System

Abstract

To provide a system capable of actively providing necessary information and reducing an information gap to an unmarried layer having much time to spend alone.SOLUTION: In the information providing system, means for inputting user information, means for transmitting the user information to a server, means for storing the user information in a database, means for collecting behavior data of a user, means for transmitting the behavior data to the server, means for analyzing the user information and the behavior data, means for selecting information suitable for the user on the basis of an analysis result, means for notifying the user of the selected information, means for receiving feedback from the user by the notification, means for transmitting the feedback to the server and analyzing the feedback again, and means for providing additional information to the user on the basis of the analysis result of the feedback. Means for aggregating and globally analyzing data of a plurality of users and distributing equal information to all users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention aims to solve the problem of the difficulty of selecting and discarding information in modern society. In particular, it aims to actively provide necessary information to single people who spend a lot of time alone, thereby reducing the information gap. [Means for solving the problem]

[0005] The present invention provides a means for inputting user information and transmitting it to a server. The server then stores the received user information in a database, and then collects and transmits user behavior data to the server. The server then analyzes this data and selects information appropriate for the user.

[0006] The selected information is provided by a means of notifying the user, and a means is also provided for receiving feedback from the user. This feedback is sent back to the server, and additional information is provided based on the analysis results. There is also a means for aggregating data from multiple users and analyzing it globally to distribute uniform information to all users.

[0007] Furthermore, by adding a means to provide selected information via a voice assistant and a means to periodically collect user behavioral data and adjust the timing of transmission, the accuracy and convenience of information provision can be improved.

[0008] An "information provision system" is a system that includes devices, means, and processes for providing information to users.

[0009] "User Information" is data that includes a user's profile information, interests, goals, etc.

[0010] "Server" means a computer system for storing and analyzing user information and behavioral data.

[0011] "Database" means a structured data storage system for systematically storing and managing user information and behavioral data.

[0012] "Behavioral data" is information about user behavior, such as user app usage and website browsing history.

[0013] An "analysis algorithm" is a mathematical method for analyzing user information and behavioral data to identify user interests and concerns.

[0014] A "notification" is a message, alert, or audio means of conveying information to a user.

[0015] "Feedback" is information based on reactions and responses from users.

[0016] A "voice assistant" is software that interacts with users via voice and carries out their instructions.

[0017] "Global analysis" means aggregating data from multiple users and analyzing overall patterns and trends.

[0018] The "digital divide" refers to the inequality in the opportunities and quality of information acquisition and use between different social classes and individual users.

[0019] A "notification means" is a method or device for providing selected information to a user.

[0020] "Selection" is the process or action of determining the information most relevant to a user. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention relates to a system in which a device such as a smartphone speaks to a user spontaneously and provides necessary information and advice. Specific program processing and its flow are described below for an embodiment of the present invention.

[0043] Basic System Configuration

[0044] 1. Initial registration of user information

[0045] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0046] 2. Collecting behavioral data

[0047] The device periodically records user behavior data (such as app usage and website browsing history), and then periodically transmits this data to a server.

[0048] 3. Data Analysis

[0049] The server runs analytical algorithms based on the received user information and behavioral data to profile the user's interests and concerns.

[0050] 4. Selection and provision of information

[0051] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "There are some recent health articles based on your interests. Please take a look."

[0052] 5. User interaction

[0053] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[0054] 6. Closing the digital divide

[0055] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[0056] Specific examples

[0057] Example of initial registration and information provision

[0058] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[0059] Terminal: Sends the entered information to the server.

[0060] Server: Stores user information in a database and analyzes recent health trends.

[0061] Server: Selects health articles that are useful to the user and sends them to the device.

[0062] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[0063] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[0064] Terminal: Sends user feedback to the server in real time.

[0065] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[0066] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0067] This allows the entire system to work together to provide information to users and further optimize the process based on their feedback.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[0071] Step 2:

[0072] The terminal receives the entered user information and sends it to the server.

[0073] Step 3:

[0074] The server stores the user information in a database.

[0075] Step 4:

[0076] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[0077] Step 5:

[0078] The device periodically transmits the collected behavioral data to the server.

[0079] Step 6:

[0080] The server then runs the received behavioral data, along with user information, through an analytical algorithm to profile the user's interests and concerns.

[0081] Step 7:

[0082] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[0083] Step 8:

[0084] The server transmits the selected information to the terminal.

[0085] Step 9:

[0086] Based on the information received, the device displays notifications to the user at the appropriate time.

[0087] Step 10:

[0088] The device will then activate the voice assistant and verbally notify the user of the notification, which will be something like, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0089] Step 11:

[0090] Users can send questions or feedback about the suggested information to the device via voice or text.

[0091] Step 12:

[0092] The device sends user feedback to the server in real time.

[0093] Step 13:

[0094] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[0095] Step 14:

[0096] The server transmits the generated response to the terminal.

[0097] Step 15:

[0098] The device will then provide the answer to the user through the voice assistant, such as, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0099] Step 16:

[0100] The server aggregates data from multiple users and analyzes it globally.

[0101] Step 17:

[0102] The server extracts the most useful information and trends and distributes updated data to devices to provide uniform information to all users.

[0103] Example 1

[0104] 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."

[0105] Conventional information provision systems face the challenge of providing optimal information to individual users when collecting and analyzing user information and behavioral data. It is also difficult to effectively utilize user feedback when providing information. Furthermore, there are insufficient means for aggregating data from multiple users and analyzing and providing it from a global perspective. For these reasons, there is a need to improve the quality of information provided to users and continuously optimize it based on feedback.

[0106] 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.

[0107] In this invention, the server includes means for analyzing a user's natural language input using a generative AI model and generating an appropriate response, means for providing the generated response to the user through a voice assistant function, and means for aggregating and globally analyzing data from multiple users to distribute uniform information to all users. This enables optimal information to be provided to individual users, and the quality of information provided can be improved by effectively utilizing user feedback. Furthermore, by integrating and analyzing data from multiple users, uniform information provision from a global perspective can also be achieved.

[0108] "User information" refers to profile data entered by the user, such as age, gender, interests, and purpose.

[0109] A "server" is a central processing unit that stores and analyzes user information and behavioral data and provides appropriate information.

[0110] A "database" is a system managed by a server for efficiently storing, searching, and updating user information and behavioral data.

[0111] "Behavioral data" refers to data such as usage and browsing history when a user uses an app or website.

[0112] "Analysis" is the act of using collected user information and behavioral data to run specific algorithms and profile users' interests and concerns.

[0113] "Feedback" refers to questions, reactions, opinions, etc. that users have in response to the information provided.

[0114] A "generative AI model" is an artificial intelligence model that analyzes a user's natural language input and generates an appropriate response based on it.

[0115] The "voice assistant function" is a function that allows users to interact with the device via voice and provide necessary information.

[0116] "Global analysis" refers to large-scale data analysis that aggregates data from multiple users and extracts and provides the most useful information overall.

[0117] This invention relates to an information provision system in which a device such as a smartphone speaks to the user spontaneously and provides necessary information and advice. This system is mainly composed of three elements: a server, a device, and a user.

[0118] First-time user information registration

[0119] Users launch a dedicated application on their smartphone or other device and access the initial registration screen. They enter their profile information, such as age, gender, interests, and purpose, into the registration form. The device serializes the entered information into JSON format and sends it to the server using the HTTPS protocol.

[0120] Storing information on the server

[0121] The server parses the received JSON data and stores it in a relational database management system (e.g., MySQL (registered trademark)). The stored information is stored in a user attribute table (age and gender), and a category table (interests and goals).

[0122] Behavioral data collection

[0123] The device periodically records user behavioral data, such as app usage and website browsing history, using SQLite as the database. The collected data is periodically sent to the server in CSV format.

[0124] Analyzing the data

[0125] The server uses Python libraries (e.g., Pandas, Scikit-learn) to analyze user information and behavioral data, profiling user interests and constructing user profiles using generative AI models (e.g., GPT-4®).

[0126] Selection and provision of information

[0127] The server retrieves relevant topics using a news API or similar based on the generated user profile. The selected information is serialized in JSON format and sent to the device. The device then notifies the user through its voice assistant function using the Google Assistant SDK or Amazon Alexa SDK.

[0128] User interaction

[0129] When a user provides feedback or asks a question, the device converts it into a string using the Google Cloud Speech-to-Text API and sends it to the server. The server then uses a generative AI model (GPT-4) to analyze the question and generate an appropriate response. The generated response is then sent to the device and conveyed to the user via the voice assistant.

[0130] Global data analysis and information provision

[0131] The server aggregates data from multiple users and analyzes it globally using big data analysis tools such as Apache Hadoop and Spark. Based on the results of this analysis, information is provided equally to reduce the digital divide.

[0132] As a specific example, if a 30-year-old woman is interested in health and running and is trying to lose weight, the server will provide appropriate information such as, "Recently, protein has been said to be effective for dieting. Please read this article." Also, if the user asks, "Which specific protein is good?", the server will provide a specific answer such as, "The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0133] Prompt Sentence Examples

[0134] Generative AI model: GPT-4

[0135] Prompt: "For a 30-year-old woman interested in health and running who is looking to lose weight, please recommend a protein product. Please include specific brands and reviews."

[0136] The above is an embodiment of the present invention.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] First-time user information registration

[0140] User: Launches the dedicated smartphone app and accesses the initial registration screen. The user enters profile information such as age, gender, interests, and purpose.

[0141] Terminal: User input information is serialized in JSON format and sent to the server using the HTTPS protocol.

[0142] Server: Parse the received JSON data and save it in a MySQL database to store age and gender in a user attribute table and interests and goals in a category table. This saves user information in a structured format in the database.

[0143] Step 2:

[0144] Behavioral data collection

[0145] Device: Periodically records your app usage and website browsing history. This data includes the amount of time you spend using apps and the URLs of websites you visit.

[0146] Terminal: Collected behavioral data is sent to the server in batches every certain period (for example, every hour). Data is often sent in CSV format.

[0147] Server: Analyzes the received CSV data, stores it in the action log table with a timestamp, and saves it in the database.

[0148] Step 3:

[0149] Analyzing the data

[0150] Server: Analyzes the received user information and behavioral data using Python libraries such as Pandas and Scikit-learn.

[0151] Server: Runs a clustering algorithm to build user profiles, specifically using K-means clustering to group users by their interests.

[0152] Server: Based on the generated user profile, a generative AI model (GPT-4) is used to predict information based on the user's interests.

[0153] Step 4:

[0154] Selection and provision of information

[0155] Server: Calls the news API to get the latest articles on relevant topics based on the user's interests.

[0156] Server: Serializes the selected information into JSON format and sends it to the device.

[0157] Device: Analyzes the received information and notifies the user at the appropriate time using the voice assistant function. For example, using the Google Assistant SDK, it can say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0158] Step 5:

[0159] User interaction

[0160] User: Reads the suggested article and asks questions or gives feedback about the content. For example, "I'm interested in protein, which specific product is good?"

[0161] On the device: Recognizes the user's voice and converts it into text, using the Google Cloud Speech-to-Text API.

[0162] Terminal: Sends the converted text to the server.

[0163] Server: Analyzes the user's question using a generative AI model (GPT-4) and generates an appropriate answer, such as "The recommended protein product is XX brand YY. The reviews for this product are also very positive."

[0164] Device: The generated answer is synthesized into speech and conveyed to the user.

[0165] Step 6:

[0166] Global data analysis and information provision

[0167] Server: Uses big data analysis tools such as Apache Hadoop and Spark to statistically analyze all collected user data.

[0168] Server: Extracts the most useful information from the acquired data and generates a model to provide it to all users equally.

[0169] Server: Use this model to continuously improve the quality of information provision and reduce the digital divide.

[0170] (Application example 1)

[0171] 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."

[0172] Conventional information provision systems lack the functionality to suggest optimal meals based on a user's dietary preferences and health status, making it difficult to link appropriate meal suggestions for individual users with food delivery services. Furthermore, information cannot be updated in real time based on user feedback, making it difficult to provide timely information. The objective of this invention is to solve these problems and provide an information provision system that suggests optimal meals based on a user's health and preferences and provides a smooth service to users in collaboration with food delivery services.

[0173] 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.

[0174] In this invention, the server includes means for suggesting meals and processing food delivery orders, means for inputting user information, means for transmitting the user information to the server, means for storing the user information in a database, means for collecting user behavioral data, means for transmitting the behavioral data to the server, means for analyzing the user information and behavioral data, means for selecting information appropriate for the user based on the analysis results, means for notifying the user of the selected information, means for receiving feedback from the user via the notification, means for transmitting the feedback to the server and re-analyzing it, means for providing the user with additional information based on the analysis results of the feedback, and means for aggregating data from multiple users, analyzing it globally, and distributing uniform information to all users. This enables smooth integration of optimal meal suggestions tailored to the health and preferences of each individual user with food delivery services.

[0175] An "information provision system" is a system designed to provide users with the information and services they need.

[0176] "User Information" refers to individual data about you, such as your age, gender, health status, and dietary preferences.

[0177] A "server" is a computer system that acts as a central control device, managing, storing, and analyzing user information and behavioral data.

[0178] A "database" is a continuous collection of data that systematically stores and manages collected user information and behavioral data.

[0179] "Behavioral data" refers to information about a user's behavior, such as their app usage and food ordering history.

[0180] An "analysis algorithm" is a calculation method used to profile users and select information based on collected data.

[0181] "Notifying" refers to the act of conveying selected information to a user in the form of voice, text, or other means.

[0182] "Feedback" refers to information provided by users in the form of responses, comments, questions, etc.

[0183] "Meal Suggestion" refers to the act of recommending optimal meal plans and menus based on a user's health status and preferences.

[0184] "Food delivery" refers to the process of providing meals selected by users through a delivery service.

[0185] MODE FOR CARRYING OUT THE INVENTION

[0186] System configuration and programs

[0187] The system for implementing this invention includes an information provision system that suggests optimal meals based on the user's health condition and dietary preferences and works in conjunction with a food delivery service. The main components of the system and their functions are as follows:

[0188] 1. Initial registration of user information

[0189] Users access the initial registration screen using a device such as a smartphone and enter information such as their name, age, gender, allergies, dietary preferences, and health status. The device sends the entered information to a server, which then stores it in a database.

[0190] 2. Collecting behavioral data

[0191] The device records behavioral data such as the meals ordered by the user, delivery history, and app usage, and this data is periodically sent to the server.

[0192] 3. Data Analysis

[0193] The server runs analytics algorithms to profile health status and preferences based on the received user information and behavioral data. Specific software used includes MySQL and Postgres for database management, and Pytorch and Tensorflow® for analytics algorithms.

[0194] 4. Selection and provision of information

[0195] Based on the analysis results, the server selects the most suitable meal suggestions and information on food delivery services for the user. The selected information is sent to the device, which notifies the user at the appropriate time. For example, the voice assistant may notify the user, "Today's recommendation is low-carb chicken salad. Would you like to order it?"

[0196] 5. User interaction

[0197] When a user submits feedback on a suggestion, the information is sent to the server in real time and analyzed again. Additional suggestions and detailed information are generated based on the feedback and provided to the user via their device. For example, if a user asks, "Do you have a menu with a slightly larger portion?", the server analyzes and responds with the appropriate menu item.

[0198] 6. Closing the digital divide

[0199] The server aggregates data collected from multiple users and analyzes it globally, making it possible to provide equally useful information to all users, thereby eliminating the information gap.

[0200] Specific examples

[0201] First-time registration and information provision process

[0202] User: On the initial registration screen, enter the following information: "Age: 30," "Gender: Male," "Health condition: On a low-carb diet," "Food preference: Japanese food," and "Allergies: None."

[0203] Terminal: Sends the entered information to the server.

[0204] Server: Stores user information in a database and runs analytical algorithms to generate user profiles.

[0205] Server: Selects the best meal suggestions for the user and sends the information to the device.

[0206] Device: Announces, "We recommend the low-carb chicken salad. Would you like to order it?"

[0207] Examples of user interactions

[0208] User: "Do you have something a little more substantial?"

[0209] Server: Analyzes the feedback and suggests, "How about a chicken steak and salad combo?"

[0210] The generative AI model used is GPT-4 (OpenAI (registered trademark)), which generates information appropriate for the user using the following prompt sentences:

[0211] Example prompt:

[0212] User profile information: "30 years old, male, on a low-carb diet, no allergies"

[0213] Sample prompt: "Suggest a low-carb lunch option that would work best for your target audience. Specifically, choose from chicken salad or grilled chicken."

[0214] In this way, the system of the present invention provides optimal meal suggestions and food delivery services based on the user's health and dietary preferences.

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1:

[0217] First time user registration:

[0218] The user accesses the initial registration screen using a smartphone and enters their name, age, gender, health condition, dietary preferences, allergy information, etc. The device formats the entered information in JSON format and sends it to the server using an HTTP POST request. The server stores the received data in a database. The server uses MySQL as its database management system and adds the user information to a table.

[0219] Step 2:

[0220] Behavioral Data Collection:

[0221] The device records user behavioral data, such as app usage and past order history, in a local data store. Using a periodic scheduler (e.g., a cron job), the local data is sent to the server. This data is also formatted in JSON and sent to the server via an HTTP POST request. The server stores the received data in a database.

[0222] Step 3:

[0223] Data analysis:

[0224] The server periodically runs batch processes to analyze user information and behavioral data stored in the database. The server uses Python scripts and TensorFlow to run machine learning models to create user profiles that reflect the user's behavioral patterns and preferences. The analysis results are stored in the database.

[0225] Step 4:

[0226] Selection and provision of information:

[0227] Based on the analysis results, the server selects the most suitable meal suggestion for the user. The generative AI model used is GPT-4 (OpenAI), which generates the prompt text. A sample prompt text includes user profile information, as well as a message such as, "Please suggest the best low-carb lunch menu for the target user. Specifically, please choose from chicken salad, grilled chicken, etc." The generated suggestion text is sent to the device in JSON format.

[0228] Step 5:

[0229] Notice to users:

[0230] The device analyzes the information it receives and notifies the user using a voice assistant or push notification. For example, it might say, "We recommend a low-carb chicken salad. Would you like to order it?" It may also ask for user feedback.

[0231] Step 6:

[0232] Collecting user feedback:

[0233] Users provide feedback through voice or text input, which the device collects in real time and sends via HTTP POST requests to the server, which stores the received feedback in a database.

[0234] Step 7:

[0235] Feedback reanalysis:

[0236] The server analyzes the feedback and runs the machine learning model again to identify additional user needs. Based on the new analysis, it generates additional information and suggestions, which are stored in a database and sent to the device.

[0237] Step 8:

[0238] Bridging the digital divide:

[0239] The server aggregates all user data and performs global analysis to identify the most useful information, which is then distributed equally to all users through periodic reports and notifications to the broad user base.

[0240] Step 9:

[0241] Food delivery ordering process:

[0242] If the user accepts the suggestion, the device will call the food delivery service's API to complete the order, and the order details will be sent to the server and saved as an order history.

[0243] 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.

[0244] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[0245] Basic System Configuration

[0246] 1. Initial registration of user information

[0247] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0248] 2. Collecting behavioral data

[0249] The device periodically records user behavior data (such as app usage and website browsing history) in local storage, and then periodically transmits this data to the server.

[0250] 3. Emotion Recognition by Emotion Engine

[0251] The device collects emotional data from the user's voice and facial expressions, and uses an emotion engine to analyze multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time. The analysis results are sent to a server.

[0252] 4. Data Analysis

[0253] The server runs analytical algorithms to profile the user's interests and concerns based on the received user information, behavioral data, and emotional data.

