system

Abstract

We provide the system. [Solution] A computer device that selects information related to the user's interests and transmits it as a set of information data, A processing device that analyzes user interests based on received information data, collects and ranks related information, A processing device that generates media content tailored to the user and converts it into a format that is easy for the user to handle, A processing unit that transmits the generated media content to a computer and notifies the user, A computer and processing device that collects user feedback and uses it to improve the accuracy of future media content generation, A device characterized by providing the latest information and video clips based on the user's selection of interest categories, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, due to an overflow of a large amount of information, it is difficult for individual users to efficiently select and collect the information they need. This information overload situation increases the trouble and stress of information search for users and is a factor reducing productivity. Also, it is an issue that a lot of time is spent until relevant information is found.

Means for Solving the Problems

[0005] This invention provides a procedure for selecting user interest information via a terminal device and transmitting that information to a server device. Furthermore, the server device uses the received data packets to analyze the user's interests using a generation AI model, collecting and prioritizing relevant information. As a result, it automatically generates user-specific content, formats it in a user-friendly format, and transmits it to the terminal device. The terminal device then notifies the user of this content and is designed to improve the accuracy of future content generation by utilizing the feedback provided by the user. This allows users to efficiently obtain the information they need and reduces stress.

[0006] A "terminal device" is a device operated by a user that has the function of selecting information of the user's interest and sending it to a server device.

[0007] A "server device" is a device that uses an AI model to generate information based on the user's interests, collects and prioritizes relevant information, and generates and transmits user-specific content.

[0008] A "generative AI model" is an algorithm that analyzes user interests and automatically generates relevant content corresponding to that information.

[0009] A "data packet" is a collection of data containing user interest information, transmitted from a terminal device to a server device in a specific format.

[0010] A "user-friendly format" is a display format for content that is easily understood and usable by users.

[0011] "Feedback" refers to evaluations and opinions provided by users, and is used to improve the accuracy of future information provision. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The information processing system of the present invention aims to support users in efficiently collecting information and consists of a terminal device and a server device.

[0034] First, the user launches a dedicated app on their device and selects categories of information they are interested in. This saves their interest information to their device. This information reflects the user's preferences and interests and will later be used to generate content.

[0035] The terminal forms the user's saved interest information into data packets and sends them to the server device. Based on the received data packets, the server uses a generative AI model to collect relevant information based on the user's interests. The AI ​​model selects the most relevant information from a large number of sources and prioritizes it using a pre-configured algorithm.

[0036] Next, the server generates user-specific content based on the prioritized information. This content is delivered to the user in the form of news summaries, video clips, recommendation lists, etc., and is formatted in a user-friendly way.

[0037] The generated content is sent from the server to the device, and the device notifies the user, allowing it to be displayed within the app. Users can easily view and interact with this content and provide feedback on the information provided.

[0038] Finally, the terminal device collects user feedback and sends it to the server as data to be used in future content generation. This integration enables the system to continuously provide optimal information tailored to the user's preferences.

[0039] For example, if a user is interested in "travel" and "gourmet food," the server can collect the latest tourist information and restaurant ratings related to these interests and prioritize providing the user with the most relevant information. In this way, the present invention provides an effective means to streamline information acquisition for users and reduce stress.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches a dedicated app on their device and selects information categories that interest them (e.g., travel, food, technology). This selection information is saved on the device as a user profile.

[0043] Step 2:

[0044] The terminal packages the user's selected interest information into data packets and transmits them to the server device via the communication network. This data includes the user's interest categories and past behavioral history.

[0045] Step 3:

[0046] The server analyzes the received data packets to identify the user's interests. A generative AI model is then activated to collect information related to the user's interests from multiple sources. During this process, the relevance of the collected information is evaluated and prioritized.

[0047] Step 4:

[0048] The server uses prioritized information to generate user-specific content. This content is formatted into user-friendly summary formats such as text, video clips, and news summaries.

[0049] Step 5:

[0050] The server generates content and sends it to the terminal device, which then notifies the user and displays the content. The user can review the provided content and provide feedback as needed.

[0051] Step 6:

[0052] The device collects feedback from users and sends it to the server device. The server uses this feedback as training data for its AI generation model to improve the accuracy of future content generation.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In modern society, users face information overload and find it difficult to quickly and accurately obtain the information they need. Furthermore, with the wide variety of information available, there is a growing need to efficiently provide information tailored to users' interests. Conventional systems often fail to adequately provide information that matches individual user preferences, which can impair the user experience.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for analyzing the user's areas of interest, acquiring and ranking relevant information, constructing user-specific information, converting it into a user-friendly format, and transmitting the generated information to a device and presenting it to the user. This makes it possible to efficiently provide information tailored to the user's preferences.

[0058] A "user" is an entity that receives information and content, and is a person who utilizes an information processing system.

[0059] "Areas of interest" refers to the categories or themes of information that a user is interested in.

[0060] A "data format" is a means of representing information in a specific structure, and is the format used during communication.

[0061] "Device" refers to equipment or systems used for processing, transmitting, or receiving information.

[0062] A "processing unit" refers to a device that has the ability to process and analyze received information.

[0063] "Acquisition and ranking" refers to the process of gathering relevant information and organizing it based on its relevance and importance.

[0064] "User-specific information" refers to information that has been specially tailored based on the user's interests and preferences.

[0065] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate information and create content tailored to the user's interests.

[0066] "Learning information" refers to the data and knowledge used to improve generative AI models.

[0067] "Software" refers to the programs and applications necessary to operate a device.

[0068] This invention presents a form of information processing system. This system aims to effectively acquire and provide information tailored to the user's interests. The system primarily consists of three elements: a terminal, a server, and a generative AI model, all of which work together.

[0069] The terminal acts as the interface with the user and uses dedicated software to collect information about the user's interests. The user selects areas of interest through the application, and this information is stored on the terminal. This selected information is then packetized as data and sent to a server for further processing.

[0070] The server has a central information processing function and uses a generative AI model to retrieve and rank relevant information based on data received from terminals. This model searches for useful content from a large amount of data sources, analyzes that information, and constructs information in a form optimized for the user's preferences.

[0071] The acquired information is further formatted on the server into a user-friendly format and sent to the terminal as generated, dedicated content. This allows the user to smoothly view and interact with the information within the application. Specific examples of prompts generated using the AI ​​model include "Please retrieve the latest travel information" and "Please return restaurant ratings based on the user's culinary interests."

[0072] This system allows users to quickly and efficiently obtain the information they need and to access useful information tailored to their interests at any time. This reduces the stress users experience from information overload and provides a better information experience.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user launches a dedicated app on their device and selects their areas of interest. The input is the user's selected areas of interest, and the output is the temporary storage of this information on the device. Specifically, the user selects multiple categories of interest on the app screen, and this information is recorded on the device in data format.

[0076] Step 2:

[0077] The terminal converts user interest information into data packets in formats such as JSON. The input is selected interest area information, and the output is a data packet to be sent to the server. When converting to JSON format, the user ID and selected category information are included in the packet and securely sent to the server over the network.

[0078] Step 3:

[0079] The server analyzes data packets received from the terminal. The input is the received data packets, and the output is a profile of user interest information based on the analysis results. To build the profile, the server matches the user's selection information against a database and converts it into a format that can be used by the generated AI model.

[0080] Step 4:

[0081] The server uses a generative AI model to collect and prioritize relevant information based on the user's interests. The input is a profile of the user's interests, and the output is a prioritized list of relevant information. The server retrieves data from multiple sources and uses algorithms to select the most relevant information.

[0082] Step 5:

[0083] The server generates and formats user-specific content based on prioritized information. The input is a prioritized list, and the output is user-optimized content. The generated content is formatted into user-friendly formats such as news summaries and video clips.

[0084] Step 6:

[0085] The server sends the generated content to the terminal. The input is the formatted content, and the output is the completion of the transmission to the terminal. The terminal notifies the user of the received content and displays it within the application.

[0086] Step 7:

[0087] Users view the provided content and provide feedback. The input is the user's feedback information, and the output is the feedback recorded on the device. The device collects this feedback and sends it to the server to help improve the system in the future.

[0088] Step 8:

[0089] The server analyzes the feedback received from the terminal and updates it as training information for the generative AI model. The input is user feedback data, and the output is an improved generative AI model. This optimizes the next information delivery to better match the user's preferences.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In today's information society, it is difficult for users to efficiently and quickly obtain the information they need from the vast amount of data available. Furthermore, building a system that appropriately provides optimal content based on user interests is technically complex. Therefore, there is a need for methods that accurately understand user interests and efficiently provide content that meets those interests.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means including a computer device that selects information relating to the user's interests and transmits it as a group of information data; means including a processing device that analyzes the user's interests based on the received group of information data, collects and ranks relevant information; and means including a processing device that transmits the generated media content to the computer device and notifies the user. This makes it possible to efficiently provide optimal content tailored to the user and to quickly obtain the information the user needs.

[0095] "User interests" refer to specific themes, topics, or fields that individual users are interested in.

[0096] "Information data set" refers to a collection of data related to user interests that is transmitted through a computer device.

[0097] A "computer device" is a device used to exchange data with users and display processing results.

[0098] A "processing device" refers to a device that has the function of analyzing received data and generating and providing necessary information.

[0099] "Media content" refers to the specific format and content of information provided to users, including video clips, news articles, recommendation lists, etc.

[0100] "Prioritization" refers to the process of assigning priority levels to collected information and rearranging it accordingly.

[0101] A "generative AI model" refers to an artificial intelligence-based algorithm used to generate content based on user interests.

[0102] The system for carrying out this invention consists of a computer device and a server device. The computer device provides information related to the user's interests as options, generates the information selected by the user as a data set, and transmits it to the server device. Based on this received data set, the server device analyzes the user's interests using a generative AI model. In this analysis, information is collected and ranked based on its relevance.

