obtained by the device

[1950] Processing: Periodically, the device retrieves location, time, and weather information using the Geopy library to get location information and the Requests library to get weather information.

[1951] Data processing: The acquired information is converted into an appropriate format and sent to the server.

[1952] Output: Real-time data (location, time, weather information)

[1953] Specifically, the device uses GPS to obtain its current location and the current time from the clock. Weather information is obtained from an external weather API. This data is then sent to the server, which stores it in a database.

[1954] Step 3:

[1955] Request a story

[1956] Input: Periodic requests from the user's device

[1957] Processing: The user device sends a story generation request to the server at the specified time.

[1958] Data processing: Receive the request and retrieve the corresponding user profile and real-time data from the database.

[1959] Output: Story generation request with profile and real-time data

[1960] Specifically, the device sends a request to the server at 8 a.m., and the server receives it and retrieves the user profile and real-time data from the database.

[1961] Step 4:

[1962] Story Generation

[1963] Inputs: User profiles, real-time data, generative AI models

[1964] Processing: The server generates prompts based on the user profile and real-time data, and inputs them into the generative AI model. For example, it generates a prompt like "Mystery Story: Time 2023-10-01 08:00:00 Location Tokyo Station Weather Sunny."

[1965] Data processing: The AI ​​model generates a story based on the given prompts.

[1966] Output: Generated stories

[1967] Specifically, the server creates prompts based on the user profile and real-time data, and then inputs them into a generative AI model to generate a story.

[1968] Step 5:

[1969] Distributing and viewing stories

[1970] Input: Generated stories

[1971] Processing: The server sends the generated story to the device. The device receives the story and notifies the user.

[1972] Data processing: Converting the story into a data format for display on the device.

[1973] Output: The story displayed on the user's device

[1974] Specifically, the server sends the generated story to the terminal via an HTTP request, and the terminal receives it and displays it on the screen.

[1975] Step 6:

[1976] Gathering feedback

[1977] Input: User's rating and comments on the story

[1978] Processing: After the device finishes reading the story, it displays an interface asking the user to rate it. The user enters a rating and comments. The device then sends the entered information to the server.

[1979] Data processing: The server stores the received feedback in a database. The feedback data is analyzed and reflected in future story generation.

[1980] Output: Saved feedback data

[1981] Specifically, after a user finishes reading a story, a rating interface will appear on the screen, and the user can enter a star rating and comments. The device will then send the information to the server, which will then store it in a database.

[1982] 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.

[1983] This invention is a system that generates individually optimized stories based on user information and real-time data, and combines this with an emotion engine that recognizes the user's emotions to provide a more personalized experience. The specific system configuration and operation method are explained below.

[1984] System Configuration

[1985] The system includes the following major components:

[1986] User device: A device operated by a user, including a smartphone, tablet, or PC. It inputs user information, acquires real-time data, collects emotional data, receives and displays stories, and sends feedback.

[1987] Server: The central system that processes data and generates stories. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[1988] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated stories, and feedback.

[1989] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize the user's emotions.

[1990] System Operation Overview

[1991] 1. Collection of User Information

[1992] When a user uses the app for the first time, they enter basic information such as their name, age, gender, and favorite genres.

[1993] (Device) sends this information to the server.

[1994] (Server) receives the information and creates and stores the user profile in a database.

[1995] 2. Obtaining real-time data

[1996] (Device) periodically retrieves the user's current location, time, and weather information.

[1997] (Device) transmits the acquired real-time data to the server.

[1998] (Server) receives real-time data and stores it in a database.

[1999] 3. Collecting Emotional Data

[2000] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[2001] (Emotion engine) analyzes this data and recognizes the user's emotions.

[2002] The emotion engine sends the recognized emotion data to the server.

[2003] (Server) stores the emotion data in a database.

[2004] 4. Story Generation

[2005] (Device) sends a story request to the server at a specified time every day.

[2006] (Server) receives the request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[2007] (Server) uses an artificial intelligence engine to generate stories based on user profiles, real-time data, and emotional data.