[0254] 5. Selection and provision of information

[0255] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "I sensed that you've been interested in health recently. Please take a look at this health article."

[0256] 6. User interaction

[0257] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[0258] 7. Closing the digital divide

[0259] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[0260] Specific examples

[0261] Example of initial registration and information provision

[0262] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[0263] Terminal: Sends the entered information to the server.

[0264] Server: Stores user information in a database and analyzes recent health trends.

[0265] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server.

[0266] Server: Selects the most appropriate information based on the user's behavioral and emotional data and sends it to the device.

[0267] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[0268] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[0269] Terminal: Sends user feedback to the server in real time.

[0270] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[0271] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0272] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[0273] The processing flow will be explained below.

[0274] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[0275] Basic System Configuration

[0276] Step 1:

[0277] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[0278] Step 2:

[0279] The terminal transmits the entered user information to the server.

[0280] Step 3:

[0281] The server stores the user information in a database.

[0282] Step 4:

[0283] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[0284] Step 5:

[0285] The device periodically transmits the collected behavioral data to the server.

[0286] Step 6:

[0287] The device uses an emotion engine to analyze the user's voice data and facial expression data captured by the camera, recognizing multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time.

[0288] Step 7:

[0289] The device transmits the emotion data acquired by the emotion engine to the server.

[0290] Step 8:

[0291] The server runs the user information, behavioral data, and emotional data through analytical algorithms to profile the user's interests and concerns.

[0292] Step 9:

[0293] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[0294] Step 10:

[0295] The server transmits the selected information to the terminal.

[0296] Step 11:

[0297] Based on the information received, the device displays notifications to the user at the appropriate time.

[0298] Step 12:

[0299] The device will activate the voice assistant and announce the notification to the user aloud. For example, it might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0300] Step 13:

[0301] Users can send questions or feedback about the suggested information to the device via voice or text.

[0302] Step 14:

[0303] The device sends user feedback to the server in real time.

[0304] Step 15:

[0305] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[0306] Step 16:

[0307] The server transmits the generated response to the terminal.

[0308] Step 17:

[0309] The device will then provide the answer to the user through the voice assistant, for example, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0310] Step 18:

[0311] The server aggregates data from multiple users and analyzes it globally.

[0312] Step 19:

[0313] The server generates updated data based on the global analysis results, distributing the most useful information and trends evenly to all users.

[0314] Step 20:

[0315] The server sends the generated update data to the terminal, and the terminal notifies the user of it.

[0316] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[0317] Example 2

[0318] 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."

[0319] Conventional information provision systems have struggled to accurately collect and analyze user behavioral data and emotional information, and provide optimal information to individual users in real time. Furthermore, methods for effectively utilizing user feedback and emotional data to optimize the system were limited. Furthermore, there was a lack of a mechanism for aggregating data from multiple users to provide equally useful information to all users. This made it challenging to provide truly useful information to users.

[0320] 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.

[0321] In this invention, the server includes a means for storing user information in a database, a means for periodically collecting user behavioral data, and a means for analyzing collected voice and facial expression data using an emotion engine. This enables highly accurate collection and analysis of user behavioral data and emotion data. Furthermore, optimal information can be provided to users in real time based on the analysis results, and feedback from users can be effectively utilized to optimize the system. Furthermore, data from multiple users can be aggregated and analyzed globally, enabling useful information to be provided equally to all users.

[0322] "User Information" refers to a user's profile information, including data such as age, gender, interests, and purpose.

[0323] "Terminal" refers to a device used by a user to input information, and refers to a mobile terminal such as a smartphone or tablet.

[0324] A "server" is a central computer that receives, stores, and analyzes user information and behavioral data, and communicates with multiple terminals over a network.

[0325] A "database" is an information storage system that systematically stores user information, behavioral data, emotional data, etc. on a server.

[0326] "Behavioral data" includes data about a user's operations and behavior, such as their app usage and website browsing history.

[0327] "Emotion data" is information about the emotional state extracted from the user's voice and facial expressions, and includes emotions such as joy, sadness, and anger.

[0328] An "emotion engine" is software or algorithm that analyzes a user's voice and facial expression data to recognize their emotional state.

[0329] An "analysis algorithm" is a calculation method used by the server to analyze user information, behavioral data, and emotional data to profile users' interests and concerns.

[0330] "Notifications" are messages or alerts that convey selected information from the server to the user via the terminal.

[0331] "Feedback" refers to reactions such as opinions and questions that users send in response to the information provided.

[0332] "Global analysis" refers to data analysis that aggregates data from multiple users and analyzes it over a wide area to provide useful information to all users.

[0333] A "voice assistant" is an interface that provides information through voice interaction with the user, and is a system that responds to the user's questions and commands via voice.

[0334] The present invention relates to an information provision system that combines an emotion engine that recognizes the emotions of a user. A specific embodiment of the information provision system will be described below.

[0335] System Configuration

[0336] This system mainly consists of a user's smartphone or other device, a central server, and a database. The operation of each element and how they work together are described in detail below.

[0337] First-time user information registration

[0338] Using a device such as a smartphone, a user accesses the initial registration screen and enters their profile information (age, gender, interests, goals, etc.). This information is sent by the device to the server, which then stores it in a database. For example, if a user enters information such as "Age: 30," "Interests: health, running," and "Goal: dieting," the device sends this information to the server using an HTTP request. The server analyzes the received information and executes an INSERT query in a relational database such as MySQL to store the information.

[0339] Behavioral data collection

[0340] Devices periodically record user behavioral data, such as app usage and website browsing history, in local storage. This data is then periodically sent to a server. Typically, this data is written to an SQLite database on the device and sent as a batch process. The server then analyzes the received behavioral data and stores it in the database.

[0341] Emotion recognition by emotion engine

[0342] The device collects the user's voice and facial expressions in real time and analyzes them using an emotion engine. This analysis uses tools such as the Emotion API from Microsoft® Azure® Cognitive Services. For example, when a user provides data through their smartphone's microphone or camera, the device analyzes it and sends it to the server as emotional data such as joy, sadness, or anger. The server then stores this data in a database.

[0343] Analyzing the data

[0344] The server runs an analysis algorithm based on the collected user information, behavioral data, and emotional data. Specifically, it uses machine learning models using Python's Pandas and Scikit-Learn. This allows it to profile users' interests and select appropriate information. The analysis results are stored in a database and appropriately tagged.

[0345] Selection and provision of information

[0346] The server selects the most appropriate information and advice for the user based on the analysis results. A recommendation algorithm is used for this selection. The selected information is sent to the device in JSON format as an HTTP response. The device then notifies the user of the information via push notification or a voice assistant (e.g., "Little Honda"). For example, the voice assistant might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0347] User interaction

[0348] When a user asks a question or gives feedback about the information provided by the device, the device sends it to the server. For example, if a user asks, "I'm interested in protein. Which product is specifically recommended?", the device sends this feedback to the server in real time. The server analyzes the question and uses a text analysis algorithm to generate a specific answer, which is then sent to the device. The device might respond, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0349] Eliminating the digital divide

[0350] The server aggregates data from multiple users, analyzes it globally, and provides the most useful information equally to all users. Specifically, it uses big data analysis tools (e.g., Apache Hadoop). This information is also stored in a database and notified equally to each user.

[0351] Prompt Sentence Examples

[0352] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[0353] As described above, the present invention realizes a system in which each element works in cooperation with each other to provide individually optimized information to each user.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] Step 1: Initial registration of user information

[0356] User: Accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.).

[0357] Input: Age, Gender, Interests, Purpose

[0358] Output: A dataset of the input user information

[0359] Terminal: Receives the entered user information and sends it to the server using an HTTP request.

[0360] Data processing: Convert user information into JSON format

[0361] Output: JSON data of user information sent to the server

[0362] Server: Analyzes the received user information and stores it in a database.

[0363] Input: JSON data of user information received by the server

[0364] Data operation: Inserting data into the database

[0365] Output: User information stored in the database

[0366] Step 2: Collect behavioral data

[0367] Device: Periodically records user behavioral data, such as app usage and website browsing history, in local storage.

[0368] Input: User operation history (app usage, website browsing history, etc.)

[0369] Data processing: Convert to a format that can be saved in local storage

[0370] Output: Behavioral data recorded in the device's local storage

[0371] Device: Sends recorded behavioral data to the server at regular intervals.

[0372] Input: Behavioral data recorded in local storage

[0373] Data calculation: Send to server using HTTP request

[0374] Output: Behavioral data sent to the server

[0375] Server: Stores the received behavioral data in a database.

[0376] Input: Behavioral data received by the server

[0377] Data operation: Inserting data into the database

[0378] Output: Behavioral data stored in a database

[0379] Step 3: Emotion recognition by the emotion engine

[0380] Device: Collects the user's voice and facial expressions in real time.

[0381] Input: User's voice data, facial expression data

[0382] Data processing: Collection of voice and facial expression data

[0383] Output: Collected voice data and facial expression data

[0384] Terminal: Uses an emotion engine to analyze the collected data.

[0385] Input: Collected voice data, facial expression data

[0386] Data calculation: Analysis by emotion engine (e.g. Emotion API)

[0387] Output: Emotion data (e.g., joy, sadness, anger, etc.)

[0388] Terminal: Sends the analysis results to the server.

[0389] Input: Emotion data

[0390] Data calculation: Send to server using HTTP request

[0391] Output: Emotion data sent to the server

[0392] Server: Stores the received emotion data in a database.

[0393] Input: Emotion data received by the server

[0394] Data operation: Inserting data into the database

[0395] Output: Emotion data stored in a database

[0396] Step 4: Analyze the data

[0397] Server: Runs analytical algorithms based on user information, behavioral data, and emotional data.

[0398] Input: User information, behavioral data, and emotional data stored in a database

[0399] Data Computation: Applying machine learning models using Python's Pandas and Scikit-Learn

[0400] Output: Analysis results

[0401] Server: Stores the analysis results in a database and tags them appropriately.

[0402] Input: Analysis results

[0403] Data processing: storing and tagging analysis results in a database

[0404] Output: Analysis results stored in a database

[0405] Step 5: Select and provide information

[0406] Server: Selects the most appropriate information and advice for users based on the analysis results.

[0407] Input: Analysis results stored in the database

[0408] Data operations: Applying recommendation algorithms

[0409] Output: Selected information

[0410] Server: Sends the selected information to the device.

[0411] Input: Selected information

[0412] Data calculation: sent as HTTP response in JSON format

[0413] Output: Information sent to the terminal

[0414] Terminal: Notifies the user of the received information.

[0415] Input: Information received from the server

[0416] Data calculation: Push notifications and voice assistant notifications

[0417] Output: Notification to the user (e.g., a message for a voice assistant)

[0418] Step 6: Interact with the user

[0419] Users: Ask questions or give feedback on the information provided.

[0420] Input: Questions or feedback on the proposed information

[0421] Data processing: Feedback description

[0422] Output: Feedback data

[0423] Device: Sends feedback to the server in real time.

[0424] Input: User feedback

[0425] Data calculation: Send to server using HTTP request

[0426] Output: Feedback data sent to the server

[0427] Server: Analyzes the question and generates information that addresses the user's additional needs.

[0428] Input: Feedback data

[0429] Data Computing: Applying Text Analysis Algorithms

[0430] Output: Additional information

[0431] Server: Sends the generated information to the terminal.

[0432] Input: Additional information

[0433] Data calculation: sent in HTTP response

[0434] Output: Additional information sent to the terminal

[0435] Terminal: Notify the user of additional information.

[0436] Input: Additional information received from the server

[0437] Data calculation: Push notifications and voice assistant notifications

[0438] Output: User notification (e.g., "The recommended protein product is XX brand YY.")

[0439] Step 7: Bridging the digital divide

[0440] Server: Aggregates data from multiple users and analyzes it globally.

[0441] Input: Multi-user dataset

[0442] Data calculation: Analysis using big data analysis tools (e.g., Apache Hadoop)

[0443] Output: Globally analyzed results

[0444] Server: Provides the most useful information equally to all users.

[0445] Input: Globally analyzed results

[0446] Data processing: Converting to the appropriate format and distributing information evenly

[0447] Output: Useful information notice to all users

[0448] Prompt Sentence Examples

[0449] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[0450] The above is the specific flow of the program processing of this system.

[0451] (Application example 2)

[0452] 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."

[0453] Conventional information delivery systems have difficulty providing information that takes into account the user's real-time emotions, and are therefore unable to provide optimal advertisements or information that reflect the user's current state or mood. Furthermore, methods for effectively utilizing user feedback and optimizing information delivery have been limited. As a result, there has been a problem in that the user experience has not been sufficiently improved.

[0454] 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.

[0455] In this invention, the server includes a means for analyzing the user's voice or facial expression to collect emotional data, a means for transmitting the emotional data to the server, and a means for analyzing the user information, behavioral data, and emotional data. This makes it possible to recognize the user's emotions in real time and provide optimal information and advertisements according to the user's emotional state. Furthermore, it is possible to quickly incorporate feedback from the user and improve the accuracy of the information provided.

[0456] An "information provision system" is a system that collects user information and provides appropriate data and content to users based on that information.

[0457] "User Information" refers to the profile data entered by a user when they first register, and includes information such as age, gender, interests, and purpose.

[0458] "Server" refers to a central processing unit that receives, stores, and analyzes user information, behavioral data, and emotional data, and provides appropriate information to users based on the analysis results.

[0459] A "database" is an information storage system for systematically accumulating and managing user information, behavioral data, emotional data, etc.

[0460] "User behavioral data" refers to data about a user's daily behavior, such as app usage and website browsing history.

[0461] "Emotional data" refers to data about the user's emotional state, such as joy, sadness, surprise, or anger, obtained by analyzing the user's voice and facial expressions.

[0462] An "emotion engine" is an analytical engine that analyzes input data such as voice and facial expressions to specifically identify the user's emotions.

[0463] An "analysis algorithm" is a mathematical processing method for profiling users' interests and concerns by comprehensively analyzing user information, behavioral data, and emotional data.

[0464] "Feedback" refers to the opinions and reactions that users give to the information they provide, and is important data that allows the system to provide even more optimal information based on this.

[0465] "Notifications" are messaging methods used to communicate selected information or advertisements to users.

[0466] An "ad serving app" is software that selects and serves optimal advertisements based on the user's emotional state and behavioral data.

[0467] A "voice assistant" is an interface that communicates with users via voice and provides necessary information.

[0468] This invention is an advertising system that recognizes a user's emotions in real time and provides optimal information and advertisements according to that emotional state. This system comprehensively analyzes user information, behavioral data, and emotional data, and provides appropriate feedback to the user.

[0469] System Configuration

[0470] The system mainly consists of a user terminal, a server, a database, an emotion engine, and an advertisement serving engine.

[0471] User device roles

[0472] User terminals refer to devices such as smartphones and smart glasses, and perform the following functions:

[0473] 1. Initial registration of user information

[0474] The user accesses the initial registration screen and enters their profile data (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0475] 2. Collecting behavioral data

[0476] The device periodically collects behavioral data, such as the user's app usage and website browsing history, and appropriately adjusts the timing of sending this data to the server.

[0477] 3. Collecting Emotional Data

[0478] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data, for example, by using libraries such as OpenCV and FaceNet for facial recognition and by converting voice data into text using Google Cloud Speech-to-Text.

[0479] Server Roles

[0480] The server has the following roles:

[0481] 1. Data Analysis

[0482] The user information, behavioral data, and emotional data are received and analyzed comprehensively using big data analysis tools (e.g., Apache Hadoop and TensorFlow). This analysis allows the system to select optimal information and advertisements based on the user's current emotional state and past behavioral patterns.

[0483] 2. Providing information and advertising

[0484] Based on the analysis results, we select information and advertisements appropriate for the user and send them to the user's device. For example, we provide advertisements using an advertisement delivery system (e.g., Magento).

[0485] 3. Processing Feedback

[0486] We collect user feedback and analyze it again, and use this feedback analysis to further optimize our advertising and information provision strategies.

[0487] Specific Examples

[0488] For example, when a user uses a smartphone, the camera captures the user's facial expressions and simultaneously collects their vocal responses. The collected data is sent to a server in real time, where an emotion analysis engine (e.g., IBM Watson® Tone Analyzer) identifies the emotion. Big data analysis tools are then used to comprehensively analyze the user's current emotional state and past behavioral data, and the most appropriate advertisements are selected and delivered.

[0489] Prompt Sentence Examples

[0490] The following prompt can be used to generate the code for the sentiment analysis part of the generative AI model:

[0491] Generate Python code that has the following functionality:

[0492] Real-time face detection from webcam using OpenCV.

[0493] Uses Google Cloud Speech-to-Text to convert what the user says into text in real time.

[0494] Identify emotions from text using IBM Watson Tone Analyzer.

[0495] Emotion data is saved to local storage at regular intervals.

[0496] In this way, specific steps for implementing the present invention can be clearly defined, making it possible to provide information and advertisements that are optimal for the user's emotional state in real time.

[0497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0498] Step 1: Initial registration of user information

[0499] A user accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores the received information in a database. In this step, the input data is the user's profile information, and the output data is the user information stored in the database.

[0500] Step 2: Collect behavioral data

[0501] The device periodically records user behavioral data, such as app usage and website browsing history, in local storage. This data is then sent to the server at regular intervals. The input data in this step is the user behavioral data, and the output data is the behavioral history sent to the server.

[0502] Step 3: Collecting emotion data

[0503] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It performs facial recognition using OpenCV and FaceNet, and converts the voice into text using Google Cloud Speech-to-Text. Based on this data, it uses an emotion analysis engine such as IBM Watson Tone Analyzer to obtain emotion data, which is then sent to the server. The input data in this step are the user's facial expressions and voice information, and the output data is the emotion data sent to the server.

[0504] Step 4: Data synthesis and analysis

[0505] The server receives user information, behavioral data, and emotional data and performs an integrated analysis of them. Big data analysis tools such as Apache Hadoop and TensorFlow are used to profile users' interests, concerns, and emotional states. In this process, the input data are the various types of user data received, and the output data is profile information resulting from the analysis.

[0506] Step 5: Information and advertising selection

[0507] The server selects appropriate information and advertisements based on the results of the integrated analysis. For example, if a user is feeling depressed, it selects entertainment-related advertisements to help them relax. It selects the optimal advertisements using an advertisement delivery system such as Magento. The input data in this step are the analysis results, and the output data is the selected advertisement information.

[0508] Step 6: Information and advertising notices

[0509] The device notifies the user of the information and advertisements received from the server. This can be done, for example, via push notifications or a voice assistant. The notification may also include a message prompting the user for feedback. The input data in this step is the selected advertisement information, and the output data is the information notified to the user.

[0510] Step 7: Gather feedback and reanalyze

[0511] When a user responds to an advertisement or information (by clicking, asking a question, commenting, etc.), that feedback is sent from the device to the server. The server then analyzes this feedback again and uses it to optimize the advertising strategy. The input data in this step is the user feedback, and the output data is the improved advertising strategy as a result of the reanalysis.

[0512] Step 8: Analyzing individual and global data

[0513] The server globally analyzes data collected from multiple users and seeks ways to provide information equally. This reduces the information gap and ensures that useful information reaches all users. In this process, the input data is the data of all users, and the output data is an optimized information provision strategy as a result of the analysis.

[0514] 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.

[0515] 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.

[0516] 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.

[0517] [Second embodiment]

[0518] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0519] 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.

[0520] 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).

[0521] 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.

[0522] 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.

[0523] 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).

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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."

[0530] The present invention relates to a system in which a device such as a smartphone speaks to a user spontaneously and provides necessary information and advice. Specific program processing and its flow are described below for an embodiment of the present invention.