[0103] The server device generates media content suitable for the user based on information provided by the computer device and formats it into a user-friendly format. The formatted media content is transmitted to the computer device and notified to the user.

[0104] Users can provide their ratings and opinions while viewing the provided media content. The computer collects this feedback and sends it to the server. This feedback is used to improve the accuracy of future content generation.

[0105] As a concrete example, this system can be implemented as a smartphone application. For instance, if a user interested in travel selects "tourist destinations" and "gourmet food," the server uses this information to utilize a generative AI model to collect information on recent tourist spots and highly-rated restaurants. This allows the content most likely to interest the user to be displayed preferentially.

[0106] The hardware and software used include terminal devices such as smartphones and tablets, and high-performance cloud servers. The software includes machine learning libraries such as TENSORFLOW® and PyTorch to run generative AI models, and frameworks such as Flask and Django to manage communication with the server.

[0107] An example of a prompt for a generative AI model might be: "User's interest categories: Health, Fitness. What information should be collected to provide the latest research findings and exercise videos?"

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The terminal displays an interface for the user to select their areas of interest. The user chooses a category that corresponds to their interests from the displayed options. This input information becomes the basic data for subsequent processing.

[0111] Step 2:

[0112] The terminal formats the selected user's interests as a data set and sends it to the server. The transmitted data set includes metadata such as category ID, timestamp, and user ID.

[0113] Step 3:

[0114] The server takes the received data set as input and analyzes it. Using a generative AI model, it collects relevant information based on the analyzed user interest data. This information is then ranked in order of relevance by an algorithm.

[0115] Step 4:

[0116] The server generates media content optimized for the user based on ranked information. This generated content includes news articles, video clips, and recommended item lists that may be of interest. This data is then formatted into a user-friendly format.

[0117] Step 5:

[0118] The server sends the formatted media content to the terminal and notifies the user. The terminal receives this and displays it on the user interface.

[0119] Step 6:

[0120] Users review the displayed media content and provide their ratings and feedback. The device collects this feedback and sends it to the server as data packets.

[0121] Step 7:

[0122] The server uses the received feedback data as training data for its AI model, adjusting the algorithm for future content generation. This feedback loop enables the provision of content that more accurately reflects user interests.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] The information processing system of the present invention aims to provide personalized content based on the user's interests and emotions. The system consists of a terminal device, a server device, and an emotion engine.

[0125] First, the user launches a dedicated app on their device and selects categories of interest. This saves the user's interest information to the device. Furthermore, while using the app, an emotion engine recognizes the user's emotions in real time through facial expressions, voice, and other data. This emotion data is used to improve the user experience.

[0126] The terminal constructs acquired interest and sentiment data as data packets and sends them to the server device. The server receives this data and uses a generative AI model to collect and analyze relevant information based on the user's interests. Sentiment data is also considered when prioritizing the information so that it matches the user's current emotions.

[0127] Next, the server generates user-specific content based on the prioritized data. This content is optimized for the user's interests and emotions and is delivered in formats such as video clips, text, and images. The server formats this content and sends it to the terminal device.

[0128] The device notifies the user of received content and displays it within the app. The user can view this content and, if necessary, provide feedback through emotional changes constantly measured by the emotion engine and manual evaluations.

[0129] Finally, the feedback collected by the device is sent to a server device, which uses that feedback as training data for the generating AI model and emotion engine. This allows the system to continuously improve its ability to provide users with more appropriate content.

[0130] As a concrete example, let's assume a user is searching for information about "travel" but is feeling stressed. The emotion engine can recognize this stress and prioritize providing information about relaxing tourist spots or calming music. This makes the information provided to the user more personalized, resulting in a more satisfying user experience.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user launches a dedicated app on their device and selects information categories of interest. This selection information is saved on the device as a profile.

[0134] Step 2:

[0135] The device activates an emotion engine and recognizes emotions in real time through the user's facial expressions, voice, touch intensity, and other factors. The recognized emotion data is added to the user's profile.

[0136] Step 3:

[0137] The device packages user interest information and emotional data as data packets and sends them to the server device. This data reflects the user's current state and is necessary for generating personalized content.

[0138] Step 4:

[0139] The server analyzes the received data packets and activates a generative AI model to collect relevant information aligned with the user's interests. During this process, emotional data is also considered, with priority given to information that resonates with the user's emotions.

[0140] Step 5:

[0141] Based on the data collected by the server, personalized content is generated that responds to the user's emotions. For example, if the user wants to relax, content including related calming videos and music will be generated.

[0142] Step 6:

[0143] The server formats the generated content into a user-friendly format and sends it to the terminal.

[0144] Step 7:

[0145] The device receives the transmitted content, notifies the user, and displays it within the app. The user views the provided content, and the emotion engine continues to monitor their emotional state while they are viewing the content.

[0146] Step 8:

[0147] Users provide feedback on the content, including their opinions and feelings. This feedback is used to improve future content creation.

[0148] Step 9:

[0149] The device sends feedback to the server, which then incorporates it into the generated AI model and emotion engine training data, thereby improving the accuracy of future content delivery.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0152] Traditional information delivery systems had limitations in providing relevant information based on user interests, and it was difficult to deliver personalized content that took into account the user's real-time emotional state. Furthermore, there was a lack of mechanisms to efficiently utilize user feedback and improve the quality of the content provided, resulting in insufficient improvement in the user experience.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for analyzing the user's facial expressions and voice to acquire emotional data, means for analyzing the user's interests and collecting and organizing relevant information using generative AI technology based on the acquired interest information and emotional data, and means for prioritizing information according to the user's emotions and generating user-friendly output. This makes it possible to provide more personalized content that simultaneously considers the user's interests and emotional state.

[0155] An "information processing device" is a device that selects information of interest to the user, constructs it as a data structure, and sends it to a server.

[0156] "Emotional data" refers to data that indicates a user's emotional state, obtained in real time by analyzing the user's facial expressions and voice.

[0157] "Generative AI technology" is a technology that analyzes, collects, and organizes relevant information based on users' interest information and sentiment data, and uses it to generate personalized content.

[0158] A "computational device" is a device that performs processing to analyze and organize related information based on acquired interest information and emotion data.

[0159] "User-friendly output" refers to content that prioritizes and presents information in an easy-to-use format based on the user's emotions.

[0160] A "display device" is a device that provides generated content to users and operates through a user-specific application.

[0161] "Feedback" refers to the evaluations and reactions that users give to the content provided, and this information is used to improve the quality of future content creation.

[0162] This information processing system aims to provide personalized content based on the user's interests and emotions, and mainly consists of terminal devices, server devices, and an emotion recognition engine.

[0163] The terminal device is equipped with a dedicated application for user operation. Using this application, users can select categories of interest. This selection information is stored as a data structure within the device, and a sentiment recognition engine analyzes the user's facial expressions and voice in real time within the application to obtain user sentiment data. This sentiment data is a crucial element for improving the user experience.

[0164] The server device receives interest information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to collect and analyze information. The server selects and collects content related to the user's interests from the internet and databases. In this process, by prioritizing information based on the user's current emotions using sentiment data, it becomes possible to generate more appropriate content.

[0165] The server generates user-optimized content in the form of video clips, text, and images based on the generated data. This information is then formatted in a user-friendly format and sent to the device.

[0166] Users view content displayed on their devices and provide feedback on that content. This feedback is often expressed as changes in emotion or as an in-app rating. This feedback information is sent back from the device to the server and used as training data for the generative AI model and emotion recognition engine, contributing to improvements in the accuracy of future content generation.

[0167] As a concrete example, consider a situation where a user is seeking information related to "travel" and is also experiencing stress. In this case, the emotion recognition engine detects that the user is under stress and prioritizes providing information about relaxing tourist spots and calming music. In this way, the system provides a more highly personalized user experience.

[0168] An example of a prompt might be: "The user's interest category is 'travel,' and the emotion engine has detected stress. Please generate recommended content about relaxing destinations."

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user launches a dedicated app on their device and selects a category of interest. This interest information is saved as data in the device's memory. For example, the user can select the category "Cooking." The input is the user's selection, and the output is the selected interest information.

[0172] Step 2:

[0173] The device uses an emotion recognition engine built into the app to sense the user's facial expressions and voice and acquire emotion data in real time. This uses the device's camera and microphone. The input is the user's facial expressions and voice information, and the output is emotion data based on that. This emotion data forms the basis for more precisely customizing the user experience.

[0174] Step 3:

[0175] The device integrates stored interest information and acquired sentiment data, constructing them as data packets. These are then sent to a server via the internet. The input is interest information and sentiment data, and the output is the transmitted data packets. Specifically, the data packets are sent using the HTTP or HTTPS protocol.

[0176] Step 4:

[0177] The server analyzes data packets received from the terminal and uses a generative AI model to collect information related to the user's interests. It also takes sentiment data into consideration to determine the priority of the information. The input is data packets from the terminal, and the output is the prioritized information. Specifically, the generative AI model uses natural language processing techniques to generate prompt sentences and searches for information based on them.

[0178] Step 5:

[0179] The server generates user-optimized content and formats it into a user-friendly format. For example, it creates content in formats such as video, text, and images. The input is prioritized information, and the output is formatted content. Specifically, it can use an API to call video editing software and generate video clips.

[0180] Step 6:

[0181] The server sends the generated content to the device. The device notifies the user of this content and displays it within the app. The input is the content data from the server, and the output is the screen information displayed to the user. A push notification system is used for this specific operation.

[0182] Step 7:

[0183] The user views the presented content, and the emotion recognition engine measures their reaction again. Users can also manually enter their evaluation through the in-app feedback option. The input is the user's visual and auditory reactions, and the output is feedback information based on those reactions.