[2008] For example, if a user likes mysteries and is aware that they are in a state of psychological tension during their morning commute, a mystery-themed story will be generated to heighten the sense of tension on the way to work.

[2009] (Server) sends the generated story to the device.

[2010] 5. Story Distribution and Display

[2011] (Device) notifies the user of the story received from the server.

[2012] (User) sees the notification, opens the app and starts reading the story.

[2013] 6. Gathering Feedback

[2014] (Device) displays an interface asking the user to rate the story and provide feedback.

[2015] (User) enters a rating (star rating, comment) for the story.

[2016] (Device) sends user feedback to the server.

[2017] The server receives the feedback and stores it in a database, which is then used to generate future stories.

[2018] Examples:

[2019] User: Suzuki Ichiro, 35 years old, male, sci-fi novel lover

[2020] Time: 9pm, relaxation time

[2021] Location: Home

[2022] Weather: Rain

[2023] Emotional state: Feeling stressed

[2024] 1. Collection of User Information

[2025] (Ichiro Suzuki) enters "Ichiro Suzuki," "35 years old," "male," and "likes science fiction."

[2026] (Device) sends this information to the server.

[2027] (Server) receives the information and creates and stores Suzuki's profile in the database.

[2028] 2. Obtaining real-time data

[2029] (Device) periodically obtains Suzuki's current location (home) and weather information (rain).

[2030] (Device) sends location and weather information to the server.

[2031] (Server) receives this information and stores it in a database.

[2032] 3. Collecting Emotional Data

[2033] (Device) sends Suzuki's facial expression data to the emotion engine.

[2034] (Emotion engine) analyzes facial expression data and recognizes that the person is feeling stressed.

[2035] (Emotion engine) sends emotion data to the server.

[2036] (Server) stores the emotion data in a database.

[2037] 4. Story Generation

[2038] (Device) sends a story request to the server at 9:00 PM.

[2039] (Server) receives the request and retrieves Suzuki's profile, latest real-time data, and emotion data.

[2040] The server uses an artificial intelligence engine to generate a "science fiction story to read while relaxing at home on a rainy night" based on the conditions "I like science fiction," "I'm at home," "It's raining," and "I'm feeling stressed."

[2041] (Server) sends the generated story to Suzuki's device.

[2042] 5. Story Distribution and Display

[2043] (Device) receives the story and notifies Suzuki.

[2044] (Suzuki) opens the app and reads the story.

[2045] 6. Gathering Feedback

[2046] (Device) asks Suzuki to rate and comment on the story.

[2047] (Mr. Suzuki) enters feedback as "5 stars" and "Very relaxing!"

[2048] (Device) sends feedback to the server.

[2049] (Server) receives the feedback, stores it in a database, and improves future story generation based on the feedback.

[2050] In this way, a system is realized that provides personalized stories that take into account the user's emotions.

[2051] The processing flow will be explained below.

[2052] Step 1:

[2053] (User) launches the app for the first time and enters basic information such as name, age, gender, and favorite genres.

[2054] Step 2:

[2055] (Device) collects information entered by the user and sends it to the server.

[2056] Step 3:

[2057] (Server) creates a user profile based on the received user information and stores it in the database.

[2058] Step 4:

[2059] The device periodically (for example, every hour) obtains the user's current location, time, and weather information.

[2060] Step 5:

[2061] (Device) sends the real-time data (location, time, weather information) it has acquired to the server.

[2062] Step 6:

[2063] (Server) receives real-time data and stores it in the user's status database.

[2064] Step 7:

[2065] (Device) sends the user's facial expression data, voice data, or text data to the emotion engine.

[2066] Step 8:

[2067] (Emotion engine) analyzes the received data and recognizes the user's emotions.

[2068] Step 9:

[2069] The emotion engine sends the recognized emotion data to the server.

[2070] Step 10:

[2071] (Server) receives the emotion data and stores it in a database.