[0531] Basic System Configuration

[0532] 1. Initial registration of user information

[0533] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0534] 2. Collecting behavioral data

[0535] The device periodically records user behavior data (such as app usage and website browsing history), and then periodically transmits this data to a server.

[0536] 3. Data Analysis

[0537] The server runs analytical algorithms based on the received user information and behavioral data to profile the user's interests and concerns.

[0538] 4. Selection and provision of information

[0539] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "There are some recent health articles based on your interests. Please take a look."

[0540] 5. User interaction

[0541] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[0542] 6. Closing the digital divide

[0543] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[0544] Specific examples

[0545] Example of initial registration and information provision

[0546] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[0547] Terminal: Sends the entered information to the server.

[0548] Server: Stores user information in a database and analyzes recent health trends.

[0549] Server: Selects health articles that are useful to the user and sends them to the device.

[0550] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[0551] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[0552] Terminal: Sends user feedback to the server in real time.

[0553] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[0554] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0555] This allows the entire system to work together to provide information to users and further optimize the process based on their feedback.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[0559] Step 2:

[0560] The terminal receives the entered user information and sends it to the server.

[0561] Step 3:

[0562] The server stores the user information in a database.

[0563] Step 4:

[0564] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[0565] Step 5:

[0566] The device periodically transmits the collected behavioral data to the server.

[0567] Step 6:

[0568] The server then runs the received behavioral data, along with user information, through an analytical algorithm to profile the user's interests and concerns.

[0569] Step 7:

[0570] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[0571] Step 8:

[0572] The server transmits the selected information to the terminal.

[0573] Step 9:

[0574] Based on the information received, the device displays notifications to the user at the appropriate time.

[0575] Step 10:

[0576] The device will then activate the voice assistant and verbally notify the user of the notification, which will be something like, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0577] Step 11:

[0578] Users can send questions or feedback about the suggested information to the device via voice or text.

[0579] Step 12:

[0580] The device sends user feedback to the server in real time.

[0581] Step 13:

[0582] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[0583] Step 14:

[0584] The server transmits the generated response to the terminal.

[0585] Step 15:

[0586] The device will then provide the answer to the user through the voice assistant, such as, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0587] Step 16:

[0588] The server aggregates data from multiple users and analyzes it globally.

[0589] Step 17:

[0590] The server extracts the most useful information and trends and distributes updated data to devices to provide uniform information to all users.

[0591] Example 1

[0592] 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."

[0593] Conventional information provision systems face the challenge of providing optimal information to individual users when collecting and analyzing user information and behavioral data. It is also difficult to effectively utilize user feedback when providing information. Furthermore, there are insufficient means for aggregating data from multiple users and analyzing and providing it from a global perspective. For these reasons, there is a need to improve the quality of information provided to users and continuously optimize it based on feedback.

[0594] 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.

[0595] In this invention, the server includes means for analyzing a user's natural language input using a generative AI model and generating an appropriate response, means for providing the generated response to the user through a voice assistant function, and means for aggregating and globally analyzing data from multiple users to distribute uniform information to all users. This enables optimal information to be provided to individual users, and the quality of information provided can be improved by effectively utilizing user feedback. Furthermore, by integrating and analyzing data from multiple users, uniform information provision from a global perspective can also be achieved.

[0596] "User information" refers to profile data entered by the user, such as age, gender, interests, and purpose.

[0597] A "server" is a central processing unit that stores and analyzes user information and behavioral data and provides appropriate information.

[0598] A "database" is a system managed by a server for efficiently storing, searching, and updating user information and behavioral data.

[0599] "Behavioral data" refers to data such as usage and browsing history when a user uses an app or website.

[0600] "Analysis" is the act of using collected user information and behavioral data to run specific algorithms and profile users' interests and concerns.

[0601] "Feedback" refers to questions, reactions, opinions, etc. that users have in response to the information provided.

[0602] A "generative AI model" is an artificial intelligence model that analyzes a user's natural language input and generates an appropriate response based on it.

[0603] The "voice assistant function" is a function that allows users to interact with the device via voice and provide necessary information.

[0604] "Global analysis" refers to large-scale data analysis that aggregates data from multiple users and extracts and provides the most useful information overall.

[0605] This invention relates to an information provision system in which a device such as a smartphone speaks to the user spontaneously and provides necessary information and advice. This system is mainly composed of three elements: a server, a device, and a user.

[0606] First-time user information registration

[0607] Users launch a dedicated application on their smartphone or other device and access the initial registration screen. They enter their profile information, such as age, gender, interests, and purpose, into the registration form. The device serializes the entered information into JSON format and sends it to the server using the HTTPS protocol.

[0608] Storing information on the server

[0609] The server parses the received JSON data and stores it in a relational database management system (e.g., MySQL). The stored information is stored in a user attribute table (age and gender), and a category table (interests and goals).

[0610] Behavioral data collection

[0611] The device periodically records user behavioral data, such as app usage and website browsing history, using SQLite as the database. The collected data is periodically sent to the server in CSV format.

[0612] Analyzing the data

[0613] The server uses Python libraries (e.g., Pandas, Scikit-learn) to analyze user information and behavioral data, profiling user interests and constructing user profiles using generative AI models (e.g., GPT-4).

[0614] Selection and provision of information

[0615] The server retrieves relevant topics using a news API or similar based on the generated user profile. The selected information is serialized in JSON format and sent to the device. The device then notifies the user through its voice assistant function using the Google Assistant SDK or Amazon Alexa SDK.

[0616] User interaction

[0617] When a user provides feedback or asks a question, the device converts it into a string using the Google Cloud Speech-to-Text API and sends it to the server. The server then uses a generative AI model (GPT-4) to analyze the question and generate an appropriate response. The generated response is then sent to the device and conveyed to the user via the voice assistant.

[0618] Global data analysis and information provision

[0619] The server aggregates data from multiple users and analyzes it globally using big data analysis tools such as Apache Hadoop and Spark. Based on the results of this analysis, information is provided equally to reduce the digital divide.

[0620] As a specific example, if a 30-year-old woman is interested in health and running and is trying to lose weight, the server will provide appropriate information such as, "Recently, protein has been said to be effective for dieting. Please read this article." Also, if the user asks, "Which specific protein is good?", the server will provide a specific answer such as, "The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0621] Prompt Sentence Examples

[0622] Generative AI model: GPT-4

[0623] Prompt: "For a 30-year-old woman interested in health and running who is looking to lose weight, please recommend a protein product. Please include specific brands and reviews."

[0624] The above is an embodiment of the present invention.

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] First-time user information registration

[0628] User: Launches the dedicated smartphone app and accesses the initial registration screen. The user enters profile information such as age, gender, interests, and purpose.

[0629] Terminal: User input information is serialized in JSON format and sent to the server using the HTTPS protocol.

[0630] Server: Parse the received JSON data and save it in a MySQL database to store age and gender in a user attribute table and interests and goals in a category table. This saves user information in a structured format in the database.

[0631] Step 2:

[0632] Behavioral data collection

[0633] Device: Periodically records your app usage and website browsing history. This data includes the amount of time you spend using apps and the URLs of websites you visit.

[0634] Terminal: Collected behavioral data is sent to the server in batches every certain period (for example, every hour). Data is often sent in CSV format.

[0635] Server: Analyzes the received CSV data, stores it in the action log table with a timestamp, and saves it in the database.

[0636] Step 3:

[0637] Analyzing the data

[0638] Server: Analyzes the received user information and behavioral data using Python libraries such as Pandas and Scikit-learn.

[0639] Server: Runs a clustering algorithm to build user profiles, specifically using K-means clustering to group users by their interests.

[0640] Server: Based on the generated user profile, a generative AI model (GPT-4) is used to predict information based on the user's interests.

[0641] Step 4:

[0642] Selection and provision of information

[0643] Server: Calls the news API to get the latest articles on relevant topics based on the user's interests.

[0644] Server: Serializes the selected information into JSON format and sends it to the device.

[0645] Device: Analyzes the received information and notifies the user at the appropriate time using the voice assistant function. For example, using the Google Assistant SDK, it can say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0646] Step 5:

[0647] User interaction

[0648] User: Reads the suggested article and asks questions or gives feedback about the content. For example, "I'm interested in protein, which specific product is good?"

[0649] On the device: Recognizes the user's voice and converts it into text, using the Google Cloud Speech-to-Text API.

[0650] Terminal: Sends the converted text to the server.

[0651] Server: Analyzes the user's question using a generative AI model (GPT-4) and generates an appropriate answer, such as "The recommended protein product is XX brand YY. The reviews for this product are also very positive."

[0652] Device: The generated answer is synthesized into speech and conveyed to the user.

[0653] Step 6:

[0654] Global data analysis and information provision

[0655] Server: Uses big data analysis tools such as Apache Hadoop and Spark to statistically analyze all collected user data.

[0656] Server: Extracts the most useful information from the acquired data and generates a model to provide it to all users equally.

[0657] Server: Use this model to continuously improve the quality of information provision and reduce the digital divide.

[0658] (Application example 1)

[0659] 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."

[0660] Conventional information provision systems lack the functionality to suggest optimal meals based on a user's dietary preferences and health status, making it difficult to link appropriate meal suggestions for individual users with food delivery services. Furthermore, information cannot be updated in real time based on user feedback, making it difficult to provide timely information. The objective of this invention is to solve these problems and provide an information provision system that suggests optimal meals based on a user's health and preferences and provides a smooth service to users in collaboration with food delivery services.

[0661] 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.

[0662] In this invention, the server includes means for suggesting meals and processing food delivery orders, means for inputting user information, means for transmitting the user information to the server, means for storing the user information in a database, means for collecting user behavioral data, means for transmitting the behavioral data to the server, means for analyzing the user information and behavioral data, means for selecting information appropriate for the user based on the analysis results, means for notifying the user of the selected information, means for receiving feedback from the user via the notification, means for transmitting the feedback to the server and re-analyzing it, means for providing the user with additional information based on the analysis results of the feedback, and means for aggregating data from multiple users, analyzing it globally, and distributing uniform information to all users. This enables smooth integration of optimal meal suggestions tailored to the health and preferences of each individual user with food delivery services.

[0663] An "information provision system" is a system designed to provide users with the information and services they need.

[0664] "User Information" refers to individual data about you, such as your age, gender, health status, and dietary preferences.

[0665] A "server" is a computer system that acts as a central control device, managing, storing, and analyzing user information and behavioral data.

[0666] A "database" is a continuous collection of data that systematically stores and manages collected user information and behavioral data.

[0667] "Behavioral data" refers to information about a user's behavior, such as their app usage and food ordering history.

[0668] An "analysis algorithm" is a calculation method used to profile users and select information based on collected data.

[0669] "Notifying" refers to the act of conveying selected information to a user in the form of voice, text, or other means.

[0670] "Feedback" refers to information provided by users in the form of responses, comments, questions, etc.

[0671] "Meal Suggestion" refers to the act of recommending optimal meal plans and menus based on a user's health status and preferences.

[0672] "Food delivery" refers to the process of providing meals selected by users through a delivery service.

[0673] MODE FOR CARRYING OUT THE INVENTION

[0674] System configuration and programs

[0675] The system for implementing this invention includes an information provision system that suggests optimal meals based on the user's health condition and dietary preferences and works in conjunction with a food delivery service. The main components of the system and their functions are as follows:

[0676] 1. Initial registration of user information

[0677] Users access the initial registration screen using a device such as a smartphone and enter information such as their name, age, gender, allergies, dietary preferences, and health status. The device sends the entered information to a server, which then stores it in a database.

[0678] 2. Collecting behavioral data

[0679] The device records behavioral data such as the meals ordered by the user, delivery history, and app usage, and this data is periodically sent to the server.

[0680] 3. Data Analysis

[0681] The server runs analytics algorithms to profile health and preferences based on the received user information and behavioral data, using software such as MySQL and Postgres for database management and Pytorch and TensorFlow for analytics algorithms.

[0682] 4. Selection and provision of information

[0683] Based on the analysis results, the server selects the most suitable meal suggestions and information on food delivery services for the user. The selected information is sent to the device, which notifies the user at the appropriate time. For example, the voice assistant may notify the user, "Today's recommendation is low-carb chicken salad. Would you like to order it?"

[0684] 5. User interaction

[0685] When a user submits feedback on a suggestion, the information is sent to the server in real time and analyzed again. Additional suggestions and detailed information are generated based on the feedback and provided to the user via their device. For example, if a user asks, "Do you have a menu with a slightly larger portion?", the server analyzes and responds with the appropriate menu item.

[0686] 6. Closing the digital divide

[0687] The server aggregates data collected from multiple users and analyzes it globally, making it possible to provide equally useful information to all users, thereby eliminating the information gap.

[0688] Specific examples

[0689] First-time registration and information provision process

[0690] User: On the initial registration screen, enter the following information: "Age: 30," "Gender: Male," "Health condition: On a low-carb diet," "Food preference: Japanese food," and "Allergies: None."

[0691] Terminal: Sends the entered information to the server.

[0692] Server: Stores user information in a database and runs analytical algorithms to generate user profiles.

[0693] Server: Selects the best meal suggestions for the user and sends the information to the device.

[0694] Device: Announces, "We recommend the low-carb chicken salad. Would you like to order it?"

[0695] Examples of user interactions

[0696] User: "Do you have something a little more substantial?"

[0697] Server: Analyzes the feedback and suggests, "How about a chicken steak and salad combo?"

[0698] The generative AI model used is GPT-4 (OpenAI), which generates information appropriate for the user using the following prompt sentences:

[0699] Example prompt:

[0700] User profile information: "30 years old, male, on a low-carb diet, no allergies"

[0701] Sample prompt: "Suggest a low-carb lunch option that would work best for your target audience. Specifically, choose from chicken salad or grilled chicken."

[0702] In this way, the system of the present invention provides optimal meal suggestions and food delivery services based on the user's health and dietary preferences.

[0703] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0704] Step 1:

[0705] First time user registration:

[0706] The user accesses the initial registration screen using a smartphone and enters their name, age, gender, health condition, dietary preferences, allergy information, etc. The device formats the entered information in JSON format and sends it to the server using an HTTP POST request. The server stores the received data in a database. The server uses MySQL as its database management system and adds the user information to a table.

[0707] Step 2:

[0708] Behavioral Data Collection:

[0709] The device records user behavioral data, such as app usage and past order history, in a local data store. Using a periodic scheduler (e.g., a cron job), the local data is sent to the server. This data is also formatted in JSON and sent to the server via an HTTP POST request. The server stores the received data in a database.

[0710] Step 3:

[0711] Data analysis:

[0712] The server periodically runs batch processes to analyze user information and behavioral data stored in the database. The server uses Python scripts and TensorFlow to run machine learning models to create user profiles that reflect the user's behavioral patterns and preferences. The analysis results are stored in the database.

[0713] Step 4:

[0714] Selection and provision of information:

[0715] Based on the analysis results, the server selects the most suitable meal suggestion for the user. The generative AI model used is GPT-4 (OpenAI), which generates the prompt text. A sample prompt text includes user profile information, as well as a message such as, "Please suggest the best low-carb lunch menu for the target user. Specifically, please choose from chicken salad, grilled chicken, etc." The generated suggestion text is sent to the device in JSON format.

[0716] Step 5:

[0717] Notice to users:

[0718] The device analyzes the information it receives and notifies the user using a voice assistant or push notification. For example, it might say, "We recommend a low-carb chicken salad. Would you like to order it?" It may also ask for user feedback.

[0719] Step 6:

[0720] Collecting user feedback:

[0721] Users provide feedback through voice or text input, which the device collects in real time and sends via HTTP POST requests to the server, which stores the received feedback in a database.

[0722] Step 7:

[0723] Feedback reanalysis:

[0724] The server analyzes the feedback and runs the machine learning model again to identify additional user needs. Based on the new analysis, it generates additional information and suggestions, which are stored in a database and sent to the device.

[0725] Step 8:

[0726] Bridging the digital divide:

[0727] The server aggregates all user data and performs global analysis to identify the most useful information, which is then distributed equally to all users through periodic reports and notifications to the broad user base.

[0728] Step 9:

[0729] Food delivery ordering process:

[0730] If the user accepts the suggestion, the device will call the food delivery service's API to complete the order, and the order details will be sent to the server and saved as an order history.

[0731] 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.

[0732] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[0733] Basic System Configuration

[0734] 1. Initial registration of user information

[0735] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0736] 2. Collecting behavioral data

[0737] The device periodically records user behavior data (such as app usage and website browsing history) in local storage, and then periodically transmits this data to the server.

[0738] 3. Emotion Recognition by Emotion Engine

[0739] The device collects emotional data from the user's voice and facial expressions, and uses an emotion engine to analyze multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time. The analysis results are sent to a server.

[0740] 4. Data Analysis

[0741] The server runs analytical algorithms to profile the user's interests and concerns based on the received user information, behavioral data, and emotional data.

[0742] 5. Selection and provision of information

[0743] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "I sensed that you've been interested in health recently. Please take a look at this health article."

[0744] 6. User interaction

[0745] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[0746] 7. Closing the digital divide

[0747] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[0748] Specific examples

[0749] Example of initial registration and information provision

[0750] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[0751] Terminal: Sends the entered information to the server.

[0752] Server: Stores user information in a database and analyzes recent health trends.

[0753] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server.

[0754] Server: Selects the most appropriate information based on the user's behavioral and emotional data and sends it to the device.

[0755] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[0756] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[0757] Terminal: Sends user feedback to the server in real time.

[0758] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[0759] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0760] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[0761] The processing flow will be explained below.

[0762] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[0763] Basic System Configuration

[0764] Step 1:

[0765] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[0766] Step 2:

[0767] The terminal transmits the entered user information to the server.

[0768] Step 3:

[0769] The server stores the user information in a database.

[0770] Step 4:

[0771] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[0772] Step 5:

[0773] The device periodically transmits the collected behavioral data to the server.

[0774] Step 6:

[0775] The device uses an emotion engine to analyze the user's voice data and facial expression data captured by the camera, recognizing multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time.

[0776] Step 7:

[0777] The device transmits the emotion data acquired by the emotion engine to the server.

[0778] Step 8:

[0779] The server runs the user information, behavioral data, and emotional data through analytical algorithms to profile the user's interests and concerns.

[0780] Step 9:

[0781] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[0782] Step 10:

[0783] The server transmits the selected information to the terminal.

[0784] Step 11:

[0785] Based on the information received, the device displays notifications to the user at the appropriate time.

[0786] Step 12:

[0787] The device will activate the voice assistant and announce the notification to the user aloud. For example, it might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0788] Step 13:

[0789] Users can send questions or feedback about the suggested information to the device via voice or text.

[0790] Step 14:

[0791] The device sends user feedback to the server in real time.

[0792] Step 15:

[0793] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[0794] Step 16:

[0795] The server transmits the generated response to the terminal.

[0796] Step 17:

[0797] The device will then provide the answer to the user through the voice assistant, for example, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0798] Step 18:

[0799] The server aggregates data from multiple users and analyzes it globally.

[0800] Step 19:

[0801] The server generates updated data based on the global analysis results, distributing the most useful information and trends evenly to all users.

[0802] Step 20:

[0803] The server sends the generated update data to the terminal, and the terminal notifies the user of it.

[0804] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[0805] Example 2

[0806] 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."

[0807] Conventional information provision systems have struggled to accurately collect and analyze user behavioral data and emotional information, and provide optimal information to individual users in real time. Furthermore, methods for effectively utilizing user feedback and emotional data to optimize the system were limited. Furthermore, there was a lack of a mechanism for aggregating data from multiple users to provide equally useful information to all users. This made it challenging to provide truly useful information to users.