[0184] Step 8:

[0185] The device sends the collected feedback to the server. The server uses this as training material for the generative AI model and emotion recognition engine, and utilizes it to improve system performance. The input is feedback information from the device, and the output is information stored as training data. Specifically, the model is updated using a machine learning algorithm.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] While the massive supply of information in modern society offers convenience to users, it also presents the problem of causing stress due to the overwhelming amount of information received. Therefore, in order for users to lead more comfortable and satisfying lives, information needs to be provided in a way that suits not only individual interests and concerns but also their emotional states. Furthermore, traditional information distribution services have suffered from the problem of providing users with a simplistic experience because they do not consider their emotions.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes terminal means for transmitting information about the user's interests as data packets, means for analyzing the user's interests based on the received data packets, collecting and prioritizing relevant information, and an emotion engine for recognizing the user's emotional state and adjusting the set of information provided according to that emotional state. This enables the provision of more personalized and stress-reducing information tailored to the user's interests and emotional state.

[0191] "Information about user interests" refers to information related to categories and topics that users are interested in.

[0192] A "data packet" is a unit of data used when sending and receiving information over a communication network.

[0193] A "terminal device" is a device used by a user to input or receive information.

[0194] A "server" is a computing device used to process data, collect and analyze information, and transmit it.

[0195] An "information collection" is a collection of content provided to individual users.

[0196] A "user-friendly format" is a display format designed to be intuitive and easy for users to use.

[0197] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0198] "Feedback" refers to the actions or information that users provide in response to or evaluate the content they have been given.

[0199] A "generative AI model" is a system that uses artificial intelligence to generate appropriate information and ideas.

[0200] To realize this invention, the system has a server at its core equipped with a generative AI model and an emotion engine. The server receives information about the user's interests as data packets from the terminal. The terminal functions as a smartphone or tablet and can be used by the user after installing a dedicated application in advance. The terminal has an emotion engine that analyzes the user's facial expressions and voice in real time, thereby recognizing the user's emotional state.

[0201] The server analyzes and processes received data on interests and emotions, and generates relevant information sets in a user-friendly format. Using a generative AI model during the analysis process, the server effectively collects and prioritizes information that aligns with the user's interests. Furthermore, by tailoring the information according to the user's emotions, it can deliver an experience optimized for each individual user.

[0202] For example, when a user is in a cheerful mood, the emotion engine recognizes this state and prioritizes recommending highly entertaining content. Conversely, when the user wants to calm down, it recommends relaxing content. An example of a prompt message generated by the generating AI model would be: "The user is interested in comedy movies, and their current emotional state is to relax. Please recommend relaxing movies."

[0203] Ultimately, the generated content is sent from the server to the device and provided to the user. The user reviews the content and provides feedback if any. This feedback is collected again on the server side and used to improve the accuracy of future content generation. This cycle is designed to progressively improve the user experience.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user launches a dedicated application on their device and selects a category of interest. The user's selected interest information is entered into the device and sent to the server as a data packet. At this time, the device analyzes the user's facial expressions and voice using an emotion engine, and similarly includes the emotional state in the data packet before sending it.

[0207] Step 2:

[0208] The server analyzes data packets received from the terminal. Using interest information and emotion data as input, the server utilizes a generative AI model to collect information based on the user's interests. Data prioritization is performed so that the collected information matches the user's current emotional state. The server outputs a set of information prioritized according to interests and emotions.

[0209] Step 3:

[0210] The server uses this prioritized information set to generate content in a user-friendly format. During this process, the generating AI model generates prompts as needed, gathering and refining information. Specifically, it might generate a prompt such as, "The user's interest is in comedy films, and their current emotional state is to relax. Please recommend relaxing films." The output is a formatted information set in a specific format.

[0211] Step 4:

[0212] The server sends the generated information set to the device. The device notifies the user of the received content and displays it within the app. The user can review the content and, if they have any feedback, enter it into the device. The device then sends that feedback back to the server as a data packet.

[0213] Step 5:

[0214] The server receives feedback data sent by users and uses it as training data for future content generation. Specifically, it analyzes the feedback information using a generative AI model and integrates it as foundational data to improve accuracy in future information collection and distribution. Through this step, continuous service improvement is achieved.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] The information processing system of the present invention aims to support users in efficiently collecting information and consists of a terminal device and a server device.

[0232] First, the user launches a dedicated app on their device and selects categories of information they are interested in. This saves their interest information to their device. This information reflects the user's preferences and interests and will later be used to generate content.

[0233] The terminal forms the user's saved interest information into data packets and sends them to the server device. Based on the received data packets, the server uses a generative AI model to collect relevant information based on the user's interests. The AI ​​model selects the most relevant information from a large number of sources and prioritizes it using a pre-configured algorithm.

[0234] Next, the server generates user-specific content based on the prioritized information. This content is delivered to the user in the form of news summaries, video clips, recommendation lists, etc., and is formatted in a user-friendly way.

[0235] The generated content is sent from the server to the device, and the device notifies the user, allowing it to be displayed within the app. Users can easily view and interact with this content and provide feedback on the information provided.

[0236] Finally, the terminal device collects user feedback and sends it to the server as data to be used in future content generation. This integration enables the system to continuously provide optimal information tailored to the user's preferences.

[0237] For example, if a user is interested in "travel" and "gourmet food," the server can collect the latest tourist information and restaurant ratings related to these interests and prioritize providing the user with the most relevant information. In this way, the present invention provides an effective means to streamline information acquisition for users and reduce stress.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user launches a dedicated app on their device and selects information categories that interest them (e.g., travel, food, technology). This selection information is saved on the device as a user profile.

[0241] Step 2:

[0242] The terminal packages the user's selected interest information into data packets and transmits them to the server device via the communication network. This data includes the user's interest categories and past behavioral history.

[0243] Step 3:

[0244] The server analyzes the received data packets to identify the user's interests. A generative AI model is then activated to collect information related to the user's interests from multiple sources. During this process, the relevance of the collected information is evaluated and prioritized.

[0245] Step 4:

[0246] The server uses prioritized information to generate user-specific content. This content is formatted into user-friendly summary formats such as text, video clips, and news summaries.

[0247] Step 5:

[0248] The server generates content and sends it to the terminal device, which then notifies the user and displays the content. The user can review the provided content and provide feedback as needed.

[0249] Step 6:

[0250] The device collects feedback from users and sends it to the server device. The server uses this feedback as training data for its AI generation model to improve the accuracy of future content generation.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0253] In modern society, users face information overload and find it difficult to quickly and accurately obtain the information they need. Furthermore, with the wide variety of information available, there is a growing need to efficiently provide information tailored to users' interests. Conventional systems often fail to adequately provide information that matches individual user preferences, which can impair the user experience.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes means for analyzing the user's areas of interest, acquiring and ranking relevant information, constructing user-specific information, converting it into a user-friendly format, and transmitting the generated information to a device and presenting it to the user. This makes it possible to efficiently provide information tailored to the user's preferences.

[0256] A "user" is an entity that receives information and content, and is a person who utilizes an information processing system.

[0257] "Areas of interest" refers to the categories or themes of information that a user is interested in.

[0258] A "data format" is a means of representing information in a specific structure, and is the format used during communication.

[0259] "Device" refers to equipment or systems used for processing, transmitting, or receiving information.

[0260] A "processing unit" refers to a device that has the ability to process and analyze received information.

[0261] "Acquisition and ranking" refers to the process of gathering relevant information and organizing it based on its relevance and importance.

[0262] "User-specific information" refers to information that has been specially tailored based on the user's interests and preferences.

[0263] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate information and create content tailored to the user's interests.

[0264] "Learning information" refers to the data and knowledge used to improve generative AI models.

[0265] "Software" refers to the programs and applications necessary to operate a device.

[0266] This invention presents a form of information processing system. This system aims to effectively acquire and provide information tailored to the user's interests. The system primarily consists of three elements: a terminal, a server, and a generative AI model, all of which work together.

[0267] The terminal acts as the interface with the user and uses dedicated software to collect information about the user's interests. The user selects areas of interest through the application, and this information is stored on the terminal. This selected information is then packetized as data and sent to a server for further processing.

[0268] The server has a central information processing function and uses a generative AI model to retrieve and rank relevant information based on data received from terminals. This model searches for useful content from a large amount of data sources, analyzes that information, and constructs information in a form optimized for the user's preferences.

[0269] The acquired information is further formatted on the server into a user-friendly format and sent to the terminal as generated, dedicated content. This allows the user to smoothly view and interact with the information within the application. Specific examples of prompts generated using the AI ​​model include "Please retrieve the latest travel information" and "Please return restaurant ratings based on the user's culinary interests."

[0270] This system allows users to quickly and efficiently obtain the information they need and to access useful information tailored to their interests at any time. This reduces the stress users experience from information overload and provides a better information experience.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user launches a dedicated app on their device and selects their areas of interest. The input is the user's selected areas of interest, and the output is the temporary storage of this information on the device. Specifically, the user selects multiple categories of interest on the app screen, and this information is recorded on the device in data format.

[0274] Step 2:

[0275] The terminal converts user interest information into data packets in formats such as JSON. The input is selected interest area information, and the output is a data packet to be sent to the server. When converting to JSON format, the user ID and selected category information are included in the packet and securely sent to the server over the network.

[0276] Step 3:

[0277] The server analyzes the data packets received from the terminal. The input is the received data packet, and the output is a profile of user interest information based on the analysis result. The server collates the user's selection information with the database to build a profile and converts it into a format that can be used by the generation AI model.

[0278] Step 4:

[0279] The server uses the generation AI model to collect and prioritize relevant information based on the user's interests. The input is the profile of the user's interest information, and the output is a prioritized list of relevant information. The server obtains data from multiple information sources and selects the most relevant information using an algorithm.