[2072] Step 11:

[2073] (Device) sends a story request to the server at a specified time every day (e.g. 9:00 PM).

[2074] Step 12:

[2075] (Server) receives the story request and retrieves the user's profile, latest real-time data, and sentiment data from the database.

[2076] Step 13:

[2077] The (server) starts an artificial intelligence engine and passes as input the user's profile (e.g., I like science fiction), the latest real-time data (e.g., I'm at home, it's raining), and emotional data (e.g., I'm stressed).

[2078] Step 14:

[2079] (Artificial Intelligence Engine) generates a story based on the parameters you specify (e.g., a sci-fi story to read while relaxing at home on a rainy night).

[2080] Step 15:

[2081] The server retrieves the generated story and associates it with the user's profile.

[2082] Step 16:

[2083] (Server) sends the generated story to the user's device.

[2084] Step 17:

[2085] (Device) notifies the user of stories received.

[2086] Step 18:

[2087] (User) sees the notification, opens the app and reads the story.

[2088] Step 19:

[2089] (Device) displays an interface to the user asking them to rate or comment on the story.

[2090] Step 20:

[2091] (User) enters a rating (star rating, comment) for the story.

[2092] Step 21:

[2093] (Device) sends user feedback to the server.

[2094] Step 22:

[2095] (Server) receives the feedback and stores it in a database.

[2096] Step 23:

[2097] The server analyzes the feedback data and makes adjustments to reflect it in future story generation.

[2098] Example 2

[2099] 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."

[2100] Existing story generation systems have difficulty providing optimized content based on a user's individual preferences, real-time situation, and emotions. Furthermore, they lack a built-in story generation process that reflects the user's emotions in real time, resulting in an insufficiently personalized user experience.

[2101] 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.

[2102] In this invention, the server includes means for collecting user information, means for acquiring real-time data, means for recognizing emotions by analyzing facial expression data, voice data, and text data, means for generating individually optimized stories based on the collected user information, real-time data, and emotional data, means for delivering the generated stories to user terminals, means for collecting feedback from users, and means for adjusting the story generation process based on the feedback, thereby enabling the generation and delivery of personalized stories based on the individual information and emotional state of the user.

[2103] "User Information" refers to basic data provided by a user when they start using an application, and specifically includes information such as their name, age, gender, and favorite genres.

[2104] "Real-time data" is data obtained at a specific time and place, and includes location information, time, weather information, and the like.

[2105] "Emotional data" refers to data that indicates the emotional state of a user obtained by analyzing their facial expressions, voice, and text data.

[2106] A "user profile" is a data set for an individual user that is generated based on user information and stored in a database.

[2107] "Story generation" is the process of creating individually optimized stories based on collected user information, real-time data, and sentiment data.

[2108] "Feedback" refers to response data provided by users, such as ratings and comments on stories.

[2109] A "generative AI model" is a system that includes algorithms that automatically generate stories and content based on input data, for example, using natural language processing or machine learning techniques.

[2110] A "prompt" is an instruction entered into a generative AI model to specify the content and direction of the story to be generated.

[2111] This invention is a system that generates individually optimized stories based on user information and real-time data. The system incorporates an emotion engine that recognizes user emotions, enabling it to provide a more personalized experience. The system is primarily composed of components such as a user terminal, a server, a database, and an emotion engine.

[2112] System configuration:

[2113] User device:

[2114] A device operated by a user, such as a smartphone, tablet, or PC, that inputs user information, obtains real-time data, collects emotional data, receives and displays stories, and sends feedback.

[2115] Examples:

[2116] The user installs the application the first time they use it, and it uses the device's camera and microphone to collect facial expression and voice data. It also uses the GPS function to obtain location information. For example, it is commonly used as a smartphone application.

[2117] server:

[2118] This is the central system for data processing and story generation. It manages user information, real-time data, and emotional data, generates stories, and receives and analyzes feedback.