[0808] 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.

[0809] In this invention, the server includes a means for storing user information in a database, a means for periodically collecting user behavioral data, and a means for analyzing collected voice and facial expression data using an emotion engine. This enables highly accurate collection and analysis of user behavioral data and emotion data. Furthermore, optimal information can be provided to users in real time based on the analysis results, and feedback from users can be effectively utilized to optimize the system. Furthermore, data from multiple users can be aggregated and analyzed globally, enabling useful information to be provided equally to all users.

[0810] "User Information" refers to a user's profile information, including data such as age, gender, interests, and purpose.

[0811] "Terminal" refers to a device used by a user to input information, and refers to a mobile terminal such as a smartphone or tablet.

[0812] A "server" is a central computer that receives, stores, and analyzes user information and behavioral data, and communicates with multiple terminals over a network.

[0813] A "database" is an information storage system that systematically stores user information, behavioral data, emotional data, etc. on a server.

[0814] "Behavioral data" includes data about a user's operations and behavior, such as their app usage and website browsing history.

[0815] "Emotion data" is information about the emotional state extracted from the user's voice and facial expressions, and includes emotions such as joy, sadness, and anger.

[0816] An "emotion engine" is software or algorithm that analyzes a user's voice and facial expression data to recognize their emotional state.

[0817] An "analysis algorithm" is a calculation method used by the server to analyze user information, behavioral data, and emotional data to profile users' interests and concerns.

[0818] "Notifications" are messages or alerts that convey selected information from the server to the user via the terminal.

[0819] "Feedback" refers to reactions such as opinions and questions that users send in response to the information provided.

[0820] "Global analysis" refers to data analysis that aggregates data from multiple users and analyzes it over a wide area to provide useful information to all users.

[0821] A "voice assistant" is an interface that provides information through voice interaction with the user, and is a system that responds to the user's questions and commands via voice.

[0822] The present invention relates to an information provision system that combines an emotion engine that recognizes the emotions of a user. A specific embodiment of the information provision system will be described below.

[0823] System Configuration

[0824] This system mainly consists of a user's smartphone or other device, a central server, and a database. The operation of each element and how they work together are described in detail below.

[0825] First-time user information registration

[0826] Using a device such as a smartphone, a user accesses the initial registration screen and enters their profile information (age, gender, interests, goals, etc.). This information is sent by the device to the server, which then stores it in a database. For example, if a user enters information such as "Age: 30," "Interests: health, running," and "Goal: dieting," the device sends this information to the server using an HTTP request. The server analyzes the received information and executes an INSERT query in a relational database such as MySQL to store the information.

[0827] Behavioral data collection

[0828] Devices periodically record user behavioral data, such as app usage and website browsing history, in local storage. This data is then periodically sent to a server. Typically, this data is written to an SQLite database on the device and sent as a batch process. The server then analyzes the received behavioral data and stores it in the database.

[0829] Emotion recognition by emotion engine

[0830] The device collects the user's voice and facial expressions in real time and analyzes them using an emotion engine. This analysis uses the Emotion API from Microsoft Azure's Cognitive Services. For example, when a user provides data through their smartphone's microphone or camera, the device analyzes it and sends it to the server as emotional data such as joy, sadness, or anger. The server then stores this data in a database.

[0831] Analyzing the data

[0832] The server runs an analysis algorithm based on the collected user information, behavioral data, and emotional data. Specifically, it uses machine learning models using Python's Pandas and Scikit-Learn. This allows it to profile users' interests and select appropriate information. The analysis results are stored in a database and appropriately tagged.

[0833] Selection and provision of information

[0834] The server selects the most appropriate information and advice for the user based on the analysis results. A recommendation algorithm is used for this selection. The selected information is sent to the device in JSON format as an HTTP response. The device then notifies the user of the information via push notification or a voice assistant (e.g., "Little Honda"). For example, the voice assistant might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[0835] User interaction

[0836] When a user asks a question or gives feedback about the information provided by the device, the device sends it to the server. For example, if a user asks, "I'm interested in protein. Which product is specifically recommended?", the device sends this feedback to the server in real time. The server analyzes the question and uses a text analysis algorithm to generate a specific answer, which is then sent to the device. The device might respond, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[0837] Eliminating the digital divide

[0838] The server aggregates data from multiple users, analyzes it globally, and provides the most useful information equally to all users. Specifically, it uses big data analysis tools (e.g., Apache Hadoop). This information is also stored in a database and notified equally to each user.

[0839] Prompt Sentence Examples

[0840] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[0841] As described above, the present invention realizes a system in which each element works in cooperation with each other to provide individually optimized information to each user.

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1: Initial registration of user information

[0844] User: Accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.).

[0845] Input: Age, Gender, Interests, Purpose

[0846] Output: A dataset of the input user information

[0847] Terminal: Receives the entered user information and sends it to the server using an HTTP request.

[0848] Data processing: Convert user information into JSON format

[0849] Output: JSON data of user information sent to the server

[0850] Server: Analyzes the received user information and stores it in a database.

[0851] Input: JSON data of user information received by the server

[0852] Data operation: Inserting data into the database

[0853] Output: User information stored in the database

[0854] Step 2: Collect behavioral data

[0855] Device: Periodically records user behavioral data, such as app usage and website browsing history, in local storage.

[0856] Input: User operation history (app usage, website browsing history, etc.)

[0857] Data processing: Convert to a format that can be saved in local storage

[0858] Output: Behavioral data recorded in the device's local storage

[0859] Device: Sends recorded behavioral data to the server at regular intervals.

[0860] Input: Behavioral data recorded in local storage

[0861] Data calculation: Send to server using HTTP request

[0862] Output: Behavioral data sent to the server

[0863] Server: Stores the received behavioral data in a database.

[0864] Input: Behavioral data received by the server

[0865] Data operation: Inserting data into the database

[0866] Output: Behavioral data stored in a database

[0867] Step 3: Emotion recognition by the emotion engine

[0868] Device: Collects the user's voice and facial expressions in real time.

[0869] Input: User's voice data, facial expression data

[0870] Data processing: Collection of voice and facial expression data

[0871] Output: Collected voice data and facial expression data

[0872] Terminal: Uses an emotion engine to analyze the collected data.

[0873] Input: Collected voice data, facial expression data

[0874] Data calculation: Analysis by emotion engine (e.g. Emotion API)

[0875] Output: Emotion data (e.g., joy, sadness, anger, etc.)

[0876] Terminal: Sends the analysis results to the server.

[0877] Input: Emotion data

[0878] Data calculation: Send to server using HTTP request

[0879] Output: Emotion data sent to the server

[0880] Server: Stores the received emotion data in a database.

[0881] Input: Emotion data received by the server

[0882] Data operation: Inserting data into the database

[0883] Output: Emotion data stored in a database

[0884] Step 4: Analyze the data

[0885] Server: Runs analytical algorithms based on user information, behavioral data, and emotional data.

[0886] Input: User information, behavioral data, and emotional data stored in a database

[0887] Data Computation: Applying machine learning models using Python's Pandas and Scikit-Learn

[0888] Output: Analysis results

[0889] Server: Stores the analysis results in a database and tags them appropriately.

[0890] Input: Analysis results

[0891] Data processing: storing and tagging analysis results in a database

[0892] Output: Analysis results stored in a database

[0893] Step 5: Select and provide information

[0894] Server: Selects the most appropriate information and advice for users based on the analysis results.

[0895] Input: Analysis results stored in the database

[0896] Data operations: Applying recommendation algorithms

[0897] Output: Selected information

[0898] Server: Sends the selected information to the device.

[0899] Input: Selected information

[0900] Data calculation: sent as HTTP response in JSON format

[0901] Output: Information sent to the terminal

[0902] Terminal: Notifies the user of the received information.

[0903] Input: Information received from the server

[0904] Data calculation: Push notifications and voice assistant notifications

[0905] Output: Notification to the user (e.g., a message for a voice assistant)

[0906] Step 6: Interact with the user

[0907] Users: Ask questions or give feedback on the information provided.

[0908] Input: Questions or feedback on the proposed information

[0909] Data processing: Feedback description

[0910] Output: Feedback data

[0911] Device: Sends feedback to the server in real time.

[0912] Input: User feedback

[0913] Data calculation: Send to server using HTTP request

[0914] Output: Feedback data sent to the server

[0915] Server: Analyzes the question and generates information that addresses the user's additional needs.

[0916] Input: Feedback data

[0917] Data Computing: Applying Text Analysis Algorithms

[0918] Output: Additional information

[0919] Server: Sends the generated information to the terminal.

[0920] Input: Additional information

[0921] Data calculation: sent in HTTP response

[0922] Output: Additional information sent to the terminal

[0923] Terminal: Notify the user of additional information.

[0924] Input: Additional information received from the server

[0925] Data calculation: Push notifications and voice assistant notifications

[0926] Output: User notification (e.g., "The recommended protein product is XX brand YY.")

[0927] Step 7: Bridging the digital divide

[0928] Server: Aggregates data from multiple users and analyzes it globally.

[0929] Input: Multi-user dataset

[0930] Data calculation: Analysis using big data analysis tools (e.g., Apache Hadoop)

[0931] Output: Globally analyzed results

[0932] Server: Provides the most useful information equally to all users.

[0933] Input: Globally analyzed results

[0934] Data processing: Converting to the appropriate format and distributing information evenly

[0935] Output: Useful information notice to all users

[0936] Prompt Sentence Examples

[0937] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[0938] The above is the specific flow of the program processing of this system.

[0939] (Application example 2)

[0940] 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."

[0941] Conventional information delivery systems have difficulty providing information that takes into account the user's real-time emotions, and are therefore unable to provide optimal advertisements or information that reflect the user's current state or mood. Furthermore, methods for effectively utilizing user feedback and optimizing information delivery have been limited. As a result, there has been a problem in that the user experience has not been sufficiently improved.

[0942] 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.

[0943] In this invention, the server includes a means for analyzing the user's voice or facial expression to collect emotional data, a means for transmitting the emotional data to the server, and a means for analyzing the user information, behavioral data, and emotional data. This makes it possible to recognize the user's emotions in real time and provide optimal information and advertisements according to the user's emotional state. Furthermore, it is possible to quickly incorporate feedback from the user and improve the accuracy of the information provided.

[0944] An "information provision system" is a system that collects user information and provides appropriate data and content to users based on that information.

[0945] "User Information" refers to the profile data entered by a user when they first register, and includes information such as age, gender, interests, and purpose.

[0946] "Server" refers to a central processing unit that receives, stores, and analyzes user information, behavioral data, and emotional data, and provides appropriate information to users based on the analysis results.

[0947] A "database" is an information storage system for systematically accumulating and managing user information, behavioral data, emotional data, etc.

[0948] "User behavioral data" refers to data about a user's daily behavior, such as app usage and website browsing history.

[0949] "Emotional data" refers to data about the user's emotional state, such as joy, sadness, surprise, or anger, obtained by analyzing the user's voice and facial expressions.

[0950] An "emotion engine" is an analytical engine that analyzes input data such as voice and facial expressions to specifically identify the user's emotions.

[0951] An "analysis algorithm" is a mathematical processing method for profiling users' interests and concerns by comprehensively analyzing user information, behavioral data, and emotional data.

[0952] "Feedback" refers to the opinions and reactions that users give to the information they provide, and is important data that allows the system to provide even more optimal information based on this.

[0953] "Notifications" are messaging methods used to communicate selected information or advertisements to users.

[0954] An "ad serving app" is software that selects and serves optimal advertisements based on the user's emotional state and behavioral data.

[0955] A "voice assistant" is an interface that communicates with users via voice and provides necessary information.

[0956] This invention is an advertising system that recognizes a user's emotions in real time and provides optimal information and advertisements according to that emotional state. This system comprehensively analyzes user information, behavioral data, and emotional data, and provides appropriate feedback to the user.

[0957] System Configuration

[0958] The system mainly consists of a user terminal, a server, a database, an emotion engine, and an advertisement serving engine.

[0959] User device roles

[0960] User terminals refer to devices such as smartphones and smart glasses, and perform the following functions:

[0961] 1. Initial registration of user information

[0962] The user accesses the initial registration screen and enters their profile data (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[0963] 2. Collecting behavioral data

[0964] The device periodically collects behavioral data, such as the user's app usage and website browsing history, and appropriately adjusts the timing of sending this data to the server.

[0965] 3. Collecting Emotional Data

[0966] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data, for example, by using libraries such as OpenCV and FaceNet for facial recognition and by converting voice data into text using Google Cloud Speech-to-Text.

[0967] Server Roles

[0968] The server has the following roles:

[0969] 1. Data Analysis

[0970] The user information, behavioral data, and emotional data are received and analyzed comprehensively using big data analysis tools (e.g., Apache Hadoop and TensorFlow). This analysis allows the system to select optimal information and advertisements based on the user's current emotional state and past behavioral patterns.

[0971] 2. Providing information and advertising

[0972] Based on the analysis results, we select information and advertisements appropriate for the user and send them to the user's device. For example, we provide advertisements using an advertisement delivery system (e.g., Magento).

[0973] 3. Processing Feedback

[0974] We collect user feedback and analyze it again, and use this feedback analysis to further optimize our advertising and information provision strategies.

[0975] Specific Examples

[0976] For example, when a user uses a smartphone, the camera captures the user's facial expressions and simultaneously collects their vocal responses. The collected data is sent to a server in real time, where an emotion analysis engine (e.g., IBM Watson Tone Analyzer) identifies the emotion. Big data analysis tools are then used to comprehensively analyze the user's current emotional state and past behavioral data, and the most appropriate advertisements are selected and delivered.

[0977] Prompt Sentence Examples

[0978] The following prompt can be used to generate the code for the sentiment analysis part of the generative AI model:

[0979] Generate Python code that has the following functionality:

[0980] Real-time face detection from webcam using OpenCV.

[0981] Uses Google Cloud Speech-to-Text to convert what the user says into text in real time.

[0982] Identify emotions from text using IBM Watson Tone Analyzer.

[0983] Emotion data is saved to local storage at regular intervals.

[0984] In this way, specific steps for implementing the present invention can be clearly defined, making it possible to provide information and advertisements that are optimal for the user's emotional state in real time.

[0985] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0986] Step 1: Initial registration of user information

[0987] A user accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores the received information in a database. In this step, the input data is the user's profile information, and the output data is the user information stored in the database.

[0988] Step 2: Collect behavioral data

[0989] The device periodically records user behavioral data, such as app usage and website browsing history, in local storage. This data is then sent to the server at regular intervals. The input data in this step is the user behavioral data, and the output data is the behavioral history sent to the server.

[0990] Step 3: Collecting emotion data

[0991] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It performs facial recognition using OpenCV and FaceNet, and converts the voice into text using Google Cloud Speech-to-Text. Based on this data, it uses an emotion analysis engine such as IBM Watson Tone Analyzer to obtain emotion data, which is then sent to the server. The input data in this step are the user's facial expressions and voice information, and the output data is the emotion data sent to the server.

[0992] Step 4: Data synthesis and analysis

[0993] The server receives user information, behavioral data, and emotional data and performs an integrated analysis of them. Big data analysis tools such as Apache Hadoop and TensorFlow are used to profile users' interests, concerns, and emotional states. In this process, the input data are the various types of user data received, and the output data is profile information resulting from the analysis.

[0994] Step 5: Information and advertising selection

[0995] The server selects appropriate information and advertisements based on the results of the integrated analysis. For example, if a user is feeling depressed, it selects entertainment-related advertisements to help them relax. It selects the optimal advertisements using an advertisement delivery system such as Magento. The input data in this step are the analysis results, and the output data is the selected advertisement information.

[0996] Step 6: Information and advertising notices

[0997] The device notifies the user of the information and advertisements received from the server. This can be done, for example, via push notifications or a voice assistant. The notification may also include a message prompting the user for feedback. The input data in this step is the selected advertisement information, and the output data is the information notified to the user.

[0998] Step 7: Gather feedback and reanalyze

[0999] When a user responds to an advertisement or information (by clicking, asking a question, commenting, etc.), that feedback is sent from the device to the server. The server then analyzes this feedback again and uses it to optimize the advertising strategy. The input data in this step is the user feedback, and the output data is the improved advertising strategy as a result of the reanalysis.

[1000] Step 8: Analyzing individual and global data

[1001] The server globally analyzes data collected from multiple users and seeks ways to provide information equally. This reduces the information gap and ensures that useful information reaches all users. In this process, the input data is the data of all users, and the output data is an optimized information provision strategy as a result of the analysis.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] [Third embodiment]

[1006] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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).

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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."

[1018] The present invention relates to a system in which a device such as a smartphone speaks to a user spontaneously and provides necessary information and advice. Specific program processing and its flow are described below for an embodiment of the present invention.

[1019] Basic System Configuration

[1020] 1. Initial registration of user information

[1021] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1022] 2. Collecting behavioral data

[1023] The device periodically records user behavior data (such as app usage and website browsing history), and then periodically transmits this data to a server.

[1024] 3. Data Analysis

[1025] The server runs analytical algorithms based on the received user information and behavioral data to profile the user's interests and concerns.

[1026] 4. Selection and provision of information

[1027] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "There are some recent health articles based on your interests. Please take a look."

[1028] 5. User interaction

[1029] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[1030] 6. Closing the digital divide

[1031] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[1032] Specific examples

[1033] Example of initial registration and information provision

[1034] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[1035] Terminal: Sends the entered information to the server.

[1036] Server: Stores user information in a database and analyzes recent health trends.

[1037] Server: Selects health articles that are useful to the user and sends them to the device.

[1038] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[1039] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[1040] Terminal: Sends user feedback to the server in real time.

[1041] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[1042] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1043] This allows the entire system to work together to provide information to users and further optimize the process based on their feedback.

[1044] The processing flow will be explained below.

[1045] Step 1:

[1046] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[1047] Step 2:

[1048] The terminal receives the entered user information and sends it to the server.

[1049] Step 3:

[1050] The server stores the user information in a database.

[1051] Step 4:

[1052] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[1053] Step 5:

[1054] The device periodically transmits the collected behavioral data to the server.

[1055] Step 6:

[1056] The server then runs the received behavioral data, along with user information, through an analytical algorithm to profile the user's interests and concerns.

[1057] Step 7:

[1058] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[1059] Step 8:

[1060] The server transmits the selected information to the terminal.

[1061] Step 9:

[1062] Based on the information received, the device displays notifications to the user at the appropriate time.

[1063] Step 10:

[1064] The device will then activate the voice assistant and verbally notify the user of the notification, which will be something like, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1065] Step 11:

[1066] Users can send questions or feedback about the suggested information to the device via voice or text.

[1067] Step 12:

[1068] The device sends user feedback to the server in real time.

[1069] Step 13:

[1070] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[1071] Step 14:

[1072] The server transmits the generated response to the terminal.

[1073] Step 15:

[1074] The device will then provide the answer to the user through the voice assistant, such as, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1075] Step 16:

[1076] The server aggregates data from multiple users and analyzes it globally.

[1077] Step 17:

[1078] The server extracts the most useful information and trends and distributes updated data to devices to provide uniform information to all users.

[1079] Example 1

[1080] 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."

[1081] Conventional information provision systems face the challenge of providing optimal information to individual users when collecting and analyzing user information and behavioral data. It is also difficult to effectively utilize user feedback when providing information. Furthermore, there are insufficient means for aggregating data from multiple users and analyzing and providing it from a global perspective. For these reasons, there is a need to improve the quality of information provided to users and continuously optimize it based on feedback.