[0280] Step 5:

[0281] The server generates and formats user-specific content based on the prioritized information. The input is the prioritized list, and the output is content optimized for the user. The generated content is formatted into a user-friendly form such as news summaries and video clips.

[0282] Step 6:

[0283] The server sends the generated content to the terminal. The input is the formatted content, and the output is the completion of transmission to the terminal. The terminal notifies the user of the received content and displays it within the application.

[0284] Step 7:

[0285] The user views the provided content and enters feedback. The input is the user's feedback information, and the output is the feedback recorded on the terminal. The terminal collects this feedback and sends it to the server for use in improving the system in subsequent times.

[0286] Step 8:

[0287] The server analyzes the feedback received from the terminal and updates it as learning information for the generation AI model. The input is the user's feedback data, and the output is an improved generation AI model. As a result, the next information provision is optimized to better match the user's preferences.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In modern information society, it is difficult for users to efficiently and quickly obtain the necessary information from a vast amount of information. Also, it is technically complex to construct a mechanism for appropriately providing optimal content based on the interests of users. Therefore, there is a need for a method to accurately understand the interests of users and efficiently provide corresponding content.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes means comprising a computer device that selects information regarding the user's concerns and transmits it as an information data group, means comprising a processing device that analyzes the user's interests based on the received information data group and collects and serializes relevant information, and means comprising a processing device that transmits the generated media content to a computer device and notifies the user. Thereby, it becomes possible to efficiently provide optimal content tailored to the user and quickly obtain the information required by the user.

[0293] The "user's concerns" refer to specific themes, topics, or fields in which individual users are interested.

[0294] "Information data set" refers to a collection of data related to user interests that is transmitted through a computer device.

[0295] A "computer device" is a device used to exchange data with users and display processing results.

[0296] A "processing device" refers to a device that has the function of analyzing received data and generating and providing necessary information.

[0297] "Media content" refers to the specific format and content of information provided to users, including video clips, news articles, recommendation lists, etc.

[0298] "Prioritization" refers to the process of assigning priority levels to collected information and rearranging it accordingly.

[0299] A "generative AI model" refers to an artificial intelligence-based algorithm used to generate content based on user interests.

[0300] The system for carrying out this invention consists of a computer device and a server device. The computer device provides information related to the user's interests as options, generates the information selected by the user as a data set, and transmits it to the server device. Based on this received data set, the server device analyzes the user's interests using a generative AI model. In this analysis, information is collected and ranked based on its relevance.

[0301] The server device generates media content suitable for the user based on information provided by the computer device and formats it into a user-friendly format. The formatted media content is transmitted to the computer device and notified to the user.

[0302] While viewing the provided media content, the user can provide their own evaluations and opinions. The computer device collects this feedback and transmits it to the server device. This feedback is utilized to improve the accuracy of content generation in subsequent sessions.

[0303] As a specific example, this system can be implemented as a smartphone application. For instance, when a user interested in travel selects "tourist attractions" and "gourmet", the server utilizes the AI model generated based on this information to collect information on recent tourist spots and popular restaurants. As a result, the content that the user is most likely to be interested in is displayed preferentially.

[0304] The hardware and software used include terminal devices such as smartphones and tablets, and high-performance cloud servers. For software, machine learning libraries such as TensorFlow and PyTorch for running the generated AI model, and frameworks such as Flask and Django for managing communication with the server are used.

[0305] As an example of a prompt sentence for the generated AI model, a query such as "User's interest categories: health, fitness. What information should be collected to provide the latest research results and exercise videos?" can be considered.

[0306] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The terminal displays an interface for the user to select their areas of concern. The user selects the category that corresponds to their interests from the multiple displayed options. This input information serves as the basic data for subsequent processing.

[0309] Step 2:

[0310] The terminal formats the selected user's interests as a data set and sends it to the server. The transmitted data set includes metadata such as category ID, timestamp, and user ID.

[0311] Step 3:

[0312] The server takes the received data set as input and analyzes it. Using a generative AI model, it collects relevant information based on the analyzed user interest data. This information is then ranked in order of relevance by an algorithm.

[0313] Step 4:

[0314] The server generates media content optimized for the user based on ranked information. This generated content includes news articles, video clips, and recommended item lists that may be of interest. This data is then formatted into a user-friendly format.

[0315] Step 5:

[0316] The server sends the formatted media content to the terminal and notifies the user. The terminal receives this and displays it on the user interface.

[0317] Step 6:

[0318] Users review the displayed media content and provide their ratings and feedback. The device collects this feedback and sends it to the server as data packets.

[0319] Step 7:

[0320] The server uses the received feedback data as training data for its AI model, adjusting the algorithm for future content generation. This feedback loop enables the provision of content that more accurately reflects user interests.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] The information processing system of the present invention aims to provide personalized content based on the user's interests and emotions. The system consists of a terminal device, a server device, and an emotion engine.

[0323] First, the user launches a dedicated app on their device and selects categories of interest. This saves the user's interest information to the device. Furthermore, while using the app, an emotion engine recognizes the user's emotions in real time through facial expressions, voice, and other data. This emotion data is used to improve the user experience.

[0324] The terminal constructs acquired interest and sentiment data as data packets and sends them to the server device. The server receives this data and uses a generative AI model to collect and analyze relevant information based on the user's interests. Sentiment data is also considered when prioritizing the information so that it matches the user's current emotions.

[0325] Next, the server generates user-specific content based on the prioritized data. This content is optimized for the user's interests and emotions and is delivered in formats such as video clips, text, and images. The server formats this content and sends it to the terminal device.

[0326] The device notifies the user of received content and displays it within the app. The user can view this content and, if necessary, provide feedback through emotional changes constantly measured by the emotion engine and manual evaluations.

[0327] Finally, the feedback collected by the device is sent to a server device, which uses that feedback as training data for the generating AI model and emotion engine. This allows the system to continuously improve its ability to provide users with more appropriate content.

[0328] As a concrete example, let's assume a user is searching for information about "travel" but is feeling stressed. The emotion engine can recognize this stress and prioritize providing information about relaxing tourist spots or calming music. This makes the information provided to the user more personalized, resulting in a more satisfying user experience.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user launches a dedicated app on their device and selects information categories of interest. This selection information is saved on the device as a profile.

[0332] Step 2:

[0333] The device activates an emotion engine and recognizes emotions in real time through the user's facial expressions, voice, touch intensity, and other factors. The recognized emotion data is added to the user's profile.

[0334] Step 3:

[0335] The device packages user interest information and emotional data as data packets and sends them to the server device. This data reflects the user's current state and is necessary for generating personalized content.

[0336] Step 4:

[0337] The server analyzes the received data packets and activates a generative AI model to collect relevant information aligned with the user's interests. During this process, emotional data is also considered, with priority given to information that resonates with the user's emotions.

[0338] Step 5:

[0339] Based on the data collected by the server, personalized content is generated that responds to the user's emotions. For example, if the user wants to relax, content including related calming videos and music will be generated.

[0340] Step 6:

[0341] The server formats the generated content into a user-friendly format and sends it to the terminal.

[0342] Step 7:

[0343] The device receives the transmitted content, notifies the user, and displays it within the app. The user views the provided content, and the emotion engine continues to monitor their emotional state while they are viewing the content.

[0344] Step 8:

[0345] Users provide feedback on the content, including their opinions and feelings. This feedback is used to improve future content creation.

[0346] Step 9:

[0347] The device sends feedback to the server, which then incorporates it into the generated AI model and emotion engine training data, thereby improving the accuracy of future content delivery.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] Traditional information delivery systems had limitations in providing relevant information based on user interests, and it was difficult to deliver personalized content that took into account the user's real-time emotional state. Furthermore, there was a lack of mechanisms to efficiently utilize user feedback and improve the quality of the content provided, resulting in insufficient improvement in the user experience.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for analyzing the user's facial expressions and voice to acquire emotional data, means for analyzing the user's interests and collecting and organizing relevant information using generative AI technology based on the acquired interest information and emotional data, and means for prioritizing information according to the user's emotions and generating user-friendly output. This makes it possible to provide more personalized content that simultaneously considers the user's interests and emotional state.

[0353] An "information processing device" is a device that selects information of interest to the user, constructs it as a data structure, and sends it to a server.

[0354] "Emotional data" refers to data that indicates a user's emotional state, obtained in real time by analyzing the user's facial expressions and voice.

[0355] "Generative AI technology" is a technology that analyzes, collects, and organizes relevant information based on users' interest information and sentiment data, and uses it to generate personalized content.

[0356] A "computational device" is a device that performs processing to analyze and organize related information based on acquired interest information and emotion data.

[0357] "User-friendly output" refers to content that prioritizes and presents information in an easy-to-use format based on the user's emotions.

[0358] A "display device" is a device that provides generated content to users and operates through a user-specific application.

[0359] "Feedback" refers to the evaluations and reactions that users give to the content provided, and this information is used to improve the quality of future content creation.

[0360] This information processing system aims to provide personalized content based on the user's interests and emotions, and mainly consists of terminal devices, server devices, and an emotion recognition engine.

[0361] The terminal device is equipped with a dedicated application for user operation. Using this application, users can select categories of interest. This selection information is stored as a data structure within the device, and a sentiment recognition engine analyzes the user's facial expressions and voice in real time within the application to obtain user sentiment data. This sentiment data is a crucial element for improving the user experience.

[0362] The server device receives interest information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to collect and analyze information. The server selects and collects content related to the user's interests from the internet and databases. In this process, by prioritizing information based on the user's current emotions using sentiment data, it becomes possible to generate more appropriate content.

[0363] The server generates user-optimized content in the form of video clips, text, and images based on the generated data. This information is then formatted in a user-friendly format and sent to the device.