[2119] Examples:

[2120] We use frameworks such as Python and Django to build a backend API to manage and analyze incoming data. We use OpenAI's GPT-3 as a generative AI model to generate stories by inputting prompt sentences.

[2121] Database:

[2122] It is installed on a server and stores data such as user information, real-time data, emotional data, generated stories, and feedback.

[2123] Examples:

[2124] Data is stored and retrieved using SQL queries in database management systems such as MySQL or PostgreSQL. User profiles and feedback are used to inform future story generation.

[2125] Emotion Engine:

[2126] This is a system that analyzes a user's facial expression data, voice data, and text data to recognize the user's emotions.

[2127] Examples:

[2128] Using Google Cloud Vision API and IBM Watson's sentiment analysis API, the collected data is analyzed to generate emotion data, which is then sent to a server and stored in a database.

[2129] Examples and prompts:

[2130] Examples:

[2131] User: 35-year-old male, sci-fi fan

[2132] Real-time data: At home, time is 9pm, weather is raining

[2133] Emotional state: Stressed

[2134] Example prompt sentence:

[2135] "Generate a relaxing sci-fi story for this user who is a 35-year-old male who likes sci-fi, is currently at home, and is feeling stressed because it's raining outside."

[2136] In this way, a system is realized that takes into account user emotions and real-time data to generate individually optimized stories and provide users with a personalized experience.

[2137] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2138] Step 1:

[2139] Collection of User Information

[2140] Input: Basic information entered by the user, such as name, age, gender, and favorite genres

[2141] Processing: When a user first uses the app, they enter basic information such as their name, age, gender, and favorite genres. The device then sends this information to the server.

[2142] Output: The server stores the received user information in a database and creates a user profile.

[2143] How it works: When a user enters information into the input form displayed on the app screen and presses the submit button, the device sends the data to the backend API, and the server executes an SQL query to store the received data in the database.

[2144] Step 2:

[2145] Real-time data acquisition

[2146] Input: User's current location, time, and weather information

[2147] Processing: The device periodically obtains the user's location, time, and weather information and sends it to the server.

[2148] Output: The server stores the received real-time data in a database.

[2149] Specific operation: Obtain location information from the device's GPS sensor and time information from the system clock. Obtain weather information using an external API (e.g., OpenWeatherMap). Send this data to a backend API on a regular timer, and the server executes an SQL query to store the received data in a database.

[2150] Step 3:

[2151] Collecting Emotional Data

[2152] Input: User's facial expression data, voice data, text data

[2153] Processing: The device sends this data to the emotion engine and receives the analysis results.

[2154] Output: The emotion engine sends the analyzed emotion data to the server, which stores it in a database.

[2155] How it works: It uses the device's camera, microphone, and input devices to collect facial expression, voice, and text data. This data is then sent to the emotion engine's API, which uses image processing and natural language processing algorithms to determine the user's emotional state. The result is then sent to the backend API, and the server executes an SQL query to store the received data in a database.

[2156] Step 4:

[2157] Story Generation

[2158] Inputs: User profile data, up-to-date real-time data, sentiment data

[2159] Processing: The server receives a story request from the device at the specified time, retrieves the user profile, real-time data, and emotion data from the database, and then inputs the prompt sentence into the generative AI model to generate the story.

[2160] Output: Send the generated story from the server to the device.

[2161] Specific operation: At the specified time, the device sends a story request to the backend API. The server executes an SQL query to obtain the necessary data and inputs the following prompt statement into the generative AI model (e.g., GPT-3): "This user is a 35-year-old male who likes science fiction. He is currently at home and feeling stressed because it's raining outside. Please generate a relaxing science fiction story for him." The generated story is then sent to the device via the backend API.

[2162] Step 5:

[2163] Distributing and viewing stories

[2164] Input: Story sent from the server

[2165] Processing: The device notifies the user of the received story, and the user opens the app to read the story.

[2166] Output: The story is displayed to the user.

[2167] What happens: The device sends a push notification to the user to let them know that a story has been delivered. The user taps the app to launch it and view the received story.