[1082] 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.

[1083] In this invention, the server includes means for analyzing a user's natural language input using a generative AI model and generating an appropriate response, means for providing the generated response to the user through a voice assistant function, and means for aggregating and globally analyzing data from multiple users to distribute uniform information to all users. This enables optimal information to be provided to individual users, and the quality of information provided can be improved by effectively utilizing user feedback. Furthermore, by integrating and analyzing data from multiple users, uniform information provision from a global perspective can also be achieved.

[1084] "User information" refers to profile data entered by the user, such as age, gender, interests, and purpose.

[1085] A "server" is a central processing unit that stores and analyzes user information and behavioral data and provides appropriate information.

[1086] A "database" is a system managed by a server for efficiently storing, searching, and updating user information and behavioral data.

[1087] "Behavioral data" refers to data such as usage and browsing history when a user uses an app or website.

[1088] "Analysis" is the act of using collected user information and behavioral data to run specific algorithms and profile users' interests and concerns.

[1089] "Feedback" refers to questions, reactions, opinions, etc. that users have in response to the information provided.

[1090] A "generative AI model" is an artificial intelligence model that analyzes a user's natural language input and generates an appropriate response based on it.

[1091] The "voice assistant function" is a function that allows users to interact with the device via voice and provide necessary information.

[1092] "Global analysis" refers to large-scale data analysis that aggregates data from multiple users and extracts and provides the most useful information overall.

[1093] This invention relates to an information provision system in which a device such as a smartphone speaks to the user spontaneously and provides necessary information and advice. This system is mainly composed of three elements: a server, a device, and a user.

[1094] First-time user information registration

[1095] Users launch a dedicated application on their smartphone or other device and access the initial registration screen. They enter their profile information, such as age, gender, interests, and purpose, into the registration form. The device serializes the entered information into JSON format and sends it to the server using the HTTPS protocol.

[1096] Storing information on the server

[1097] The server parses the received JSON data and stores it in a relational database management system (e.g., MySQL). The stored information is stored in a user attribute table (age and gender), and a category table (interests and goals).

[1098] Behavioral data collection

[1099] The device periodically records user behavioral data, such as app usage and website browsing history, using SQLite as the database. The collected data is periodically sent to the server in CSV format.

[1100] Analyzing the data

[1101] The server uses Python libraries (e.g., Pandas, Scikit-learn) to analyze user information and behavioral data, profiling user interests and constructing user profiles using generative AI models (e.g., GPT-4).

[1102] Selection and provision of information

[1103] The server retrieves relevant topics using a news API or similar based on the generated user profile. The selected information is serialized in JSON format and sent to the device. The device then notifies the user through its voice assistant function using the Google Assistant SDK or Amazon Alexa SDK.

[1104] User interaction

[1105] When a user provides feedback or asks a question, the device converts it into a string using the Google Cloud Speech-to-Text API and sends it to the server. The server then uses a generative AI model (GPT-4) to analyze the question and generate an appropriate response. The generated response is then sent to the device and conveyed to the user via the voice assistant.

[1106] Global data analysis and information provision

[1107] The server aggregates data from multiple users and analyzes it globally using big data analysis tools such as Apache Hadoop and Spark. Based on the results of this analysis, information is provided equally to reduce the digital divide.

[1108] As a specific example, if a 30-year-old woman is interested in health and running and is trying to lose weight, the server will provide appropriate information such as, "Recently, protein has been said to be effective for dieting. Please read this article." Also, if the user asks, "Which specific protein is good?", the server will provide a specific answer such as, "The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1109] Prompt Sentence Examples

[1110] Generative AI model: GPT-4

[1111] Prompt: "For a 30-year-old woman interested in health and running who is looking to lose weight, please recommend a protein product. Please include specific brands and reviews."

[1112] The above is an embodiment of the present invention.

[1113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1114] Step 1:

[1115] First-time user information registration

[1116] User: Launches the dedicated smartphone app and accesses the initial registration screen. The user enters profile information such as age, gender, interests, and purpose.

[1117] Terminal: User input information is serialized in JSON format and sent to the server using the HTTPS protocol.

[1118] Server: Parse the received JSON data and save it in a MySQL database to store age and gender in a user attribute table and interests and goals in a category table. This saves user information in a structured format in the database.

[1119] Step 2:

[1120] Behavioral data collection

[1121] Device: Periodically records your app usage and website browsing history. This data includes the amount of time you spend using apps and the URLs of websites you visit.

[1122] Terminal: Collected behavioral data is sent to the server in batches every certain period (for example, every hour). Data is often sent in CSV format.

[1123] Server: Analyzes the received CSV data, stores it in the action log table with a timestamp, and saves it in the database.

[1124] Step 3:

[1125] Analyzing the data

[1126] Server: Analyzes the received user information and behavioral data using Python libraries such as Pandas and Scikit-learn.

[1127] Server: Runs a clustering algorithm to build user profiles, specifically using K-means clustering to group users by their interests.

[1128] Server: Based on the generated user profile, a generative AI model (GPT-4) is used to predict information based on the user's interests.

[1129] Step 4:

[1130] Selection and provision of information

[1131] Server: Calls the news API to get the latest articles on relevant topics based on the user's interests.

[1132] Server: Serializes the selected information into JSON format and sends it to the device.

[1133] Device: Analyzes the received information and notifies the user at the appropriate time using the voice assistant function. For example, using the Google Assistant SDK, it can say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1134] Step 5:

[1135] User interaction

[1136] User: Reads the suggested article and asks questions or gives feedback about the content. For example, "I'm interested in protein, which specific product is good?"

[1137] On the device: Recognizes the user's voice and converts it into text, using the Google Cloud Speech-to-Text API.

[1138] Terminal: Sends the converted text to the server.

[1139] Server: Analyzes the user's question using a generative AI model (GPT-4) and generates an appropriate answer, such as "The recommended protein product is XX brand YY. The reviews for this product are also very positive."

[1140] Device: The generated answer is synthesized into speech and conveyed to the user.

[1141] Step 6:

[1142] Global data analysis and information provision

[1143] Server: Uses big data analysis tools such as Apache Hadoop and Spark to statistically analyze all collected user data.

[1144] Server: Extracts the most useful information from the acquired data and generates a model to provide it to all users equally.

[1145] Server: Use this model to continuously improve the quality of information provision and reduce the digital divide.

[1146] (Application example 1)

[1147] 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."

[1148] Conventional information provision systems lack the functionality to suggest optimal meals based on a user's dietary preferences and health status, making it difficult to link appropriate meal suggestions for individual users with food delivery services. Furthermore, information cannot be updated in real time based on user feedback, making it difficult to provide timely information. The objective of this invention is to solve these problems and provide an information provision system that suggests optimal meals based on a user's health and preferences and provides a smooth service to users in collaboration with food delivery services.

[1149] 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.

[1150] In this invention, the server includes means for suggesting meals and processing food delivery orders, means for inputting user information, means for transmitting the user information to the server, means for storing the user information in a database, means for collecting user behavioral data, means for transmitting the behavioral data to the server, means for analyzing the user information and behavioral data, means for selecting information appropriate for the user based on the analysis results, means for notifying the user of the selected information, means for receiving feedback from the user via the notification, means for transmitting the feedback to the server and re-analyzing it, means for providing the user with additional information based on the analysis results of the feedback, and means for aggregating data from multiple users, analyzing it globally, and distributing uniform information to all users. This enables smooth integration of optimal meal suggestions tailored to the health and preferences of each individual user with food delivery services.

[1151] An "information provision system" is a system designed to provide users with the information and services they need.

[1152] "User Information" refers to individual data about you, such as your age, gender, health status, and dietary preferences.

[1153] A "server" is a computer system that acts as a central control device, managing, storing, and analyzing user information and behavioral data.

[1154] A "database" is a continuous collection of data that systematically stores and manages collected user information and behavioral data.

[1155] "Behavioral data" refers to information about a user's behavior, such as their app usage and food ordering history.

[1156] An "analysis algorithm" is a calculation method used to profile users and select information based on collected data.

[1157] "Notifying" refers to the act of conveying selected information to a user in the form of voice, text, or other means.

[1158] "Feedback" refers to information provided by users in the form of responses, comments, questions, etc.

[1159] "Meal Suggestion" refers to the act of recommending optimal meal plans and menus based on a user's health status and preferences.

[1160] "Food delivery" refers to the process of providing meals selected by users through a delivery service.

[1161] MODE FOR CARRYING OUT THE INVENTION

[1162] System configuration and programs

[1163] The system for implementing this invention includes an information provision system that suggests optimal meals based on the user's health condition and dietary preferences and works in conjunction with a food delivery service. The main components of the system and their functions are as follows:

[1164] 1. Initial registration of user information

[1165] Users access the initial registration screen using a device such as a smartphone and enter information such as their name, age, gender, allergies, dietary preferences, and health status. The device sends the entered information to a server, which then stores it in a database.

[1166] 2. Collecting behavioral data

[1167] The device records behavioral data such as the meals ordered by the user, delivery history, and app usage, and this data is periodically sent to the server.

[1168] 3. Data Analysis

[1169] The server runs analytics algorithms to profile health and preferences based on the received user information and behavioral data, using software such as MySQL and Postgres for database management and Pytorch and TensorFlow for analytics algorithms.

[1170] 4. Selection and provision of information

[1171] Based on the analysis results, the server selects the most suitable meal suggestions and information on food delivery services for the user. The selected information is sent to the device, which notifies the user at the appropriate time. For example, the voice assistant may notify the user, "Today's recommendation is low-carb chicken salad. Would you like to order it?"

[1172] 5. User interaction

[1173] When a user submits feedback on a suggestion, the information is sent to the server in real time and analyzed again. Additional suggestions and detailed information are generated based on the feedback and provided to the user via their device. For example, if a user asks, "Do you have a menu with a slightly larger portion?", the server analyzes and responds with the appropriate menu item.

[1174] 6. Closing the digital divide

[1175] The server aggregates data collected from multiple users and analyzes it globally, making it possible to provide equally useful information to all users, thereby eliminating the information gap.

[1176] Specific examples

[1177] First-time registration and information provision process

[1178] User: On the initial registration screen, enter the following information: "Age: 30," "Gender: Male," "Health condition: On a low-carb diet," "Food preference: Japanese food," and "Allergies: None."

[1179] Terminal: Sends the entered information to the server.

[1180] Server: Stores user information in a database and runs analytical algorithms to generate user profiles.

[1181] Server: Selects the best meal suggestions for the user and sends the information to the device.

[1182] Device: Announces, "We recommend the low-carb chicken salad. Would you like to order it?"

[1183] Examples of user interactions

[1184] User: "Do you have something a little more substantial?"

[1185] Server: Analyzes the feedback and suggests, "How about a chicken steak and salad combo?"

[1186] The generative AI model used is GPT-4 (OpenAI), which generates information appropriate for the user using the following prompt sentences:

[1187] Example prompt:

[1188] User profile information: "30 years old, male, on a low-carb diet, no allergies"

[1189] Sample prompt: "Suggest a low-carb lunch option that would work best for your target audience. Specifically, choose from chicken salad or grilled chicken."

[1190] In this way, the system of the present invention provides optimal meal suggestions and food delivery services based on the user's health and dietary preferences.

[1191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1192] Step 1:

[1193] First time user registration:

[1194] The user accesses the initial registration screen using a smartphone and enters their name, age, gender, health condition, dietary preferences, allergy information, etc. The device formats the entered information in JSON format and sends it to the server using an HTTP POST request. The server stores the received data in a database. The server uses MySQL as its database management system and adds the user information to a table.

[1195] Step 2:

[1196] Behavioral Data Collection:

[1197] The device records user behavioral data, such as app usage and past order history, in a local data store. Using a periodic scheduler (e.g., a cron job), the local data is sent to the server. This data is also formatted in JSON and sent to the server via an HTTP POST request. The server stores the received data in a database.

[1198] Step 3:

[1199] Data analysis:

[1200] The server periodically runs batch processes to analyze user information and behavioral data stored in the database. The server uses Python scripts and TensorFlow to run machine learning models to create user profiles that reflect the user's behavioral patterns and preferences. The analysis results are stored in the database.

[1201] Step 4:

[1202] Selection and provision of information:

[1203] Based on the analysis results, the server selects the most suitable meal suggestion for the user. The generative AI model used is GPT-4 (OpenAI), which generates the prompt text. A sample prompt text includes user profile information, as well as a message such as, "Please suggest the best low-carb lunch menu for the target user. Specifically, please choose from chicken salad, grilled chicken, etc." The generated suggestion text is sent to the device in JSON format.

[1204] Step 5:

[1205] Notice to users:

[1206] The device analyzes the information it receives and notifies the user using a voice assistant or push notification. For example, it might say, "We recommend a low-carb chicken salad. Would you like to order it?" It may also ask for user feedback.

[1207] Step 6:

[1208] Collecting user feedback:

[1209] Users provide feedback through voice or text input, which the device collects in real time and sends via HTTP POST requests to the server, which stores the received feedback in a database.

[1210] Step 7:

[1211] Feedback reanalysis:

[1212] The server analyzes the feedback and runs the machine learning model again to identify additional user needs. Based on the new analysis, it generates additional information and suggestions, which are stored in a database and sent to the device.

[1213] Step 8:

[1214] Bridging the digital divide:

[1215] The server aggregates all user data and performs global analysis to identify the most useful information, which is then distributed equally to all users through periodic reports and notifications to the broad user base.

[1216] Step 9:

[1217] Food delivery ordering process:

[1218] If the user accepts the suggestion, the device will call the food delivery service's API to complete the order, and the order details will be sent to the server and saved as an order history.

[1219] 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.

[1220] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[1221] Basic System Configuration

[1222] 1. Initial registration of user information

[1223] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1224] 2. Collecting behavioral data

[1225] The device periodically records user behavior data (such as app usage and website browsing history) in local storage, and then periodically transmits this data to the server.

[1226] 3. Emotion Recognition by Emotion Engine

[1227] The device collects emotional data from the user's voice and facial expressions, and uses an emotion engine to analyze multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time. The analysis results are sent to a server.

[1228] 4. Data Analysis

[1229] The server runs analytical algorithms to profile the user's interests and concerns based on the received user information, behavioral data, and emotional data.

[1230] 5. Selection and provision of information

[1231] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "I sensed that you've been interested in health recently. Please take a look at this health article."

[1232] 6. User interaction

[1233] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[1234] 7. Closing the digital divide

[1235] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[1236] Specific examples

[1237] Example of initial registration and information provision

[1238] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[1239] Terminal: Sends the entered information to the server.

[1240] Server: Stores user information in a database and analyzes recent health trends.

[1241] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server.

[1242] Server: Selects the most appropriate information based on the user's behavioral and emotional data and sends it to the device.

[1243] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[1244] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[1245] Terminal: Sends user feedback to the server in real time.

[1246] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[1247] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1248] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[1249] The processing flow will be explained below.

[1250] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[1251] Basic System Configuration

[1252] Step 1:

[1253] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[1254] Step 2:

[1255] The terminal transmits the entered user information to the server.

[1256] Step 3:

[1257] The server stores the user information in a database.

[1258] Step 4:

[1259] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[1260] Step 5:

[1261] The device periodically transmits the collected behavioral data to the server.

[1262] Step 6:

[1263] The device uses an emotion engine to analyze the user's voice data and facial expression data captured by the camera, recognizing multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time.

[1264] Step 7:

[1265] The device transmits the emotion data acquired by the emotion engine to the server.

[1266] Step 8:

[1267] The server runs the user information, behavioral data, and emotional data through analytical algorithms to profile the user's interests and concerns.

[1268] Step 9:

[1269] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[1270] Step 10:

[1271] The server transmits the selected information to the terminal.

[1272] Step 11:

[1273] Based on the information received, the device displays notifications to the user at the appropriate time.

[1274] Step 12:

[1275] The device will activate the voice assistant and announce the notification to the user aloud. For example, it might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1276] Step 13:

[1277] Users can send questions or feedback about the suggested information to the device via voice or text.

[1278] Step 14:

[1279] The device sends user feedback to the server in real time.

[1280] Step 15:

[1281] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[1282] Step 16:

[1283] The server transmits the generated response to the terminal.

[1284] Step 17:

[1285] The device will then provide the answer to the user through the voice assistant, for example, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1286] Step 18:

[1287] The server aggregates data from multiple users and analyzes it globally.

[1288] Step 19:

[1289] The server generates updated data based on the global analysis results, distributing the most useful information and trends evenly to all users.

[1290] Step 20:

[1291] The server sends the generated update data to the terminal, and the terminal notifies the user of it.

[1292] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[1293] Example 2

[1294] 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."

[1295] Conventional information provision systems have struggled to accurately collect and analyze user behavioral data and emotional information, and provide optimal information to individual users in real time. Furthermore, methods for effectively utilizing user feedback and emotional data to optimize the system were limited. Furthermore, there was a lack of a mechanism for aggregating data from multiple users to provide equally useful information to all users. This made it challenging to provide truly useful information to users.

[1296] 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.

[1297] In this invention, the server includes a means for storing user information in a database, a means for periodically collecting user behavioral data, and a means for analyzing collected voice and facial expression data using an emotion engine. This enables highly accurate collection and analysis of user behavioral data and emotion data. Furthermore, optimal information can be provided to users in real time based on the analysis results, and feedback from users can be effectively utilized to optimize the system. Furthermore, data from multiple users can be aggregated and analyzed globally, enabling useful information to be provided equally to all users.

[1298] "User Information" refers to a user's profile information, including data such as age, gender, interests, and purpose.

[1299] "Terminal" refers to a device used by a user to input information, and refers to a mobile terminal such as a smartphone or tablet.

[1300] A "server" is a central computer that receives, stores, and analyzes user information and behavioral data, and communicates with multiple terminals over a network.

[1301] A "database" is an information storage system that systematically stores user information, behavioral data, emotional data, etc. on a server.

[1302] "Behavioral data" includes data about a user's operations and behavior, such as their app usage and website browsing history.

[1303] "Emotion data" is information about the emotional state extracted from the user's voice and facial expressions, and includes emotions such as joy, sadness, and anger.

[1304] An "emotion engine" is software or algorithm that analyzes a user's voice and facial expression data to recognize their emotional state.

[1305] An "analysis algorithm" is a calculation method used by the server to analyze user information, behavioral data, and emotional data to profile users' interests and concerns.

[1306] "Notifications" are messages or alerts that convey selected information from the server to the user via the terminal.

[1307] "Feedback" refers to reactions such as opinions and questions that users send in response to the information provided.

[1308] "Global analysis" refers to data analysis that aggregates data from multiple users and analyzes it over a wide area to provide useful information to all users.

[1309] A "voice assistant" is an interface that provides information through voice interaction with the user, and is a system that responds to the user's questions and commands via voice.

[1310] The present invention relates to an information provision system that combines an emotion engine that recognizes the emotions of a user. A specific embodiment of the information provision system will be described below.

[1311] System Configuration

[1312] This system mainly consists of a user's smartphone or other device, a central server, and a database. The operation of each element and how they work together are described in detail below.

[1313] First-time user information registration

[1314] Using a device such as a smartphone, a user accesses the initial registration screen and enters their profile information (age, gender, interests, goals, etc.). This information is sent by the device to the server, which then stores it in a database. For example, if a user enters information such as "Age: 30," "Interests: health, running," and "Goal: dieting," the device sends this information to the server using an HTTP request. The server analyzes the received information and executes an INSERT query in a relational database such as MySQL to store the information.