[0364] Users view content displayed on their devices and provide feedback on that content. This feedback is often expressed as changes in emotion or as an in-app rating. This feedback information is sent back from the device to the server and used as training data for the generative AI model and emotion recognition engine, contributing to improvements in the accuracy of future content generation.

[0365] As a concrete example, consider a situation where a user is seeking information related to "travel" and is also experiencing stress. In this case, the emotion recognition engine detects that the user is under stress and prioritizes providing information about relaxing tourist spots and calming music. In this way, the system provides a more highly personalized user experience.

[0366] An example of a prompt might be: "The user's interest category is 'travel,' and the emotion engine has detected stress. Please generate recommended content about relaxing destinations."

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The user launches a dedicated app on their device and selects a category of interest. This interest information is saved as data in the device's memory. For example, the user can select the category "Cooking." The input is the user's selection, and the output is the selected interest information.

[0370] Step 2:

[0371] The device uses an emotion recognition engine built into the app to sense the user's facial expressions and voice and acquire emotion data in real time. This uses the device's camera and microphone. The input is the user's facial expressions and voice information, and the output is emotion data based on that. This emotion data forms the basis for more precisely customizing the user experience.

[0372] Step 3:

[0373] The device integrates stored interest information and acquired sentiment data, constructing them as data packets. These are then sent to a server via the internet. The input is interest information and sentiment data, and the output is the transmitted data packets. Specifically, the data packets are sent using the HTTP or HTTPS protocol.

[0374] Step 4:

[0375] The server analyzes data packets received from the terminal and uses a generative AI model to collect information related to the user's interests. It also takes sentiment data into consideration to determine the priority of the information. The input is data packets from the terminal, and the output is the prioritized information. Specifically, the generative AI model uses natural language processing techniques to generate prompt sentences and searches for information based on them.

[0376] Step 5:

[0377] The server generates user-optimized content and formats it into a user-friendly format. For example, it creates content in formats such as video, text, and images. The input is prioritized information, and the output is formatted content. Specifically, it can use an API to call video editing software and generate video clips.

[0378] Step 6:

[0379] The server sends the generated content to the device. The device notifies the user of this content and displays it within the app. The input is the content data from the server, and the output is the screen information displayed to the user. A push notification system is used for this specific operation.

[0380] Step 7:

[0381] The user views the presented content, and the emotion recognition engine measures their reaction again. Users can also manually enter their evaluation through the in-app feedback option. The input is the user's visual and auditory reactions, and the output is feedback information based on those reactions.

[0382] Step 8:

[0383] The device sends the collected feedback to the server. The server uses this as training material for the generative AI model and emotion recognition engine, and utilizes it to improve system performance. The input is feedback information from the device, and the output is information stored as training data. Specifically, the model is updated using a machine learning algorithm.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0386] While the massive supply of information in modern society offers convenience to users, it also presents the problem of causing stress due to the overwhelming amount of information received. Therefore, in order for users to lead more comfortable and satisfying lives, information needs to be provided in a way that suits not only individual interests and concerns but also their emotional states. Furthermore, traditional information distribution services have suffered from the problem of providing users with a simplistic experience because they do not consider their emotions.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes terminal means for transmitting information about the user's interests as data packets, means for analyzing the user's interests based on the received data packets, collecting and prioritizing relevant information, and an emotion engine for recognizing the user's emotional state and adjusting the set of information provided according to that emotional state. This enables the provision of more personalized and stress-reducing information tailored to the user's interests and emotional state.

[0389] "Information about user interests" refers to information related to categories and topics that users are interested in.

[0390] A "data packet" is a unit of data used when sending and receiving information over a communication network.

[0391] A "terminal device" is a device used by a user to input or receive information.

[0392] A "server" is a computing device used to process data, collect and analyze information, and transmit it.

[0393] An "information collection" is a collection of content provided to individual users.

[0394] A "user-friendly format" is a display format designed to be intuitive and easy for users to use.

[0395] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0396] "Feedback" refers to the actions or information that users provide in response to or evaluate the content they have been given.

[0397] A "generative AI model" is a system that uses artificial intelligence to generate appropriate information and ideas.

[0398] To realize this invention, the system has a server at its core equipped with a generative AI model and an emotion engine. The server receives information about the user's interests as data packets from the terminal. The terminal functions as a smartphone or tablet and can be used by the user after installing a dedicated application in advance. The terminal has an emotion engine that analyzes the user's facial expressions and voice in real time, thereby recognizing the user's emotional state.

[0399] The server analyzes and processes received data on interests and emotions, and generates relevant information sets in a user-friendly format. Using a generative AI model during the analysis process, the server effectively collects and prioritizes information that aligns with the user's interests. Furthermore, by tailoring the information according to the user's emotions, it can deliver an experience optimized for each individual user.

[0400] For example, when a user is in a cheerful mood, the emotion engine recognizes this state and prioritizes recommending highly entertaining content. Conversely, when the user wants to calm down, it recommends relaxing content. An example of a prompt message generated by the generating AI model would be: "The user is interested in comedy movies, and their current emotional state is to relax. Please recommend relaxing movies."

[0401] Ultimately, the generated content is sent from the server to the device and provided to the user. The user reviews the content and provides feedback if any. This feedback is collected again on the server side and used to improve the accuracy of future content generation. This cycle is designed to progressively improve the user experience.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user launches a dedicated application on their device and selects a category of interest. The user's selected interest information is entered into the device and sent to the server as a data packet. At this time, the device analyzes the user's facial expressions and voice using an emotion engine, and similarly includes the emotional state in the data packet before sending it.

[0405] Step 2:

[0406] The server analyzes data packets received from the terminal. Using interest information and emotion data as input, the server utilizes a generative AI model to collect information based on the user's interests. Data prioritization is performed so that the collected information matches the user's current emotional state. The server outputs a set of information prioritized according to interests and emotions.

[0407] Step 3:

[0408] The server uses this prioritized information set to generate content in a user-friendly format. During this process, the generating AI model generates prompts as needed, gathering and refining information. Specifically, it might generate a prompt such as, "The user's interest is in comedy films, and their current emotional state is to relax. Please recommend relaxing films." The output is a formatted information set in a specific format.

[0409] Step 4:

[0410] The server sends the generated information set to the device. The device notifies the user of the received content and displays it within the app. The user can review the content and, if they have any feedback, enter it into the device. The device then sends that feedback back to the server as a data packet.

[0411] Step 5:

[0412] The server receives feedback data sent by users and uses it as training data for future content generation. Specifically, it analyzes the feedback information using a generative AI model and integrates it as foundational data to improve accuracy in future information collection and distribution. Through this step, continuous service improvement is achieved.

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0424] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0425] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] The information processing system of the present invention aims to support users in efficiently collecting information and consists of a terminal device and a server device.

[0430] First, the user launches a dedicated app on their device and selects categories of information they are interested in. This saves their interest information to their device. This information reflects the user's preferences and interests and will later be used to generate content.

[0431] The terminal forms the user's saved interest information into data packets and sends them to the server device. Based on the received data packets, the server uses a generative AI model to collect relevant information based on the user's interests. The AI ​​model selects the most relevant information from a large number of sources and prioritizes it using a pre-configured algorithm.

[0432] Next, the server generates user-specific content based on the prioritized information. This content is delivered to the user in the form of news summaries, video clips, recommendation lists, etc., and is formatted in a user-friendly way.

[0433] The generated content is sent from the server to the device, and the device notifies the user, allowing it to be displayed within the app. Users can easily view and interact with this content and provide feedback on the information provided.

[0434] Finally, the terminal device collects user feedback and sends it to the server as data to be used in future content generation. This integration enables the system to continuously provide optimal information tailored to the user's preferences.

[0435] For example, if a user is interested in "travel" and "gourmet food," the server can collect the latest tourist information and restaurant ratings related to these interests and prioritize providing the user with the most relevant information. In this way, the present invention provides an effective means to streamline information acquisition for users and reduce stress.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user launches a dedicated app on their device and selects information categories that interest them (e.g., travel, food, technology). This selection information is saved on the device as a user profile.

[0439] Step 2:

[0440] The terminal packages the user's selected interest information into data packets and transmits them to the server device via the communication network. This data includes the user's interest categories and past behavioral history.

[0441] Step 3:

[0442] The server analyzes the received data packets to identify the user's interests. A generative AI model is then activated to collect information related to the user's interests from multiple sources. During this process, the relevance of the collected information is evaluated and prioritized.

[0443] Step 4:

[0444] The server uses prioritized information to generate user-specific content. This content is formatted into user-friendly summary formats such as text, video clips, and news summaries.

[0445] Step 5:

[0446] The server generates content and sends it to the terminal device, which then notifies the user and displays the content. The user can review the provided content and provide feedback as needed.

[0447] Step 6:

[0448] The device collects feedback from users and sends it to the server device. The server uses this feedback as training data for its AI generation model to improve the accuracy of future content generation.

[0449] (Example 1)

[0450] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] In modern society, users face information overload and find it difficult to quickly and accurately obtain the information they need. Furthermore, with the wide variety of information available, there is a growing need to efficiently provide information tailored to users' interests. Conventional systems often fail to adequately provide information that matches individual user preferences, which can impair the user experience.

[0452] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0453] In this invention, the server includes means for analyzing the user's areas of interest, acquiring and ranking relevant information, constructing user-specific information, converting it into a user-friendly format, and transmitting the generated information to a device and presenting it to the user. This makes it possible to efficiently provide information tailored to the user's preferences.

[0454] A "user" is an entity that receives information and content, and is a person who utilizes an information processing system.

[0455] "Areas of interest" refers to the categories or themes of information that a user is interested in.

[0456] A "data format" is a means of representing information in a specific structure, and is the format used during communication.