[2168] Step 6:

[2169] Gathering feedback

[2170] Input: User's rating and comments on the story

[2171] Processing: The device collects feedback from the user and sends it to the server.

[2172] Output: The server receives the feedback and stores it in a database. The feedback is reflected in future story generation.

[2173] Specific operation: After reading the story, the device displays a rating form, and the user enters their rating and comments. The input data is sent to the backend API by pressing the submit button, and the server stores the received data in a database and uses it for the next story generation.

[2174] (Application example 2)

[2175] 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."

[2176] Conventional ad delivery systems could only provide ads based on basic user attribute information, and were unable to provide personalized ads that took into account the user's momentary emotions and real-time circumstances. This limited the effectiveness of ads and made it difficult to attract user interest. Furthermore, they lacked a mechanism for effectively utilizing feedback to improve the quality of ads.

[2177] 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.

[2178] In this invention, the server includes: means for collecting user information; means for acquiring real-time data; means for generating individually optimized advertising stories based on the collected user information and real-time data; means including an emotion engine for collecting and analyzing user emotion data; means for delivering the generated advertising stories to a display terminal; means for collecting user feedback; and means for adjusting the advertising story generation process based on the feedback. This allows for the generation of advertisements that take into account the user's momentary emotions and real-time situations, enabling more effective and personalized advertisement delivery. Furthermore, the quality of advertisements can be continuously improved by utilizing the feedback.

[2179] "Means for collecting user information" refers to hardware or software used to collect basic user attribute information (e.g., name, age, gender, interests, etc.).

[2180] "Means for obtaining real-time data" refers to hardware or software for periodically obtaining the latest data such as the user's current location, time, and weather information.

[2181] "Means for generating individually optimized advertising stories based on collected user information and real-time data" refers to hardware or software that has algorithms or generative AI models for analyzing collected user information and real-time data and generating advertising content optimized for each individual user.

[2182] An "emotion engine that collects and analyzes user emotional data" is hardware or software that includes an AI engine that analyzes a user's facial expressions, voice, text data, etc., and recognizes their emotional state.

[2183] "Means for delivering the generated advertising story to a display device" refers to hardware or software for transmitting and displaying the generated advertising story on a display device such as a user's smartphone, tablet, or PC.

[2184] "Means for collecting user feedback" means hardware or software for collecting ratings and comments made by users on advertising stories.

[2185] A "means for adjusting the advertising story generation process based on feedback" is hardware or software for analyzing user feedback and, based thereon, improving how future advertising stories are generated.

[2186] This invention is a system that generates individually optimized advertising stories based on user information, real-time data, and emotional data, and provides users with a personalized advertising experience. Specific methods for implementing this system are described below.

[2187] System configuration

[2188] The system includes the following major components:

[2189] User devices: smartphones, tablets, computers, etc. User information is entered, real-time data is acquired, emotion data is collected, advertising stories are received and displayed, and feedback is sent.

[2190] Server: The central system that processes data and generates advertising stories. It manages user information, real-time data, and emotional data, generates advertising stories, and receives and analyzes feedback.

[2191] Database: Located on the server, it stores data such as user information, real-time data, sentiment data, generated advertising stories, and feedback.

[2192] Emotion engine: A system that analyzes a user's facial expression data, voice data, or text data to recognize emotions.

[2193] System Operation Overview

[2194] 1. Collection of User Information

[2195] When a user uses the system for the first time, they enter basic information such as their name, age, gender, and areas of interest. This information is sent to the server via the user's device and stored in a database.

[2196] 2. Obtaining real-time data

[2197] The user device periodically obtains the current location, time, and weather information and sends it to the server, which stores this real-time data in a database.

[2198] 3. Collecting Emotional Data

[2199] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database.

[2200] 4. Advertising story generation

[2201] The user device sends a request to the server to generate an advertising story at a specified time. The server obtains the user's profile, the latest real-time data, and emotional data, and uses an artificial intelligence engine to generate the optimal advertising story based on this. The generated advertising story is then sent to the user device.