[1315] Behavioral data collection

[1316] Devices periodically record user behavioral data, such as app usage and website browsing history, in local storage. This data is then periodically sent to a server. Typically, this data is written to an SQLite database on the device and sent as a batch process. The server then analyzes the received behavioral data and stores it in the database.

[1317] Emotion recognition by emotion engine

[1318] The device collects the user's voice and facial expressions in real time and analyzes them using an emotion engine. This analysis uses the Emotion API from Microsoft Azure's Cognitive Services. For example, when a user provides data through their smartphone's microphone or camera, the device analyzes it and sends it to the server as emotional data such as joy, sadness, or anger. The server then stores this data in a database.

[1319] Analyzing the data

[1320] The server runs an analysis algorithm based on the collected user information, behavioral data, and emotional data. Specifically, it uses machine learning models using Python's Pandas and Scikit-Learn. This allows it to profile users' interests and select appropriate information. The analysis results are stored in a database and appropriately tagged.

[1321] Selection and provision of information

[1322] The server selects the most appropriate information and advice for the user based on the analysis results. A recommendation algorithm is used for this selection. The selected information is sent to the device in JSON format as an HTTP response. The device then notifies the user of the information via push notification or a voice assistant (e.g., "Little Honda"). For example, the voice assistant might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1323] User interaction

[1324] When a user asks a question or gives feedback about the information provided by the device, the device sends it to the server. For example, if a user asks, "I'm interested in protein. Which product is specifically recommended?", the device sends this feedback to the server in real time. The server analyzes the question and uses a text analysis algorithm to generate a specific answer, which is then sent to the device. The device might respond, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1325] Eliminating the digital divide

[1326] The server aggregates data from multiple users, analyzes it globally, and provides the most useful information equally to all users. Specifically, it uses big data analysis tools (e.g., Apache Hadoop). This information is also stored in a database and notified equally to each user.

[1327] Prompt Sentence Examples

[1328] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[1329] As described above, the present invention realizes a system in which each element works in cooperation with each other to provide individually optimized information to each user.

[1330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1331] Step 1: Initial registration of user information

[1332] User: Accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.).

[1333] Input: Age, Gender, Interests, Purpose

[1334] Output: A dataset of the input user information

[1335] Terminal: Receives the entered user information and sends it to the server using an HTTP request.

[1336] Data processing: Convert user information into JSON format

[1337] Output: JSON data of user information sent to the server

[1338] Server: Analyzes the received user information and stores it in a database.

[1339] Input: JSON data of user information received by the server

[1340] Data operation: Inserting data into the database

[1341] Output: User information stored in the database

[1342] Step 2: Collect behavioral data

[1343] Device: Periodically records user behavioral data, such as app usage and website browsing history, in local storage.

[1344] Input: User operation history (app usage, website browsing history, etc.)

[1345] Data processing: Convert to a format that can be saved in local storage

[1346] Output: Behavioral data recorded in the device's local storage

[1347] Device: Sends recorded behavioral data to the server at regular intervals.

[1348] Input: Behavioral data recorded in local storage

[1349] Data calculation: Send to server using HTTP request

[1350] Output: Behavioral data sent to the server

[1351] Server: Stores the received behavioral data in a database.

[1352] Input: Behavioral data received by the server

[1353] Data operation: Inserting data into the database

[1354] Output: Behavioral data stored in a database

[1355] Step 3: Emotion recognition by the emotion engine

[1356] Device: Collects the user's voice and facial expressions in real time.

[1357] Input: User's voice data, facial expression data

[1358] Data processing: Collection of voice and facial expression data

[1359] Output: Collected voice data and facial expression data

[1360] Terminal: Uses an emotion engine to analyze the collected data.

[1361] Input: Collected voice data, facial expression data

[1362] Data calculation: Analysis by emotion engine (e.g. Emotion API)

[1363] Output: Emotion data (e.g., joy, sadness, anger, etc.)

[1364] Terminal: Sends the analysis results to the server.

[1365] Input: Emotion data

[1366] Data calculation: Send to server using HTTP request

[1367] Output: Emotion data sent to the server

[1368] Server: Stores the received emotion data in a database.

[1369] Input: Emotion data received by the server

[1370] Data operation: Inserting data into the database

[1371] Output: Emotion data stored in a database

[1372] Step 4: Analyze the data

[1373] Server: Runs analytical algorithms based on user information, behavioral data, and emotional data.

[1374] Input: User information, behavioral data, and emotional data stored in a database

[1375] Data Computation: Applying machine learning models using Python's Pandas and Scikit-Learn

[1376] Output: Analysis results

[1377] Server: Stores the analysis results in a database and tags them appropriately.

[1378] Input: Analysis results

[1379] Data processing: storing and tagging analysis results in a database

[1380] Output: Analysis results stored in a database

[1381] Step 5: Select and provide information

[1382] Server: Selects the most appropriate information and advice for users based on the analysis results.

[1383] Input: Analysis results stored in the database

[1384] Data operations: Applying recommendation algorithms

[1385] Output: Selected information

[1386] Server: Sends the selected information to the device.

[1387] Input: Selected information

[1388] Data calculation: sent as HTTP response in JSON format

[1389] Output: Information sent to the terminal

[1390] Terminal: Notifies the user of the received information.

[1391] Input: Information received from the server

[1392] Data calculation: Push notifications and voice assistant notifications

[1393] Output: Notification to the user (e.g., a message for a voice assistant)

[1394] Step 6: Interact with the user

[1395] Users: Ask questions or give feedback on the information provided.

[1396] Input: Questions or feedback on the proposed information

[1397] Data processing: Feedback description

[1398] Output: Feedback data

[1399] Device: Sends feedback to the server in real time.

[1400] Input: User feedback

[1401] Data calculation: Send to server using HTTP request

[1402] Output: Feedback data sent to the server

[1403] Server: Analyzes the question and generates information that addresses the user's additional needs.

[1404] Input: Feedback data

[1405] Data Computing: Applying Text Analysis Algorithms

[1406] Output: Additional information

[1407] Server: Sends the generated information to the terminal.

[1408] Input: Additional information

[1409] Data calculation: sent in HTTP response

[1410] Output: Additional information sent to the terminal

[1411] Terminal: Notify the user of additional information.

[1412] Input: Additional information received from the server

[1413] Data calculation: Push notifications and voice assistant notifications

[1414] Output: User notification (e.g., "The recommended protein product is XX brand YY.")

[1415] Step 7: Bridging the digital divide

[1416] Server: Aggregates data from multiple users and analyzes it globally.

[1417] Input: Multi-user dataset

[1418] Data calculation: Analysis using big data analysis tools (e.g., Apache Hadoop)

[1419] Output: Globally analyzed results

[1420] Server: Provides the most useful information equally to all users.

[1421] Input: Globally analyzed results

[1422] Data processing: Converting to the appropriate format and distributing information evenly

[1423] Output: Useful information notice to all users

[1424] Prompt Sentence Examples

[1425] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[1426] The above is the specific flow of the program processing of this system.

[1427] (Application example 2)

[1428] 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."

[1429] Conventional information delivery systems have difficulty providing information that takes into account the user's real-time emotions, and are therefore unable to provide optimal advertisements or information that reflect the user's current state or mood. Furthermore, methods for effectively utilizing user feedback and optimizing information delivery have been limited. As a result, there has been a problem in that the user experience has not been sufficiently improved.

[1430] 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.

[1431] In this invention, the server includes a means for analyzing the user's voice or facial expression to collect emotional data, a means for transmitting the emotional data to the server, and a means for analyzing the user information, behavioral data, and emotional data. This makes it possible to recognize the user's emotions in real time and provide optimal information and advertisements according to the user's emotional state. Furthermore, it is possible to quickly incorporate feedback from the user and improve the accuracy of the information provided.

[1432] An "information provision system" is a system that collects user information and provides appropriate data and content to users based on that information.

[1433] "User Information" refers to the profile data entered by a user when they first register, and includes information such as age, gender, interests, and purpose.

[1434] "Server" refers to a central processing unit that receives, stores, and analyzes user information, behavioral data, and emotional data, and provides appropriate information to users based on the analysis results.

[1435] A "database" is an information storage system for systematically accumulating and managing user information, behavioral data, emotional data, etc.

[1436] "User behavioral data" refers to data about a user's daily behavior, such as app usage and website browsing history.

[1437] "Emotional data" refers to data about the user's emotional state, such as joy, sadness, surprise, or anger, obtained by analyzing the user's voice and facial expressions.

[1438] An "emotion engine" is an analytical engine that analyzes input data such as voice and facial expressions to specifically identify the user's emotions.

[1439] An "analysis algorithm" is a mathematical processing method for profiling users' interests and concerns by comprehensively analyzing user information, behavioral data, and emotional data.

[1440] "Feedback" refers to the opinions and reactions that users give to the information they provide, and is important data that allows the system to provide even more optimal information based on this.

[1441] "Notifications" are messaging methods used to communicate selected information or advertisements to users.

[1442] An "ad serving app" is software that selects and serves optimal advertisements based on the user's emotional state and behavioral data.

[1443] A "voice assistant" is an interface that communicates with users via voice and provides necessary information.

[1444] This invention is an advertising system that recognizes a user's emotions in real time and provides optimal information and advertisements according to that emotional state. This system comprehensively analyzes user information, behavioral data, and emotional data, and provides appropriate feedback to the user.

[1445] System Configuration

[1446] The system mainly consists of a user terminal, a server, a database, an emotion engine, and an advertisement serving engine.

[1447] User device roles

[1448] User terminals refer to devices such as smartphones and smart glasses, and perform the following functions:

[1449] 1. Initial registration of user information

[1450] The user accesses the initial registration screen and enters their profile data (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1451] 2. Collecting behavioral data

[1452] The device periodically collects behavioral data, such as the user's app usage and website browsing history, and appropriately adjusts the timing of sending this data to the server.

[1453] 3. Collecting Emotional Data

[1454] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data, for example, by using libraries such as OpenCV and FaceNet for facial recognition and by converting voice data into text using Google Cloud Speech-to-Text.

[1455] Server Roles

[1456] The server has the following roles:

[1457] 1. Data Analysis

[1458] The user information, behavioral data, and emotional data are received and analyzed comprehensively using big data analysis tools (e.g., Apache Hadoop and TensorFlow). This analysis allows the system to select optimal information and advertisements based on the user's current emotional state and past behavioral patterns.

[1459] 2. Providing information and advertising

[1460] Based on the analysis results, we select information and advertisements appropriate for the user and send them to the user's device. For example, we provide advertisements using an advertisement delivery system (e.g., Magento).

[1461] 3. Processing Feedback

[1462] We collect user feedback and analyze it again, and use this feedback analysis to further optimize our advertising and information provision strategies.

[1463] Specific Examples

[1464] For example, when a user uses a smartphone, the camera captures the user's facial expressions and simultaneously collects their vocal responses. The collected data is sent to a server in real time, where an emotion analysis engine (e.g., IBM Watson Tone Analyzer) identifies the emotion. Big data analysis tools are then used to comprehensively analyze the user's current emotional state and past behavioral data, and the most appropriate advertisements are selected and delivered.

[1465] Prompt Sentence Examples

[1466] The following prompt can be used to generate the code for the sentiment analysis part of the generative AI model:

[1467] Generate Python code that has the following functionality:

[1468] Real-time face detection from webcam using OpenCV.

[1469] Uses Google Cloud Speech-to-Text to convert what the user says into text in real time.

[1470] Identify emotions from text using IBM Watson Tone Analyzer.

[1471] Emotion data is saved to local storage at regular intervals.

[1472] In this way, specific steps for implementing the present invention can be clearly defined, making it possible to provide information and advertisements that are optimal for the user's emotional state in real time.

[1473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1474] Step 1: Initial registration of user information

[1475] A user accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores the received information in a database. In this step, the input data is the user's profile information, and the output data is the user information stored in the database.

[1476] Step 2: Collect behavioral data

[1477] The device periodically records user behavioral data, such as app usage and website browsing history, in local storage. This data is then sent to the server at regular intervals. The input data in this step is the user behavioral data, and the output data is the behavioral history sent to the server.

[1478] Step 3: Collecting emotion data

[1479] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It performs facial recognition using OpenCV and FaceNet, and converts the voice into text using Google Cloud Speech-to-Text. Based on this data, it uses an emotion analysis engine such as IBM Watson Tone Analyzer to obtain emotion data, which is then sent to the server. The input data in this step are the user's facial expressions and voice information, and the output data is the emotion data sent to the server.

[1480] Step 4: Data synthesis and analysis

[1481] The server receives user information, behavioral data, and emotional data and performs an integrated analysis of them. Big data analysis tools such as Apache Hadoop and TensorFlow are used to profile users' interests, concerns, and emotional states. In this process, the input data are the various types of user data received, and the output data is profile information resulting from the analysis.

[1482] Step 5: Information and advertising selection

[1483] The server selects appropriate information and advertisements based on the results of the integrated analysis. For example, if a user is feeling depressed, it selects entertainment-related advertisements to help them relax. It selects the optimal advertisements using an advertisement delivery system such as Magento. The input data in this step are the analysis results, and the output data is the selected advertisement information.

[1484] Step 6: Information and advertising notices

[1485] The device notifies the user of the information and advertisements received from the server. This can be done, for example, via push notifications or a voice assistant. The notification may also include a message prompting the user for feedback. The input data in this step is the selected advertisement information, and the output data is the information notified to the user.

[1486] Step 7: Gather feedback and reanalyze

[1487] When a user responds to an advertisement or information (by clicking, asking a question, commenting, etc.), that feedback is sent from the device to the server. The server then analyzes this feedback again and uses it to optimize the advertising strategy. The input data in this step is the user feedback, and the output data is the improved advertising strategy as a result of the reanalysis.

[1488] Step 8: Analyzing individual and global data

[1489] The server globally analyzes data collected from multiple users and seeks ways to provide information equally. This reduces the information gap and ensures that useful information reaches all users. In this process, the input data is the data of all users, and the output data is an optimized information provision strategy as a result of the analysis.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] [Fourth embodiment]

[1494] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1495] 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.

[1496] 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).

[1497] 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.

[1498] 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.

[1499] 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).

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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."

[1507] The present invention relates to a system in which a device such as a smartphone speaks to a user spontaneously and provides necessary information and advice. Specific program processing and its flow are described below for an embodiment of the present invention.

[1508] Basic System Configuration

[1509] 1. Initial registration of user information

[1510] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1511] 2. Collecting behavioral data

[1512] The device periodically records user behavior data (such as app usage and website browsing history), and then periodically transmits this data to a server.

[1513] 3. Data Analysis

[1514] The server runs analytical algorithms based on the received user information and behavioral data to profile the user's interests and concerns.

[1515] 4. Selection and provision of information

[1516] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "There are some recent health articles based on your interests. Please take a look."

[1517] 5. User interaction

[1518] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[1519] 6. Closing the digital divide

[1520] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[1521] Specific examples

[1522] Example of initial registration and information provision

[1523] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[1524] Terminal: Sends the entered information to the server.

[1525] Server: Stores user information in a database and analyzes recent health trends.

[1526] Server: Selects health articles that are useful to the user and sends them to the device.

[1527] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[1528] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[1529] Terminal: Sends user feedback to the server in real time.

[1530] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[1531] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1532] This allows the entire system to work together to provide information to users and further optimize the process based on their feedback.

[1533] The processing flow will be explained below.

[1534] Step 1:

[1535] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[1536] Step 2:

[1537] The terminal receives the entered user information and sends it to the server.

[1538] Step 3:

[1539] The server stores the user information in a database.

[1540] Step 4:

[1541] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[1542] Step 5:

[1543] The device periodically transmits the collected behavioral data to the server.

[1544] Step 6:

[1545] The server then runs the received behavioral data, along with user information, through an analytical algorithm to profile the user's interests and concerns.

[1546] Step 7:

[1547] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[1548] Step 8:

[1549] The server transmits the selected information to the terminal.

[1550] Step 9:

[1551] Based on the information received, the device displays notifications to the user at the appropriate time.

[1552] Step 10:

[1553] The device will then activate the voice assistant and verbally notify the user of the notification, which will be something like, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1554] Step 11:

[1555] Users can send questions or feedback about the suggested information to the device via voice or text.

[1556] Step 12:

[1557] The device sends user feedback to the server in real time.

[1558] Step 13:

[1559] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[1560] Step 14:

[1561] The server transmits the generated response to the terminal.

[1562] Step 15:

[1563] The device will then provide the answer to the user through the voice assistant, such as, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1564] Step 16:

[1565] The server aggregates data from multiple users and analyzes it globally.

[1566] Step 17:

[1567] The server extracts the most useful information and trends and distributes updated data to devices to provide uniform information to all users.

[1568] Example 1

[1569] 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."

[1570] Conventional information provision systems face the challenge of providing optimal information to individual users when collecting and analyzing user information and behavioral data. It is also difficult to effectively utilize user feedback when providing information. Furthermore, there are insufficient means for aggregating data from multiple users and analyzing and providing it from a global perspective. For these reasons, there is a need to improve the quality of information provided to users and continuously optimize it based on feedback.

[1571] 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.

[1572] In this invention, the server includes means for analyzing a user's natural language input using a generative AI model and generating an appropriate response, means for providing the generated response to the user through a voice assistant function, and means for aggregating and globally analyzing data from multiple users to distribute uniform information to all users. This enables optimal information to be provided to individual users, and the quality of information provided can be improved by effectively utilizing user feedback. Furthermore, by integrating and analyzing data from multiple users, uniform information provision from a global perspective can also be achieved.

[1573] "User information" refers to profile data entered by the user, such as age, gender, interests, and purpose.

[1574] A "server" is a central processing unit that stores and analyzes user information and behavioral data and provides appropriate information.

[1575] A "database" is a system managed by a server for efficiently storing, searching, and updating user information and behavioral data.

[1576] "Behavioral data" refers to data such as usage and browsing history when a user uses an app or website.

[1577] "Analysis" is the act of using collected user information and behavioral data to run specific algorithms and profile users' interests and concerns.

[1578] "Feedback" refers to questions, reactions, opinions, etc. that users have in response to the information provided.

[1579] A "generative AI model" is an artificial intelligence model that analyzes a user's natural language input and generates an appropriate response based on it.

[1580] The "voice assistant function" is a function that allows users to interact with the device via voice and provide necessary information.

[1581] "Global analysis" refers to large-scale data analysis that aggregates data from multiple users and extracts and provides the most useful information overall.

[1582] This invention relates to an information provision system in which a device such as a smartphone speaks to the user spontaneously and provides necessary information and advice. This system is mainly composed of three elements: a server, a device, and a user.

[1583] First-time user information registration

[1584] Users launch a dedicated application on their smartphone or other device and access the initial registration screen. They enter their profile information, such as age, gender, interests, and purpose, into the registration form. The device serializes the entered information into JSON format and sends it to the server using the HTTPS protocol.

[1585] Storing information on the server

[1586] The server parses the received JSON data and stores it in a relational database management system (e.g., MySQL). The stored information is stored in a user attribute table (age and gender), and a category table (interests and goals).

[1587] Behavioral data collection

[1588] The device periodically records user behavioral data, such as app usage and website browsing history, using SQLite as the database. The collected data is periodically sent to the server in CSV format.

[1589] Analyzing the data

[1590] The server uses Python libraries (e.g., Pandas, Scikit-learn) to analyze user information and behavioral data, profiling user interests and constructing user profiles using generative AI models (e.g., GPT-4).

[1591] Selection and provision of information

[1592] The server retrieves relevant topics using a news API or similar based on the generated user profile. The selected information is serialized in JSON format and sent to the device. The device then notifies the user through its voice assistant function using the Google Assistant SDK or Amazon Alexa SDK.