[0457] "Device" refers to equipment or systems used for processing, transmitting, or receiving information.

[0458] A "processing unit" refers to a device that has the ability to process and analyze received information.

[0459] "Acquisition and ranking" refers to the process of gathering relevant information and organizing it based on its relevance and importance.

[0460] "User-specific information" refers to information that has been specially tailored based on the user's interests and preferences.

[0461] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate information and create content tailored to the user's interests.

[0462] "Learning information" refers to the data and knowledge used to improve generative AI models.

[0463] "Software" refers to the programs and applications necessary to operate a device.

[0464] This invention presents a form of information processing system. This system aims to effectively acquire and provide information tailored to the user's interests. The system primarily consists of three elements: a terminal, a server, and a generative AI model, all of which work together.

[0465] The terminal acts as the interface with the user and uses dedicated software to collect information about the user's interests. The user selects areas of interest through the application, and this information is stored on the terminal. This selected information is then packetized as data and sent to a server for further processing.

[0466] The server has a central information processing function and uses a generative AI model to retrieve and rank relevant information based on data received from terminals. This model searches for useful content from a large amount of data sources, analyzes that information, and constructs information in a form optimized for the user's preferences.

[0467] The acquired information is further formatted on the server into a user-friendly format and sent to the terminal as generated, dedicated content. This allows the user to smoothly view and interact with the information within the application. Specific examples of prompts generated using the AI ​​model include "Please retrieve the latest travel information" and "Please return restaurant ratings based on the user's culinary interests."

[0468] This system allows users to quickly and efficiently obtain the information they need and to access useful information tailored to their interests at any time. This reduces the stress users experience from information overload and provides a better information experience.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user launches a dedicated app on their device and selects their areas of interest. The input is the user's selected areas of interest, and the output is the temporary storage of this information on the device. Specifically, the user selects multiple categories of interest on the app screen, and this information is recorded on the device in data format.

[0472] Step 2:

[0473] The terminal converts user interest information into data packets in formats such as JSON. The input is selected interest area information, and the output is a data packet to be sent to the server. When converting to JSON format, the user ID and selected category information are included in the packet and securely sent to the server over the network.

[0474] Step 3:

[0475] The server analyzes data packets received from the terminal. The input is the received data packets, and the output is a profile of user interest information based on the analysis results. To build the profile, the server matches the user's selection information against a database and converts it into a format that can be used by the generated AI model.

[0476] Step 4:

[0477] The server uses a generative AI model to collect and prioritize relevant information based on the user's interests. The input is a profile of the user's interests, and the output is a prioritized list of relevant information. The server retrieves data from multiple sources and uses algorithms to select the most relevant information.

[0478] Step 5:

[0479] The server generates and formats user-specific content based on prioritized information. The input is a prioritized list, and the output is user-optimized content. The generated content is formatted into user-friendly formats such as news summaries and video clips.

[0480] Step 6:

[0481] The server sends the generated content to the terminal. The input is the formatted content, and the output is the completion of the transmission to the terminal. The terminal notifies the user of the received content and displays it within the application.

[0482] Step 7:

[0483] Users view the provided content and provide feedback. The input is the user's feedback information, and the output is the feedback recorded on the device. The device collects this feedback and sends it to the server to help improve the system in the future.

[0484] Step 8:

[0485] The server analyzes the feedback received from the terminal and updates it as training information for the generative AI model. The input is user feedback data, and the output is an improved generative AI model. This optimizes the next information delivery to better match the user's preferences.

[0486] (Application Example 1)

[0487] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] In today's information society, it is difficult for users to efficiently and quickly obtain the information they need from the vast amount of data available. Furthermore, building a system that appropriately provides optimal content based on user interests is technically complex. Therefore, there is a need for methods that accurately understand user interests and efficiently provide content that meets those interests.

[0489] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0490] In this invention, the server includes means including a computer device that selects information relating to the user's interests and transmits it as a group of information data; means including a processing device that analyzes the user's interests based on the received group of information data, collects and ranks relevant information; and means including a processing device that transmits the generated media content to the computer device and notifies the user. This makes it possible to efficiently provide optimal content tailored to the user and to quickly obtain the information the user needs.

[0491] "User interests" refer to specific themes, topics, or fields that individual users are interested in.

[0492] "Information data set" refers to a collection of data related to user interests that is transmitted through a computer device.

[0493] A "computer device" is a device used to exchange data with users and display processing results.

[0494] A "processing device" refers to a device that has the function of analyzing received data and generating and providing necessary information.

[0495] "Media content" refers to the specific format and content of information provided to users, including video clips, news articles, recommendation lists, etc.

[0496] "Prioritization" refers to the process of assigning priority levels to collected information and rearranging it accordingly.

[0497] A "generative AI model" refers to an artificial intelligence-based algorithm used to generate content based on user interests.

[0498] The system for carrying out this invention consists of a computer device and a server device. The computer device provides information related to the user's interests as options, generates the information selected by the user as a data set, and transmits it to the server device. Based on this received data set, the server device analyzes the user's interests using a generative AI model. In this analysis, information is collected and ranked based on its relevance.

[0499] The server device generates media content suitable for the user based on information provided by the computer device and formats it into a user-friendly format. The formatted media content is transmitted to the computer device and notified to the user.

[0500] Users can provide their ratings and opinions while viewing the provided media content. The computer collects this feedback and sends it to the server. This feedback is used to improve the accuracy of future content generation.

[0501] As a concrete example, this system can be implemented as a smartphone application. For instance, if a user interested in travel selects "tourist destinations" and "gourmet food," the server uses this information to utilize a generative AI model to collect information on recent tourist spots and highly-rated restaurants. This allows the content most likely to interest the user to be displayed preferentially.

[0502] The hardware and software used include terminal devices such as smartphones and tablets, and high-performance cloud servers. The software includes machine learning libraries such as TensorFlow and PyTorch to run generative AI models, and frameworks such as Flask and Django to manage communication with the server.

[0503] An example of a prompt for a generative AI model might be: "User's interest categories: Health, Fitness. What information should be collected to provide the latest research findings and exercise videos?"

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The terminal displays an interface for the user to select their areas of interest. The user chooses a category that corresponds to their interests from the displayed options. This input information becomes the basic data for subsequent processing.

[0507] Step 2:

[0508] The terminal formats the selected user's interests as a data set and sends it to the server. The transmitted data set includes metadata such as category ID, timestamp, and user ID.

[0509] Step 3:

[0510] The server takes the received data set as input and analyzes it. Using a generative AI model, it collects relevant information based on the analyzed user interest data. This information is then ranked in order of relevance by an algorithm.

[0511] Step 4:

[0512] The server generates media content optimized for the user based on ranked information. This generated content includes news articles, video clips, and recommended item lists that may be of interest. This data is then formatted into a user-friendly format.

[0513] Step 5:

[0514] The server sends the formatted media content to the terminal and notifies the user. The terminal receives this and displays it on the user interface.

[0515] Step 6:

[0516] Users review the displayed media content and provide their ratings and feedback. The device collects this feedback and sends it to the server as data packets.

[0517] Step 7:

[0518] The server uses the received feedback data as training data for its AI model, adjusting the algorithm for future content generation. This feedback loop enables the provision of content that more accurately reflects user interests.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] The information processing system of the present invention aims to provide personalized content based on the user's interests and emotions. The system consists of a terminal device, a server device, and an emotion engine.

[0521] First, the user launches a dedicated app on their device and selects categories of interest. This saves the user's interest information to the device. Furthermore, while using the app, an emotion engine recognizes the user's emotions in real time through facial expressions, voice, and other data. This emotion data is used to improve the user experience.

[0522] The terminal constructs acquired interest and sentiment data as data packets and sends them to the server device. The server receives this data and uses a generative AI model to collect and analyze relevant information based on the user's interests. Sentiment data is also considered when prioritizing the information so that it matches the user's current emotions.

[0523] Next, the server generates user-specific content based on the prioritized data. This content is optimized for the user's interests and emotions and is delivered in formats such as video clips, text, and images. The server formats this content and sends it to the terminal device.

[0524] The device notifies the user of received content and displays it within the app. The user can view this content and, if necessary, provide feedback through emotional changes constantly measured by the emotion engine and manual evaluations.

[0525] Finally, the feedback collected by the device is sent to a server device, which uses that feedback as training data for the generating AI model and emotion engine. This allows the system to continuously improve its ability to provide users with more appropriate content.

[0526] As a concrete example, let's assume a user is searching for information about "travel" but is feeling stressed. The emotion engine can recognize this stress and prioritize providing information about relaxing tourist spots or calming music. This makes the information provided to the user more personalized, resulting in a more satisfying user experience.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user launches a dedicated app on their device and selects information categories of interest. This selection information is saved on the device as a profile.

[0530] Step 2:

[0531] The device activates an emotion engine and recognizes emotions in real time through the user's facial expressions, voice, touch intensity, and other factors. The recognized emotion data is added to the user's profile.

[0532] Step 3:

[0533] The device packages user interest information and emotional data as data packets and sends them to the server device. This data reflects the user's current state and is necessary for generating personalized content.

[0534] Step 4:

[0535] The server analyzes the received data packets and activates a generative AI model to collect relevant information aligned with the user's interests. During this process, emotional data is also considered, with priority given to information that resonates with the user's emotions.

[0536] Step 5:

[0537] Based on the data collected by the server, personalized content is generated that responds to the user's emotions. For example, if the user wants to relax, content including related calming videos and music will be generated.

[0538] Step 6:

[0539] The server formats the generated content into a user-friendly format and sends it to the terminal.

[0540] Step 7:

[0541] The device receives the transmitted content, notifies the user, and displays it within the app. The user views the provided content, and the emotion engine continues to monitor their emotional state while they are viewing the content.