[2202] 5. Delivery and display of advertising stories

[2203] The user terminal notifies the user of the advertising story received from the server and displays it through the application.

[2204] 6. Gathering Feedback

[2205] The user device displays an interface that asks the user to rate and provide feedback on the advertising story. The user's feedback is sent to the server and stored in a database. The feedback information is reflected in future advertising story generation.

[2206] Specific examples

[2207] For example, for a user with the following profile:

[2208] User: Taro Yamada, 45 years old, male, loves technology

[2209] Time: 3pm, office

[2210] Location: Office building

[2211] Weather: Sunny

[2212] Emotional state: Focused

[2213] For this user, we generate an advertising story for "latest technology products" that will help them stay focused in an office environment.

[2214] Prompt Sentence Examples

[2215] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[2216] This system makes it possible to provide personalized ads that reflect the user's momentary emotions and real-time situations, and also to use feedback to improve the quality of ads.

[2217] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2218] Step 1:

[2219] Collection of User Information

[2220] When a user first uses the system, they enter basic information such as their name, age, gender, and areas of interest. The entered information is sent by the user's device to a server, which receives the information and stores it in a database. In this process, the entered data is used to generate a basic profile to understand the user's interests.

[2221] Step 2:

[2222] Real-time data acquisition

[2223] The user device periodically obtains the user's current location, time, and weather information. This information is updated in real time and sent to the server. The server stores this real-time data in a database. Using the real-time data (location, time, and weather) as input, the server processes the data to understand the user's current situation.

[2224] Step 3:

[2225] Collecting Emotional Data

[2226] The user device sends the user's facial expression data, voice data, or text data to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The recognized emotion data is sent to the server and stored in a database. The input data is analyzed to output the user's emotional state and store it as emotion data.

[2227] Step 4:

[2228] Advertising story generation

[2229] Each day, a user device sends a request to the server at a specified time to generate an advertising story. The server receives the request and retrieves the user's profile, the latest real-time data, and sentiment data from the database. The generative AI model generates an advertising story based on this data. The profile data, real-time data, and sentiment data are analyzed and integrated to generate an individually optimized advertising story as output.

[2230] Step 5:

[2231] Ad story distribution and display

[2232] The server sends the generated advertising story to the user terminal, which receives the advertising story, notifies the user, and displays it through the application, receiving the generated advertising story as input and outputting it as notification and display data.

[2233] Step 6:

[2234] Gathering feedback

[2235] The user device displays an interface that requests the user to rate and provide feedback on the advertising story. The user device sends the feedback to the server, which receives the feedback and stores it in a database. The feedback information is collected as input for use in generating the next advertising story, and stored as output in the feedback database.

[2236] Examples include the following:

[2237] For example, if the user is "Taro Yamada, 45 years old, male, loves technology" and is concentrating in a sunny office building at 3 p.m., an advertising story for a "personalized technology product" will be automatically generated and delivered to the user.

[2238] Prompt Sentence Examples

[2239] "Taro Yamada, 45 years old, male, loves technology. Currently, he is concentrating in a sunny office building at 3 PM. Based on this, please generate a personalized advertising story for a technology product."

[2240] 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.

[2241] 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.

[2242] 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.

[2243] 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.

[2244] 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.

[2245] 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.

[2246] 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).

[2247] 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.

[2248] 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."

[2249] 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.

[2250] 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).

[2251] 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.

[2252] 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.

[2253] 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.

[2254] 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.

[2255] 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.

[2256] 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.

[2257] 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.

[2258] 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.

[2259] 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.

[2260] 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.

[2261] The following is further disclosed regarding the above embodiment.

[2262] (Claim 1)

[2263] the means by which user information is collected;

[2264] a means for obtaining real-time data;

[2265] A means of generating individually optimized stories based on collected user information and real-time data; and

[2266] means for delivering the generated story to a display device;

[2267] a means of gathering user feedback;

[2268] a means of adjusting the story generation process based on feedback;

[2269] A system including:

[2270] (Claim 2)

[2271] 10. The system of claim 1, wherein the system obtains real-time data including location information, time, and weather information.