[1593] User interaction

[1594] When a user provides feedback or asks a question, the device converts it into a string using the Google Cloud Speech-to-Text API and sends it to the server. The server then uses a generative AI model (GPT-4) to analyze the question and generate an appropriate response. The generated response is then sent to the device and conveyed to the user via the voice assistant.

[1595] Global data analysis and information provision

[1596] The server aggregates data from multiple users and analyzes it globally using big data analysis tools such as Apache Hadoop and Spark. Based on the results of this analysis, information is provided equally to reduce the digital divide.

[1597] As a specific example, if a 30-year-old woman is interested in health and running and is trying to lose weight, the server will provide appropriate information such as, "Recently, protein has been said to be effective for dieting. Please read this article." Also, if the user asks, "Which specific protein is good?", the server will provide a specific answer such as, "The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1598] Prompt Sentence Examples

[1599] Generative AI model: GPT-4

[1600] Prompt: "For a 30-year-old woman interested in health and running who is looking to lose weight, please recommend a protein product. Please include specific brands and reviews."

[1601] The above is an embodiment of the present invention.

[1602] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1603] Step 1:

[1604] First-time user information registration

[1605] User: Launches the dedicated smartphone app and accesses the initial registration screen. The user enters profile information such as age, gender, interests, and purpose.

[1606] Terminal: User input information is serialized in JSON format and sent to the server using the HTTPS protocol.

[1607] Server: Parse the received JSON data and save it in a MySQL database to store age and gender in a user attribute table and interests and goals in a category table. This saves user information in a structured format in the database.

[1608] Step 2:

[1609] Behavioral data collection

[1610] Device: Periodically records your app usage and website browsing history. This data includes the amount of time you spend using apps and the URLs of websites you visit.

[1611] Terminal: Collected behavioral data is sent to the server in batches every certain period (for example, every hour). Data is often sent in CSV format.

[1612] Server: Analyzes the received CSV data, stores it in the action log table with a timestamp, and saves it in the database.

[1613] Step 3:

[1614] Analyzing the data

[1615] Server: Analyzes the received user information and behavioral data using Python libraries such as Pandas and Scikit-learn.

[1616] Server: Runs a clustering algorithm to build user profiles, specifically using K-means clustering to group users by their interests.

[1617] Server: Based on the generated user profile, a generative AI model (GPT-4) is used to predict information based on the user's interests.

[1618] Step 4:

[1619] Selection and provision of information

[1620] Server: Calls the news API to get the latest articles on relevant topics based on the user's interests.

[1621] Server: Serializes the selected information into JSON format and sends it to the device.

[1622] Device: Analyzes the received information and notifies the user at the appropriate time using the voice assistant function. For example, using the Google Assistant SDK, it can say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1623] Step 5:

[1624] User interaction

[1625] User: Reads the suggested article and asks questions or gives feedback about the content. For example, "I'm interested in protein, which specific product is good?"

[1626] On the device: Recognizes the user's voice and converts it into text, using the Google Cloud Speech-to-Text API.

[1627] Terminal: Sends the converted text to the server.

[1628] Server: Analyzes the user's question using a generative AI model (GPT-4) and generates an appropriate answer, such as "The recommended protein product is XX brand YY. The reviews for this product are also very positive."

[1629] Device: The generated answer is synthesized into speech and conveyed to the user.

[1630] Step 6:

[1631] Global data analysis and information provision

[1632] Server: Uses big data analysis tools such as Apache Hadoop and Spark to statistically analyze all collected user data.

[1633] Server: Extracts the most useful information from the acquired data and generates a model to provide it to all users equally.

[1634] Server: Use this model to continuously improve the quality of information provision and reduce the digital divide.

[1635] (Application example 1)

[1636] 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."

[1637] Conventional information provision systems lack the functionality to suggest optimal meals based on a user's dietary preferences and health status, making it difficult to link appropriate meal suggestions for individual users with food delivery services. Furthermore, information cannot be updated in real time based on user feedback, making it difficult to provide timely information. The objective of this invention is to solve these problems and provide an information provision system that suggests optimal meals based on a user's health and preferences and provides a smooth service to users in collaboration with food delivery services.

[1638] 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.

[1639] In this invention, the server includes means for suggesting meals and processing food delivery orders, means for inputting user information, means for transmitting the user information to the server, means for storing the user information in a database, means for collecting user behavioral data, means for transmitting the behavioral data to the server, means for analyzing the user information and behavioral data, means for selecting information appropriate for the user based on the analysis results, means for notifying the user of the selected information, means for receiving feedback from the user via the notification, means for transmitting the feedback to the server and re-analyzing it, means for providing the user with additional information based on the analysis results of the feedback, and means for aggregating data from multiple users, analyzing it globally, and distributing uniform information to all users. This enables smooth integration of optimal meal suggestions tailored to the health and preferences of each individual user with food delivery services.

[1640] An "information provision system" is a system designed to provide users with the information and services they need.

[1641] "User Information" refers to individual data about you, such as your age, gender, health status, and dietary preferences.

[1642] A "server" is a computer system that acts as a central control device, managing, storing, and analyzing user information and behavioral data.

[1643] A "database" is a continuous collection of data that systematically stores and manages collected user information and behavioral data.

[1644] "Behavioral data" refers to information about a user's behavior, such as their app usage and food ordering history.

[1645] An "analysis algorithm" is a calculation method used to profile users and select information based on collected data.

[1646] "Notifying" refers to the act of conveying selected information to a user in the form of voice, text, or other means.

[1647] "Feedback" refers to information provided by users in the form of responses, comments, questions, etc.

[1648] "Meal Suggestion" refers to the act of recommending optimal meal plans and menus based on a user's health status and preferences.

[1649] "Food delivery" refers to the process of providing meals selected by users through a delivery service.

[1650] MODE FOR CARRYING OUT THE INVENTION

[1651] System configuration and programs

[1652] The system for implementing this invention includes an information provision system that suggests optimal meals based on the user's health condition and dietary preferences and works in conjunction with a food delivery service. The main components of the system and their functions are as follows:

[1653] 1. Initial registration of user information

[1654] Users access the initial registration screen using a device such as a smartphone and enter information such as their name, age, gender, allergies, dietary preferences, and health status. The device sends the entered information to a server, which then stores it in a database.

[1655] 2. Collecting behavioral data

[1656] The device records behavioral data such as the meals ordered by the user, delivery history, and app usage, and this data is periodically sent to the server.

[1657] 3. Data Analysis

[1658] The server runs analytics algorithms to profile health and preferences based on the received user information and behavioral data, using software such as MySQL and Postgres for database management and Pytorch and TensorFlow for analytics algorithms.

[1659] 4. Selection and provision of information

[1660] Based on the analysis results, the server selects the most suitable meal suggestions and information on food delivery services for the user. The selected information is sent to the device, which notifies the user at the appropriate time. For example, the voice assistant may notify the user, "Today's recommendation is low-carb chicken salad. Would you like to order it?"

[1661] 5. User interaction

[1662] When a user submits feedback on a suggestion, the information is sent to the server in real time and analyzed again. Additional suggestions and detailed information are generated based on the feedback and provided to the user via their device. For example, if a user asks, "Do you have a menu with a slightly larger portion?", the server analyzes and responds with the appropriate menu item.

[1663] 6. Closing the digital divide

[1664] The server aggregates data collected from multiple users and analyzes it globally, making it possible to provide equally useful information to all users, thereby eliminating the information gap.

[1665] Specific examples

[1666] First-time registration and information provision process

[1667] User: On the initial registration screen, enter the following information: "Age: 30," "Gender: Male," "Health condition: On a low-carb diet," "Food preference: Japanese food," and "Allergies: None."

[1668] Terminal: Sends the entered information to the server.

[1669] Server: Stores user information in a database and runs analytical algorithms to generate user profiles.

[1670] Server: Selects the best meal suggestions for the user and sends the information to the device.

[1671] Device: Announces, "We recommend the low-carb chicken salad. Would you like to order it?"

[1672] Examples of user interactions

[1673] User: "Do you have something a little more substantial?"

[1674] Server: Analyzes the feedback and suggests, "How about a chicken steak and salad combo?"

[1675] The generative AI model used is GPT-4 (OpenAI), which generates information appropriate for the user using the following prompt sentences:

[1676] Example prompt:

[1677] User profile information: "30 years old, male, on a low-carb diet, no allergies"

[1678] Sample prompt: "Suggest a low-carb lunch option that would work best for your target audience. Specifically, choose from chicken salad or grilled chicken."

[1679] In this way, the system of the present invention provides optimal meal suggestions and food delivery services based on the user's health and dietary preferences.

[1680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1681] Step 1:

[1682] First time user registration:

[1683] The user accesses the initial registration screen using a smartphone and enters their name, age, gender, health condition, dietary preferences, allergy information, etc. The device formats the entered information in JSON format and sends it to the server using an HTTP POST request. The server stores the received data in a database. The server uses MySQL as its database management system and adds the user information to a table.

[1684] Step 2:

[1685] Behavioral Data Collection:

[1686] The device records user behavioral data, such as app usage and past order history, in a local data store. Using a periodic scheduler (e.g., a cron job), the local data is sent to the server. This data is also formatted in JSON and sent to the server via an HTTP POST request. The server stores the received data in a database.

[1687] Step 3:

[1688] Data analysis:

[1689] The server periodically runs batch processes to analyze user information and behavioral data stored in the database. The server uses Python scripts and TensorFlow to run machine learning models to create user profiles that reflect the user's behavioral patterns and preferences. The analysis results are stored in the database.

[1690] Step 4:

[1691] Selection and provision of information:

[1692] Based on the analysis results, the server selects the most suitable meal suggestion for the user. The generative AI model used is GPT-4 (OpenAI), which generates the prompt text. A sample prompt text includes user profile information, as well as a message such as, "Please suggest the best low-carb lunch menu for the target user. Specifically, please choose from chicken salad, grilled chicken, etc." The generated suggestion text is sent to the device in JSON format.

[1693] Step 5:

[1694] Notice to users:

[1695] The device analyzes the information it receives and notifies the user using a voice assistant or push notification. For example, it might say, "We recommend a low-carb chicken salad. Would you like to order it?" It may also ask for user feedback.

[1696] Step 6:

[1697] Collecting user feedback:

[1698] Users provide feedback through voice or text input, which the device collects in real time and sends via HTTP POST requests to the server, which stores the received feedback in a database.

[1699] Step 7:

[1700] Feedback reanalysis:

[1701] The server analyzes the feedback and runs the machine learning model again to identify additional user needs. Based on the new analysis, it generates additional information and suggestions, which are stored in a database and sent to the device.

[1702] Step 8:

[1703] Bridging the digital divide:

[1704] The server aggregates all user data and performs global analysis to identify the most useful information, which is then distributed equally to all users through periodic reports and notifications to the broad user base.

[1705] Step 9:

[1706] Food delivery ordering process:

[1707] If the user accepts the suggestion, the device will call the food delivery service's API to complete the order, and the order details will be sent to the server and saved as an order history.

[1708] 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.

[1709] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[1710] Basic System Configuration

[1711] 1. Initial registration of user information

[1712] Users access the initial registration screen using a device such as a smartphone and enter their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1713] 2. Collecting behavioral data

[1714] The device periodically records user behavior data (such as app usage and website browsing history) in local storage, and then periodically transmits this data to the server.

[1715] 3. Emotion Recognition by Emotion Engine

[1716] The device collects emotional data from the user's voice and facial expressions, and uses an emotion engine to analyze multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time. The analysis results are sent to a server.

[1717] 4. Data Analysis

[1718] The server runs analytical algorithms to profile the user's interests and concerns based on the received user information, behavioral data, and emotional data.

[1719] 5. Selection and provision of information

[1720] Based on the analysis results, the server selects the most appropriate information and advice for the user. The selected information is sent to the device, which then notifies the user at the appropriate time. For example, the voice assistant might say, "I sensed that you've been interested in health recently. Please take a look at this health article."

[1721] 6. User interaction

[1722] When a user asks questions or provides feedback on suggestions from their device, the information is sent to the server in real time. The server then analyzes this feedback and generates information that meets the user's additional needs. For example, if a user asks, "Which specific protein is best?", the server analyzes the detailed product information and responds to the user via their device.

[1723] 7. Closing the digital divide

[1724] The server aggregates data from multiple users and analyzes it globally, allowing it to provide the most useful information equally to all users, thereby reducing the information gap.

[1725] Specific examples

[1726] Example of initial registration and information provision

[1727] User: On the initial registration screen, enter the following information: "Age: 30," "Interests: Health, Running," and "Goal: Diet."

[1728] Terminal: Sends the entered information to the server.

[1729] Server: Stores user information in a database and analyzes recent health trends.

[1730] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server.

[1731] Server: Selects the most appropriate information based on the user's behavioral and emotional data and sends it to the device.

[1732] Device: Activate the voice assistant and have it say, "This is Little Honda. Recently, it's been said that protein is effective for dieting. Please read this article."

[1733] User: Read the article and provide feedback. "I'm interested in protein, which one specifically is good?"

[1734] Terminal: Sends user feedback to the server in real time.

[1735] Server: Analyzes the query, generates detailed information about recommended protein products, and sends it to the device.

[1736] Device: Reply "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1737] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[1738] The processing flow will be explained below.

[1739] The present invention relates to an information providing system that incorporates an emotion engine that recognizes user emotions. Specific program processing and its flow will be described below for an embodiment of the present invention.

[1740] Basic System Configuration

[1741] Step 1:

[1742] The user enters profile information (age, gender, interests, purpose, etc.) on the initial registration screen.

[1743] Step 2:

[1744] The terminal transmits the entered user information to the server.

[1745] Step 3:

[1746] The server stores the user information in a database.

[1747] Step 4:

[1748] The device periodically records user behavior data (such as app usage and website browsing history) in local storage.

[1749] Step 5:

[1750] The device periodically transmits the collected behavioral data to the server.

[1751] Step 6:

[1752] The device uses an emotion engine to analyze the user's voice data and facial expression data captured by the camera, recognizing multiple emotions (e.g., joy, sadness, anger, surprise, etc.) in real time.

[1753] Step 7:

[1754] The device transmits the emotion data acquired by the emotion engine to the server.

[1755] Step 8:

[1756] The server runs the user information, behavioral data, and emotional data through analytical algorithms to profile the user's interests and concerns.

[1757] Step 9:

[1758] Based on the results of the analysis, the server selects the most appropriate information and advice for the user.

[1759] Step 10:

[1760] The server transmits the selected information to the terminal.

[1761] Step 11:

[1762] Based on the information received, the device displays notifications to the user at the appropriate time.

[1763] Step 12:

[1764] The device will activate the voice assistant and announce the notification to the user aloud. For example, it might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1765] Step 13:

[1766] Users can send questions or feedback about the suggested information to the device via voice or text.

[1767] Step 14:

[1768] The device sends user feedback to the server in real time.

[1769] Step 15:

[1770] The server analyzes the feedback and generates appropriate answers to the user's additional needs and questions.

[1771] Step 16:

[1772] The server transmits the generated response to the terminal.

[1773] Step 17:

[1774] The device will then provide the answer to the user through the voice assistant, for example, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1775] Step 18:

[1776] The server aggregates data from multiple users and analyzes it globally.

[1777] Step 19:

[1778] The server generates updated data based on the global analysis results, distributing the most useful information and trends evenly to all users.

[1779] Step 20:

[1780] The server sends the generated update data to the terminal, and the terminal notifies the user of it.

[1781] This allows the entire system to work together to provide information to the user and further optimize the process based on feedback and sentiment data.

[1782] Example 2

[1783] 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."

[1784] Conventional information provision systems have struggled to accurately collect and analyze user behavioral data and emotional information, and provide optimal information to individual users in real time. Furthermore, methods for effectively utilizing user feedback and emotional data to optimize the system were limited. Furthermore, there was a lack of a mechanism for aggregating data from multiple users to provide equally useful information to all users. This made it challenging to provide truly useful information to users.

[1785] 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.

[1786] In this invention, the server includes a means for storing user information in a database, a means for periodically collecting user behavioral data, and a means for analyzing collected voice and facial expression data using an emotion engine. This enables highly accurate collection and analysis of user behavioral data and emotion data. Furthermore, optimal information can be provided to users in real time based on the analysis results, and feedback from users can be effectively utilized to optimize the system. Furthermore, data from multiple users can be aggregated and analyzed globally, enabling useful information to be provided equally to all users.

[1787] "User Information" refers to a user's profile information, including data such as age, gender, interests, and purpose.

[1788] "Terminal" refers to a device used by a user to input information, and refers to a mobile terminal such as a smartphone or tablet.

[1789] A "server" is a central computer that receives, stores, and analyzes user information and behavioral data, and communicates with multiple terminals over a network.

[1790] A "database" is an information storage system that systematically stores user information, behavioral data, emotional data, etc. on a server.

[1791] "Behavioral data" includes data about a user's operations and behavior, such as their app usage and website browsing history.

[1792] "Emotion data" is information about the emotional state extracted from the user's voice and facial expressions, and includes emotions such as joy, sadness, and anger.

[1793] An "emotion engine" is software or algorithm that analyzes a user's voice and facial expression data to recognize their emotional state.

[1794] An "analysis algorithm" is a calculation method used by the server to analyze user information, behavioral data, and emotional data to profile users' interests and concerns.

[1795] "Notifications" are messages or alerts that convey selected information from the server to the user via the terminal.

[1796] "Feedback" refers to reactions such as opinions and questions that users send in response to the information provided.

[1797] "Global analysis" refers to data analysis that aggregates data from multiple users and analyzes it over a wide area to provide useful information to all users.

[1798] A "voice assistant" is an interface that provides information through voice interaction with the user, and is a system that responds to the user's questions and commands via voice.

[1799] The present invention relates to an information provision system that combines an emotion engine that recognizes the emotions of a user. A specific embodiment of the information provision system will be described below.

[1800] System Configuration

[1801] This system mainly consists of a user's smartphone or other device, a central server, and a database. The operation of each element and how they work together are described in detail below.

[1802] First-time user information registration

[1803] Using a device such as a smartphone, a user accesses the initial registration screen and enters their profile information (age, gender, interests, goals, etc.). This information is sent by the device to the server, which then stores it in a database. For example, if a user enters information such as "Age: 30," "Interests: health, running," and "Goal: dieting," the device sends this information to the server using an HTTP request. The server analyzes the received information and executes an INSERT query in a relational database such as MySQL to store the information.

[1804] Behavioral data collection

[1805] Devices periodically record user behavioral data, such as app usage and website browsing history, in local storage. This data is then periodically sent to a server. Typically, this data is written to an SQLite database on the device and sent as a batch process. The server then analyzes the received behavioral data and stores it in the database.

[1806] Emotion recognition by emotion engine

[1807] The device collects the user's voice and facial expressions in real time and analyzes them using an emotion engine. This analysis uses the Emotion API from Microsoft Azure's Cognitive Services. For example, when a user provides data through their smartphone's microphone or camera, the device analyzes it and sends it to the server as emotional data such as joy, sadness, or anger. The server then stores this data in a database.

[1808] Analyzing the data

[1809] The server runs an analysis algorithm based on the collected user information, behavioral data, and emotional data. Specifically, it uses machine learning models using Python's Pandas and Scikit-Learn. This allows it to profile users' interests and select appropriate information. The analysis results are stored in a database and appropriately tagged.

[1810] Selection and provision of information

[1811] The server selects the most appropriate information and advice for the user based on the analysis results. A recommendation algorithm is used for this selection. The selected information is sent to the device in JSON format as an HTTP response. The device then notifies the user of the information via push notification or a voice assistant (e.g., "Little Honda"). For example, the voice assistant might say, "This is Little Honda. Recently, people have been saying that protein is effective for dieting. Please read this article."