[0542] Step 8:

[0543] Users provide feedback on the content, including their opinions and feelings. This feedback is used to improve future content creation.

[0544] Step 9:

[0545] The device sends feedback to the server, which then incorporates it into the generated AI model and emotion engine training data, thereby improving the accuracy of future content delivery.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] Traditional information delivery systems had limitations in providing relevant information based on user interests, and it was difficult to deliver personalized content that took into account the user's real-time emotional state. Furthermore, there was a lack of mechanisms to efficiently utilize user feedback and improve the quality of the content provided, resulting in insufficient improvement in the user experience.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes means for analyzing the user's facial expressions and voice to acquire emotional data, means for analyzing the user's interests and collecting and organizing relevant information using generative AI technology based on the acquired interest information and emotional data, and means for prioritizing information according to the user's emotions and generating user-friendly output. This makes it possible to provide more personalized content that simultaneously considers the user's interests and emotional state.

[0551] An "information processing device" is a device that selects information of interest to the user, constructs it as a data structure, and sends it to a server.

[0552] "Emotional data" refers to data that indicates a user's emotional state, obtained in real time by analyzing the user's facial expressions and voice.

[0553] "Generative AI technology" is a technology that analyzes, collects, and organizes relevant information based on users' interest information and sentiment data, and uses it to generate personalized content.

[0554] A "computational device" is a device that performs processing to analyze and organize related information based on acquired interest information and emotion data.

[0555] "User-friendly output" refers to content that prioritizes and presents information in an easy-to-use format based on the user's emotions.

[0556] A "display device" is a device that provides generated content to users and operates through a user-specific application.

[0557] "Feedback" refers to the evaluations and reactions that users give to the content provided, and this information is used to improve the quality of future content creation.

[0558] This information processing system aims to provide personalized content based on the user's interests and emotions, and mainly consists of terminal devices, server devices, and an emotion recognition engine.

[0559] The terminal device is equipped with a dedicated application for user operation. Using this application, users can select categories of interest. This selection information is stored as a data structure within the device, and a sentiment recognition engine analyzes the user's facial expressions and voice in real time within the application to obtain user sentiment data. This sentiment data is a crucial element for improving the user experience.

[0560] The server device receives interest information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to collect and analyze information. The server selects and collects content related to the user's interests from the internet and databases. In this process, by prioritizing information based on the user's current emotions using sentiment data, it becomes possible to generate more appropriate content.

[0561] The server generates user-optimized content in the form of video clips, text, and images based on the generated data. This information is then formatted in a user-friendly format and sent to the device.

[0562] Users view content displayed on their devices and provide feedback on that content. This feedback is often expressed as changes in emotion or as an in-app rating. This feedback information is sent back from the device to the server and used as training data for the generative AI model and emotion recognition engine, contributing to improvements in the accuracy of future content generation.

[0563] As a concrete example, consider a situation where a user is seeking information related to "travel" and is also experiencing stress. In this case, the emotion recognition engine detects that the user is under stress and prioritizes providing information about relaxing tourist spots and calming music. In this way, the system provides a more highly personalized user experience.

[0564] An example of a prompt might be: "The user's interest category is 'travel,' and the emotion engine has detected stress. Please generate recommended content about relaxing destinations."

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user launches a dedicated app on their device and selects a category of interest. This interest information is saved as data in the device's memory. For example, the user can select the category "Cooking." The input is the user's selection, and the output is the selected interest information.

[0568] Step 2:

[0569] The device uses an emotion recognition engine built into the app to sense the user's facial expressions and voice and acquire emotion data in real time. This uses the device's camera and microphone. The input is the user's facial expressions and voice information, and the output is emotion data based on that. This emotion data forms the basis for more precisely customizing the user experience.

[0570] Step 3:

[0571] The device integrates stored interest information and acquired sentiment data, constructing them as data packets. These are then sent to a server via the internet. The input is interest information and sentiment data, and the output is the transmitted data packets. Specifically, the data packets are sent using the HTTP or HTTPS protocol.

[0572] Step 4:

[0573] The server analyzes data packets received from the terminal and uses a generative AI model to collect information related to the user's interests. It also takes sentiment data into consideration to determine the priority of the information. The input is data packets from the terminal, and the output is the prioritized information. Specifically, the generative AI model uses natural language processing techniques to generate prompt sentences and searches for information based on them.

[0574] Step 5:

[0575] The server generates user-optimized content and formats it into a user-friendly format. For example, it creates content in formats such as video, text, and images. The input is prioritized information, and the output is formatted content. Specifically, it can use an API to call video editing software and generate video clips.

[0576] Step 6:

[0577] The server sends the generated content to the device. The device notifies the user of this content and displays it within the app. The input is the content data from the server, and the output is the screen information displayed to the user. A push notification system is used for this specific operation.

[0578] Step 7:

[0579] The user views the presented content, and the emotion recognition engine measures their reaction again. Users can also manually enter their evaluation through the in-app feedback option. The input is the user's visual and auditory reactions, and the output is feedback information based on those reactions.

[0580] Step 8:

[0581] The device sends the collected feedback to the server. The server uses this as training material for the generative AI model and emotion recognition engine, and utilizes it to improve system performance. The input is feedback information from the device, and the output is information stored as training data. Specifically, the model is updated using a machine learning algorithm.

[0582] (Application Example 2)

[0583] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] While the massive supply of information in modern society offers convenience to users, it also presents the problem of causing stress due to the overwhelming amount of information received. Therefore, in order for users to lead more comfortable and satisfying lives, information needs to be provided in a way that suits not only individual interests and concerns but also their emotional states. Furthermore, traditional information distribution services have suffered from the problem of providing users with a simplistic experience because they do not consider their emotions.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0586] In this invention, the server includes terminal means for transmitting information about the user's interests as data packets, means for analyzing the user's interests based on the received data packets, collecting and prioritizing relevant information, and an emotion engine for recognizing the user's emotional state and adjusting the set of information provided according to that emotional state. This enables the provision of more personalized and stress-reducing information tailored to the user's interests and emotional state.

[0587] "Information about user interests" refers to information related to categories and topics that users are interested in.

[0588] A "data packet" is a unit of data used when sending and receiving information over a communication network.

[0589] A "terminal device" is a device used by a user to input or receive information.

[0590] A "server" is a computing device used to process data, collect and analyze information, and transmit it.

[0591] An "information collection" is a collection of content provided to individual users.

[0592] A "user-friendly format" is a display format designed to be intuitive and easy for users to use.

[0593] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0594] "Feedback" refers to the actions or information that users provide in response to or evaluate the content they have been given.

[0595] A "generative AI model" is a system that uses artificial intelligence to generate appropriate information and ideas.

[0596] To realize this invention, the system has a server at its core equipped with a generative AI model and an emotion engine. The server receives information about the user's interests as data packets from the terminal. The terminal functions as a smartphone or tablet and can be used by the user after installing a dedicated application in advance. The terminal has an emotion engine that analyzes the user's facial expressions and voice in real time, thereby recognizing the user's emotional state.

[0597] The server analyzes and processes received data on interests and emotions, and generates relevant information sets in a user-friendly format. Using a generative AI model during the analysis process, the server effectively collects and prioritizes information that aligns with the user's interests. Furthermore, by tailoring the information according to the user's emotions, it can deliver an experience optimized for each individual user.

[0598] For example, when a user is in a cheerful mood, the emotion engine recognizes this state and prioritizes recommending highly entertaining content. Conversely, when the user wants to calm down, it recommends relaxing content. An example of a prompt message generated by the generating AI model would be: "The user is interested in comedy movies, and their current emotional state is to relax. Please recommend relaxing movies."

[0599] Ultimately, the generated content is sent from the server to the device and provided to the user. The user reviews the content and provides feedback if any. This feedback is collected again on the server side and used to improve the accuracy of future content generation. This cycle is designed to progressively improve the user experience.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The user launches a dedicated application on their device and selects a category of interest. The user's selected interest information is entered into the device and sent to the server as a data packet. At this time, the device analyzes the user's facial expressions and voice using an emotion engine, and similarly includes the emotional state in the data packet before sending it.

[0603] Step 2:

[0604] The server analyzes data packets received from the terminal. Using interest information and emotion data as input, the server utilizes a generative AI model to collect information based on the user's interests. Data prioritization is performed so that the collected information matches the user's current emotional state. The server outputs a set of information prioritized according to interests and emotions.

[0605] Step 3:

[0606] The server uses this prioritized information set to generate content in a user-friendly format. During this process, the generating AI model generates prompts as needed, gathering and refining information. Specifically, it might generate a prompt such as, "The user's interest is in comedy films, and their current emotional state is to relax. Please recommend relaxing films." The output is a formatted information set in a specific format.

[0607] Step 4:

[0608] The server sends the generated information set to the device. The device notifies the user of the received content and displays it within the app. The user can review the content and, if they have any feedback, enter it into the device. The device then sends that feedback back to the server as a data packet.

[0609] Step 5:

[0610] The server receives feedback data sent by users and uses it as training data for future content generation. Specifically, it analyzes the feedback information using a generative AI model and integrates it as foundational data to improve accuracy in future information collection and distribution. Through this step, continuous service improvement is achieved.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] The information processing system of the present invention aims to support users in efficiently collecting information and consists of a terminal device and a server device.

[0629] First, the user launches a dedicated app on their device and selects categories of information they are interested in. This saves their interest information to their device. This information reflects the user's preferences and interests and will later be used to generate content.

[0630] The terminal forms the user's saved interest information into data packets and sends them to the server device. Based on the received data packets, the server uses a generative AI model to collect relevant information based on the user's interests. The AI ​​model selects the most relevant information from a large number of sources and prioritizes it using a pre-configured algorithm.