[2272] (Claim 3)

[2273] 2. The system according to claim 1, wherein a user profile is generated based on input information from the user and stored in a database.

[2274] "Example 1"

[2275] (Claim 1)

[2276] the means by which user information is collected;

[2277] a means for obtaining real-time data;

[2278] A means of generating individually optimized stories based on collected user information and real-time data; and

[2279] means for delivering the generated story to a display device;

[2280] a means of gathering user feedback;

[2281] a means of adjusting the story generation process based on feedback;

[2282] a means for periodically obtaining the user's location, time, and weather information;

[2283] A means to submit story requests at designated times;

[2284] a means for generating prompts and generating stories using a generative AI model;

[2285] A system including:

[2286] (Claim 2)

[2287] 10. The system of claim 1, wherein the system obtains real-time data including location information, time, and weather information.

[2288] (Claim 3)

[2289] 2. The system according to claim 1, wherein a user profile is generated based on input information from the user and stored in a database.

[2290] "Application Example 1"

[2291] (Claim 1)

[2292] the means by which user information is collected;

[2293] a means for obtaining real-time data;

[2294] A means of generating individually optimized stories based on collected user information and real-time data; and

[2295] means for delivering the generated story to a display device;

[2296] a means of gathering user feedback;

[2297] a means of adjusting the story generation process based on feedback;

[2298] a means for generating personalized entertainment content based on a user's interests, current location, time of day, and weather using a generative AI model;

[2299] A system including:

[2300] (Claim 2)

[2301] 10. The system of claim 1, wherein the system obtains real-time data including location information, time, and weather information.

[2302] (Claim 3)

[2303] 2. The system according to claim 1, wherein a user profile is generated based on input information from the user and stored in a database.

[2304] "Example 2: Combining Emotion Engines"

[2305] (Claim 1)

[2306] the means by which user information is collected;

[2307] a means for obtaining real-time data;

[2308] A means for recognizing emotions by analyzing facial expression data, voice data, and text data;

[2309] a means for generating individually optimized stories based on collected user information, real-time data, and sentiment data;

[2310] A means for delivering the generated story to a user device;

[2311] a means of gathering user feedback;

[2312] a means of adjusting the story generation process based on feedback;

[2313] A system including:

[2314] (Claim 2)

[2315] 10. The system of claim 1, wherein the system obtains real-time data including location information, time, and weather information.

[2316] (Claim 3)

[2317] 2. The system according to claim 1, wherein a user profile is generated based on input information from the user and stored in a database.

[2318] "Application example 2 when combining emotion engines"

[2319] (Claim 1)

[2320] the means by which user information is collected;

[2321] a means for obtaining real-time data;

[2322] A means for generating individually optimized advertising stories based on collected user information and real-time data;

[2323] means including an emotion engine for collecting and analyzing emotion data of a user;

[2324] A means for delivering the generated advertising story to a display terminal;

[2325] a means of gathering user feedback;

[2326] a means for adjusting the advertising story generation process based on feedback;

[2327] A system including:

[2328] (Claim 2)

[2329] 10. The system of claim 1, wherein the system obtains real-time data including location information, time, and weather information.

[2330] (Claim 3)

[2331] 2. The system according to claim 1, wherein a user profile is generated based on input information from the user and stored in a database. [Explanation of symbols]

[2332] 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. the means by which user information is collected; a means for obtaining real-time data; A means of generating individually optimized stories based on collected user information and real-time data; and means for delivering the generated story to a display device; a means of gathering user feedback; a means of adjusting the story generation process based on feedback; A system including:

2. The system of claim 1 , wherein the system obtains real-time data including location information, time, and weather information.

3. 2. The system according to claim 1, wherein a user profile is generated based on information input by the user and stored in a database.

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