[1812] User interaction

[1813] When a user asks a question or gives feedback about the information provided by the device, the device sends it to the server. For example, if a user asks, "I'm interested in protein. Which product is specifically recommended?", the device sends this feedback to the server in real time. The server analyzes the question and uses a text analysis algorithm to generate a specific answer, which is then sent to the device. The device might respond, "This is Little Honda. The protein product I recommend is YY from XX brand. The reviews for this product are also very positive."

[1814] Eliminating the digital divide

[1815] The server aggregates data from multiple users, analyzes it globally, and provides the most useful information equally to all users. Specifically, it uses big data analysis tools (e.g., Apache Hadoop). This information is also stored in a database and notified equally to each user.

[1816] Prompt Sentence Examples

[1817] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[1818] As described above, the present invention realizes a system in which each element works in cooperation with each other to provide individually optimized information to each user.

[1819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1820] Step 1: Initial registration of user information

[1821] User: Accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.).

[1822] Input: Age, Gender, Interests, Purpose

[1823] Output: A dataset of the input user information

[1824] Terminal: Receives the entered user information and sends it to the server using an HTTP request.

[1825] Data processing: Convert user information into JSON format

[1826] Output: JSON data of user information sent to the server

[1827] Server: Analyzes the received user information and stores it in a database.

[1828] Input: JSON data of user information received by the server

[1829] Data operation: Inserting data into the database

[1830] Output: User information stored in the database

[1831] Step 2: Collect behavioral data

[1832] Device: Periodically records user behavioral data, such as app usage and website browsing history, in local storage.

[1833] Input: User operation history (app usage, website browsing history, etc.)

[1834] Data processing: Convert to a format that can be saved in local storage

[1835] Output: Behavioral data recorded in the device's local storage

[1836] Device: Sends recorded behavioral data to the server at regular intervals.

[1837] Input: Behavioral data recorded in local storage

[1838] Data calculation: Send to server using HTTP request

[1839] Output: Behavioral data sent to the server

[1840] Server: Stores the received behavioral data in a database.

[1841] Input: Behavioral data received by the server

[1842] Data operation: Inserting data into the database

[1843] Output: Behavioral data stored in a database

[1844] Step 3: Emotion recognition by the emotion engine

[1845] Device: Collects the user's voice and facial expressions in real time.

[1846] Input: User's voice data, facial expression data

[1847] Data processing: Collection of voice and facial expression data

[1848] Output: Collected voice data and facial expression data

[1849] Terminal: Uses an emotion engine to analyze the collected data.

[1850] Input: Collected voice data, facial expression data

[1851] Data calculation: Analysis by emotion engine (e.g. Emotion API)

[1852] Output: Emotion data (e.g., joy, sadness, anger, etc.)

[1853] Terminal: Sends the analysis results to the server.

[1854] Input: Emotion data

[1855] Data calculation: Send to server using HTTP request

[1856] Output: Emotion data sent to the server

[1857] Server: Stores the received emotion data in a database.

[1858] Input: Emotion data received by the server

[1859] Data operation: Inserting data into the database

[1860] Output: Emotion data stored in a database

[1861] Step 4: Analyze the data

[1862] Server: Runs analytical algorithms based on user information, behavioral data, and emotional data.

[1863] Input: User information, behavioral data, and emotional data stored in a database

[1864] Data Computation: Applying machine learning models using Python's Pandas and Scikit-Learn

[1865] Output: Analysis results

[1866] Server: Stores the analysis results in a database and tags them appropriately.

[1867] Input: Analysis results

[1868] Data processing: storing and tagging analysis results in a database

[1869] Output: Analysis results stored in a database

[1870] Step 5: Select and provide information

[1871] Server: Selects the most appropriate information and advice for users based on the analysis results.

[1872] Input: Analysis results stored in the database

[1873] Data operations: Applying recommendation algorithms

[1874] Output: Selected information

[1875] Server: Sends the selected information to the device.

[1876] Input: Selected information

[1877] Data calculation: sent as HTTP response in JSON format

[1878] Output: Information sent to the terminal

[1879] Terminal: Notifies the user of the received information.

[1880] Input: Information received from the server

[1881] Data calculation: Push notifications and voice assistant notifications

[1882] Output: Notification to the user (e.g., a message for a voice assistant)

[1883] Step 6: Interact with the user

[1884] Users: Ask questions or give feedback on the information provided.

[1885] Input: Questions or feedback on the proposed information

[1886] Data processing: Feedback description

[1887] Output: Feedback data

[1888] Device: Sends feedback to the server in real time.

[1889] Input: User feedback

[1890] Data calculation: Send to server using HTTP request

[1891] Output: Feedback data sent to the server

[1892] Server: Analyzes the question and generates information that addresses the user's additional needs.

[1893] Input: Feedback data

[1894] Data Computing: Applying Text Analysis Algorithms

[1895] Output: Additional information

[1896] Server: Sends the generated information to the terminal.

[1897] Input: Additional information

[1898] Data calculation: sent in HTTP response

[1899] Output: Additional information sent to the terminal

[1900] Terminal: Notify the user of additional information.

[1901] Input: Additional information received from the server

[1902] Data calculation: Push notifications and voice assistant notifications

[1903] Output: User notification (e.g., "The recommended protein product is XX brand YY.")

[1904] Step 7: Bridging the digital divide

[1905] Server: Aggregates data from multiple users and analyzes it globally.

[1906] Input: Multi-user dataset

[1907] Data calculation: Analysis using big data analysis tools (e.g., Apache Hadoop)

[1908] Output: Globally analyzed results

[1909] Server: Provides the most useful information equally to all users.

[1910] Input: Globally analyzed results

[1911] Data processing: Converting to the appropriate format and distributing information evenly

[1912] Output: Useful information notice to all users

[1913] Prompt Sentence Examples

[1914] "Please suggest optimal health information for a female user in her 30s who is interested in running and dieting. Please provide specific advice based on recent behavioral and emotional data."

[1915] The above is the specific flow of the program processing of this system.

[1916] (Application example 2)

[1917] 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."

[1918] Conventional information delivery systems have difficulty providing information that takes into account the user's real-time emotions, and are therefore unable to provide optimal advertisements or information that reflect the user's current state or mood. Furthermore, methods for effectively utilizing user feedback and optimizing information delivery have been limited. As a result, there has been a problem in that the user experience has not been sufficiently improved.

[1919] 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.

[1920] In this invention, the server includes a means for analyzing the user's voice or facial expression to collect emotional data, a means for transmitting the emotional data to the server, and a means for analyzing the user information, behavioral data, and emotional data. This makes it possible to recognize the user's emotions in real time and provide optimal information and advertisements according to the user's emotional state. Furthermore, it is possible to quickly incorporate feedback from the user and improve the accuracy of the information provided.

[1921] An "information provision system" is a system that collects user information and provides appropriate data and content to users based on that information.

[1922] "User Information" refers to the profile data entered by a user when they first register, and includes information such as age, gender, interests, and purpose.

[1923] "Server" refers to a central processing unit that receives, stores, and analyzes user information, behavioral data, and emotional data, and provides appropriate information to users based on the analysis results.

[1924] A "database" is an information storage system for systematically accumulating and managing user information, behavioral data, emotional data, etc.

[1925] "User behavioral data" refers to data about a user's daily behavior, such as app usage and website browsing history.

[1926] "Emotional data" refers to data about the user's emotional state, such as joy, sadness, surprise, or anger, obtained by analyzing the user's voice and facial expressions.

[1927] An "emotion engine" is an analytical engine that analyzes input data such as voice and facial expressions to specifically identify the user's emotions.

[1928] An "analysis algorithm" is a mathematical processing method for profiling users' interests and concerns by comprehensively analyzing user information, behavioral data, and emotional data.

[1929] "Feedback" refers to the opinions and reactions that users give to the information they provide, and is important data that allows the system to provide even more optimal information based on this.

[1930] "Notifications" are messaging methods used to communicate selected information or advertisements to users.

[1931] An "ad serving app" is software that selects and serves optimal advertisements based on the user's emotional state and behavioral data.

[1932] A "voice assistant" is an interface that communicates with users via voice and provides necessary information.

[1933] This invention is an advertising system that recognizes a user's emotions in real time and provides optimal information and advertisements according to that emotional state. This system comprehensively analyzes user information, behavioral data, and emotional data, and provides appropriate feedback to the user.

[1934] System Configuration

[1935] The system mainly consists of a user terminal, a server, a database, an emotion engine, and an advertisement serving engine.

[1936] User device roles

[1937] User terminals refer to devices such as smartphones and smart glasses, and perform the following functions:

[1938] 1. Initial registration of user information

[1939] The user accesses the initial registration screen and enters their profile data (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores it in a database.

[1940] 2. Collecting behavioral data

[1941] The device periodically collects behavioral data, such as the user's app usage and website browsing history, and appropriately adjusts the timing of sending this data to the server.

[1942] 3. Collecting Emotional Data

[1943] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data, for example, by using libraries such as OpenCV and FaceNet for facial recognition and by converting voice data into text using Google Cloud Speech-to-Text.

[1944] Server Roles

[1945] The server has the following roles:

[1946] 1. Data Analysis

[1947] The user information, behavioral data, and emotional data are received and analyzed comprehensively using big data analysis tools (e.g., Apache Hadoop and TensorFlow). This analysis allows the system to select optimal information and advertisements based on the user's current emotional state and past behavioral patterns.

[1948] 2. Providing information and advertising

[1949] Based on the analysis results, we select information and advertisements appropriate for the user and send them to the user's device. For example, we provide advertisements using an advertisement delivery system (e.g., Magento).

[1950] 3. Processing Feedback

[1951] We collect user feedback and analyze it again, and use this feedback analysis to further optimize our advertising and information provision strategies.

[1952] Specific Examples

[1953] For example, when a user uses a smartphone, the camera captures the user's facial expressions and simultaneously collects their vocal responses. The collected data is sent to a server in real time, where an emotion analysis engine (e.g., IBM Watson Tone Analyzer) identifies the emotion. Big data analysis tools are then used to comprehensively analyze the user's current emotional state and past behavioral data, and the most appropriate advertisements are selected and delivered.

[1954] Prompt Sentence Examples

[1955] The following prompt can be used to generate the code for the sentiment analysis part of the generative AI model:

[1956] Generate Python code that has the following functionality:

[1957] Real-time face detection from webcam using OpenCV.

[1958] Uses Google Cloud Speech-to-Text to convert what the user says into text in real time.

[1959] Identify emotions from text using IBM Watson Tone Analyzer.

[1960] Emotion data is saved to local storage at regular intervals.

[1961] In this way, specific steps for implementing the present invention can be clearly defined, making it possible to provide information and advertisements that are optimal for the user's emotional state in real time.

[1962] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1963] Step 1: Initial registration of user information

[1964] A user accesses the initial registration screen using a device such as a smartphone and enters their profile information (age, gender, interests, purpose, etc.). The device sends the entered information to the server, which then stores the received information in a database. In this step, the input data is the user's profile information, and the output data is the user information stored in the database.

[1965] Step 2: Collect behavioral data

[1966] The device periodically records user behavioral data, such as app usage and website browsing history, in local storage. This data is then sent to the server at regular intervals. The input data in this step is the user behavioral data, and the output data is the behavioral history sent to the server.

[1967] Step 3: Collecting emotion data

[1968] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It performs facial recognition using OpenCV and FaceNet, and converts the voice into text using Google Cloud Speech-to-Text. Based on this data, it uses an emotion analysis engine such as IBM Watson Tone Analyzer to obtain emotion data, which is then sent to the server. The input data in this step are the user's facial expressions and voice information, and the output data is the emotion data sent to the server.

[1969] Step 4: Data synthesis and analysis

[1970] The server receives user information, behavioral data, and emotional data and performs an integrated analysis of them. Big data analysis tools such as Apache Hadoop and TensorFlow are used to profile users' interests, concerns, and emotional states. In this process, the input data are the various types of user data received, and the output data is profile information resulting from the analysis.

[1971] Step 5: Information and advertising selection

[1972] The server selects appropriate information and advertisements based on the results of the integrated analysis. For example, if a user is feeling depressed, it selects entertainment-related advertisements to help them relax. It selects the optimal advertisements using an advertisement delivery system such as Magento. The input data in this step are the analysis results, and the output data is the selected advertisement information.

[1973] Step 6: Information and advertising notices

[1974] The device notifies the user of the information and advertisements received from the server. This can be done, for example, via push notifications or a voice assistant. The notification may also include a message prompting the user for feedback. The input data in this step is the selected advertisement information, and the output data is the information notified to the user.

[1975] Step 7: Gather feedback and reanalyze

[1976] When a user responds to an advertisement or information (by clicking, asking a question, commenting, etc.), that feedback is sent from the device to the server. The server then analyzes this feedback again and uses it to optimize the advertising strategy. The input data in this step is the user feedback, and the output data is the improved advertising strategy as a result of the reanalysis.

[1977] Step 8: Analyzing individual and global data

[1978] The server globally analyzes data collected from multiple users and seeks ways to provide information equally. This reduces the information gap and ensures that useful information reaches all users. In this process, the input data is the data of all users, and the output data is an optimized information provision strategy as a result of the analysis.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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).

[1986] 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.

[1987] 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."

[1988] 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.

[1989] 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).

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] 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.

[1998] 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.

[1999] 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.

[2000] The following is further disclosed regarding the above embodiment.

[2001] (Claim 1)

[2002] In the information provision system,

[2003] a means for inputting user information;

[2004] means for transmitting the user information to a server;

[2005] means for storing said user information in a database;

[2006] means of collecting user behavior data;

[2007] means for transmitting the behavioral data to a server;

[2008] means for analyzing said user information and behavioral data;

[2009] A means for selecting information suitable for the user based on the analysis results;

[2010] means for notifying a user of the selected information;

[2011] means for receiving feedback from the user via said notification;

[2012] means for transmitting said feedback to a server for further analysis;

[2013] means for providing additional information to the user based on an analysis of said feedback;

[2014] A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users.

[2015] A system including:

[2016] (Claim 2)

[2017] 10. The system of claim 1, further comprising: means for providing the selected information to a user via a voice assistant.

[2018] (Claim 3)

[2019] 2. The system according to claim 1, further comprising means for periodically collecting the user behavior data and adjusting the timing of transmitting the data to the server.

[2020] "Example 1"

[2021] (Claim 1)

[2022] a means for inputting user information;

[2023] means for transmitting the user information to a server;

[2024] means for storing said user information in a database;

[2025] means of collecting user behavior data;

[2026] means for transmitting the behavioral data to a server;

[2027] means for analyzing said user information and behavioral data;

[2028] A means for selecting information suitable for the user based on the analysis results;

[2029] means for notifying a user of the selected information;

[2030] means for receiving feedback from the user via said notification;

[2031] means for transmitting said feedback to a server for further analysis;

[2032] means for providing additional information to the user based on an analysis of said feedback;

[2033] A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users.

[2034] a means for analyzing a user's natural language input using a generative AI model and generating an appropriate response;

[2035] means for providing the generated response to a user through a voice assistant function;

[2036] A system including:

[2037] (Claim 2)

[2038] 10. The system of claim 1, further comprising: means for providing the selected information to a user via a voice assistant.

[2039] (Claim 3)

[2040] 2. The system according to claim 1, further comprising means for periodically collecting the user behavior data and adjusting the timing of transmitting the data to the server.

[2041] "Application Example 1"

[2042] (Claim 1)

[2043] In the information provision system,

[2044] a means for inputting user information;

[2045] means for transmitting the user information to a server;

[2046] means for storing said user information in a database;

[2047] means of collecting user behavior data;

[2048] means for transmitting the behavioral data to a server;

[2049] means for analyzing said user information and behavioral data;

[2050] A means for selecting information suitable for the user based on the analysis results;

[2051] means for notifying a user of the selected information;

[2052] means for receiving feedback from the user via said notification;

[2053] means for transmitting said feedback to a server for further analysis;

[2054] means for providing additional information to the user based on an analysis of said feedback;

[2055] A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users.

[2056] A means to make meal suggestions and process food delivery orders;

[2057] A system including:

[2058] (Claim 2)

[2059] 10. The system of claim 1, further comprising: means for providing the selected information to a user via a voice assistant.

[2060] (Claim 3)

[2061] 2. The system according to claim 1, further comprising means for periodically collecting the user behavior data and adjusting the timing of transmitting the data to the server.

[2062] "Example 2: Combining Emotion Engines"

[2063] (Claim 1)

[2064] a means for inputting user information;

[2065] means for transmitting the user information to a server;

[2066] means for storing said user information in a database;

[2067] A means of periodically collecting user behavior data;

[2068] means for periodically transmitting the behavioral data to a server;

[2069] A means of collecting the user's voice and facial expressions in real time,

[2070] means for analyzing the voice and facial expression data using an emotion engine;

[2071] means for transmitting the analysis results to a server;

[2072] means for analyzing the user information, behavioral data, and emotion data;

[2073] A means for selecting information suitable for the user based on the analysis results;

[2074] means for notifying a user of the selected information;

[2075] means for receiving feedback from the user via said notification;

[2076] means for transmitting said feedback to a server for further analysis;

[2077] means for providing additional information to the user based on an analysis of said feedback;

[2078] A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users.

[2079] A system including:

[2080] (Claim 2)

[2081] 10. The system of claim 1, further comprising: means for providing the selected information to a user via a voice assistant.

[2082] (Claim 3)

[2083] 2. The system according to claim 1, further comprising means for periodically collecting the user behavior data and adjusting the timing of transmitting the data to the server.

[2084] "Application example 2 when combining emotion engines"

[2085] (Claim 1)

[2086] In the information provision system,

[2087] a means for inputting user information;

[2088] means for transmitting the user information to a server;

[2089] means for storing said user information in a database;

[2090] means of collecting user behavior data;

[2091] means for transmitting the behavioral data to a server;

[2092] means for collecting emotional data by analyzing the user's voice or facial expressions;

[2093] means for transmitting the emotion data to a server;

[2094] means for analyzing the user information, behavioral data, and emotion data;

[2095] A means for selecting information suitable for the user based on the analysis results;

[2096] means for notifying a user of the selected information;

[2097] means for receiving feedback from the user via said notification;

[2098] means for transmitting said feedback to a server for further analysis;

[2099] means for providing additional information to the user based on an analysis of said feedback;

[2100] A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users.

[2101] A system including:

[2102] (Claim 2)

[2103] 10. The system of claim 1, further comprising: means for providing the selected information to a user via a voice assistant.

[2104] (Claim 3)

[2105] 2. The system according to claim 1, further comprising means for periodically collecting the user's behavioral data and emotion data and adjusting the timing of transmitting the data to the server. [Explanation of symbols]

[2106] 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. In the information provision system, a means for inputting user information; means for transmitting the user information to a server; means for storing said user information in a database; means of collecting user behavior data; means for transmitting the behavioral data to a server; means for analyzing said user information and behavioral data; A means for selecting information suitable for the user based on the analysis results; means for notifying a user of the selected information; means for receiving feedback from the user via said notification; means for transmitting said feedback to a server for further analysis; means for providing additional information to the user based on an analysis of said feedback; A means of aggregating data from multiple users, analyzing it globally, and delivering uniform information to all users. A system including:

2. The system of claim 1 , further comprising means for providing the selected information to a user via a voice assistant.

3. The system according to claim 1 , further comprising means for periodically collecting the user behavior data and adjusting the timing of transmitting the data to the server.

Images