[0631] Next, the server generates user-specific content based on the prioritized information. This content is delivered to the user in the form of news summaries, video clips, recommendation lists, etc., and is formatted in a user-friendly way.

[0632] The generated content is sent from the server to the device, and the device notifies the user, allowing it to be displayed within the app. Users can easily view and interact with this content and provide feedback on the information provided.

[0633] Finally, the terminal device collects user feedback and sends it to the server as data to be used in future content generation. This integration enables the system to continuously provide optimal information tailored to the user's preferences.

[0634] For example, if a user is interested in "travel" and "gourmet food," the server can collect the latest tourist information and restaurant ratings related to these interests and prioritize providing the user with the most relevant information. In this way, the present invention provides an effective means to streamline information acquisition for users and reduce stress.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user launches a dedicated app on their device and selects information categories that interest them (e.g., travel, food, technology). This selection information is saved on the device as a user profile.

[0638] Step 2:

[0639] The terminal packages the user's selected interest information into data packets and transmits them to the server device via the communication network. This data includes the user's interest categories and past behavioral history.

[0640] Step 3:

[0641] The server analyzes the received data packets to identify the user's interests. A generative AI model is then activated to collect information related to the user's interests from multiple sources. During this process, the relevance of the collected information is evaluated and prioritized.

[0642] Step 4:

[0643] The server uses prioritized information to generate user-specific content. This content is formatted into user-friendly summary formats such as text, video clips, and news summaries.

[0644] Step 5:

[0645] The server generates content and sends it to the terminal device, which then notifies the user and displays the content. The user can review the provided content and provide feedback as needed.

[0646] Step 6:

[0647] The device collects feedback from users and sends it to the server device. The server uses this feedback as training data for its AI generation model to improve the accuracy of future content generation.

[0648] (Example 1)

[0649] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] In modern society, users face information overload and find it difficult to quickly and accurately obtain the information they need. Furthermore, with the wide variety of information available, there is a growing need to efficiently provide information tailored to users' interests. Conventional systems often fail to adequately provide information that matches individual user preferences, which can impair the user experience.

[0651] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0652] In this invention, the server includes means for analyzing the user's areas of interest, acquiring and ranking relevant information, constructing user-specific information, converting it into a user-friendly format, and transmitting the generated information to a device and presenting it to the user. This makes it possible to efficiently provide information tailored to the user's preferences.

[0653] A "user" is an entity that receives information and content, and is a person who utilizes an information processing system.

[0654] "Areas of interest" refers to the categories or themes of information that a user is interested in.

[0655] A "data format" is a means of representing information in a specific structure, and is the format used during communication.

[0656] "Device" refers to equipment or systems used for processing, transmitting, or receiving information.

[0657] A "processing unit" refers to a device that has the ability to process and analyze received information.

[0658] "Acquisition and ranking" refers to the process of gathering relevant information and organizing it based on its relevance and importance.

[0659] "User-specific information" refers to information that has been specially tailored based on the user's interests and preferences.

[0660] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate information and create content tailored to the user's interests.

[0661] "Learning information" refers to the data and knowledge used to improve generative AI models.

[0662] "Software" refers to the programs and applications necessary to operate a device.

[0663] This invention presents a form of information processing system. This system aims to effectively acquire and provide information tailored to the user's interests. The system primarily consists of three elements: a terminal, a server, and a generative AI model, all of which work together.

[0664] The terminal acts as the interface with the user and uses dedicated software to collect information about the user's interests. The user selects areas of interest through the application, and this information is stored on the terminal. This selected information is then packetized as data and sent to a server for further processing.

[0665] The server has a central information processing function and uses a generative AI model to retrieve and rank relevant information based on data received from terminals. This model searches for useful content from a large amount of data sources, analyzes that information, and constructs information in a form optimized for the user's preferences.

[0666] The acquired information is further formatted on the server into a user-friendly format and sent to the terminal as generated, dedicated content. This allows the user to smoothly view and interact with the information within the application. Specific examples of prompts generated using the AI ​​model include "Please retrieve the latest travel information" and "Please return restaurant ratings based on the user's culinary interests."

[0667] This system allows users to quickly and efficiently obtain the information they need and to access useful information tailored to their interests at any time. This reduces the stress users experience from information overload and provides a better information experience.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The user launches a dedicated app on their device and selects their areas of interest. The input is the user's selected areas of interest, and the output is the temporary storage of this information on the device. Specifically, the user selects multiple categories of interest on the app screen, and this information is recorded on the device in data format.

[0671] Step 2:

[0672] The terminal converts user interest information into data packets in formats such as JSON. The input is selected interest area information, and the output is a data packet to be sent to the server. When converting to JSON format, the user ID and selected category information are included in the packet and securely sent to the server over the network.

[0673] Step 3:

[0674] The server analyzes data packets received from the terminal. The input is the received data packets, and the output is a profile of user interest information based on the analysis results. To build the profile, the server matches the user's selection information against a database and converts it into a format that can be used by the generated AI model.

[0675] Step 4:

[0676] The server uses a generative AI model to collect and prioritize relevant information based on the user's interests. The input is a profile of the user's interests, and the output is a prioritized list of relevant information. The server retrieves data from multiple sources and uses algorithms to select the most relevant information.

[0677] Step 5:

[0678] The server generates and formats user-specific content based on prioritized information. The input is a prioritized list, and the output is user-optimized content. The generated content is formatted into user-friendly formats such as news summaries and video clips.

[0679] Step 6:

[0680] The server sends the generated content to the terminal. The input is the formatted content, and the output is the completion of the transmission to the terminal. The terminal notifies the user of the received content and displays it within the application.

[0681] Step 7:

[0682] Users view the provided content and provide feedback. The input is the user's feedback information, and the output is the feedback recorded on the device. The device collects this feedback and sends it to the server to help improve the system in the future.

[0683] Step 8:

[0684] The server analyzes the feedback received from the terminal and updates it as training information for the generative AI model. The input is user feedback data, and the output is an improved generative AI model. This optimizes the next information delivery to better match the user's preferences.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In today's information society, it is difficult for users to efficiently and quickly obtain the information they need from the vast amount of data available. Furthermore, building a system that appropriately provides optimal content based on user interests is technically complex. Therefore, there is a need for methods that accurately understand user interests and efficiently provide content that meets those interests.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means including a computer device that selects information relating to the user's interests and transmits it as a group of information data; means including a processing device that analyzes the user's interests based on the received group of information data, collects and ranks relevant information; and means including a processing device that transmits the generated media content to the computer device and notifies the user. This makes it possible to efficiently provide optimal content tailored to the user and to quickly obtain the information the user needs.

[0690] "User interests" refer to specific themes, topics, or fields that individual users are interested in.

[0691] "Information data set" refers to a collection of data related to user interests that is transmitted through a computer device.

[0692] A "computer device" is a device used to exchange data with users and display processing results.

[0693] A "processing device" refers to a device that has the function of analyzing received data and generating and providing necessary information.

[0694] "Media content" refers to the specific format and content of information provided to users, including video clips, news articles, recommendation lists, etc.

[0695] "Prioritization" refers to the process of assigning priority levels to collected information and rearranging it accordingly.

[0696] A "generative AI model" refers to an artificial intelligence-based algorithm used to generate content based on user interests.

[0697] The system for carrying out this invention consists of a computer device and a server device. The computer device provides information related to the user's interests as options, generates the information selected by the user as a data set, and transmits it to the server device. Based on this received data set, the server device analyzes the user's interests using a generative AI model. In this analysis, information is collected and ranked based on its relevance.

[0698] The server device generates media content suitable for the user based on information provided by the computer device and formats it into a user-friendly format. The formatted media content is transmitted to the computer device and notified to the user.

[0699] Users can provide their ratings and opinions while viewing the provided media content. The computer collects this feedback and sends it to the server. This feedback is used to improve the accuracy of future content generation.

[0700] As a concrete example, this system can be implemented as a smartphone application. For instance, if a user interested in travel selects "tourist destinations" and "gourmet food," the server uses this information to utilize a generative AI model to collect information on recent tourist spots and highly-rated restaurants. This allows the content most likely to interest the user to be displayed preferentially.

[0701] The hardware and software used include terminal devices such as smartphones and tablets, and high-performance cloud servers. The software includes machine learning libraries such as TensorFlow and PyTorch to run generative AI models, and frameworks such as Flask and Django to manage communication with the server.

[0702] An example of a prompt for a generative AI model might be: "User's interest categories: Health, Fitness. What information should be collected to provide the latest research findings and exercise videos?"

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The terminal displays an interface for the user to select their areas of interest. The user chooses a category that corresponds to their interests from the displayed options. This input information becomes the basic data for subsequent processing.

[0706] Step 2:

[0707] The terminal formats the selected user's interests as a data set and sends it to the server. The transmitted data set includes metadata such as category ID, timestamp, and user ID.

[0708] Step 3:

[0709] The server takes the received data set as input and analyzes it. Using a generative AI model, it collects relevant information based on the analyzed user interest data. This information is then ranked in order of relevance by an algorithm.

[0710] Step 4:

[0711] The server generates media content optimized for the user based on ranked information. This generated content includes news articles, video clips, and recommended item lists that may be of interest. This data is then formatted into a user-friendly format.

[0712] Step 5:

[0713] The server sends the formatted media content to the terminal and notifies the user. The terminal receives this and displays it on the user interface.

[0714] Step 6:

[0715] Users review the displayed media content and provide their ratings and feedback. The device collects this feedback and sends it to the server as data packets.

[0716] Step 7:

[0717] The server uses the received feedback data as training data for its AI model, adjusting the algorithm for future content generation. This feedback loop enables the provision of content that more accurately reflects user interests.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] The information processing system of the present invention aims to provide personalized content based on the user's interests and emotions. The system consists of a terminal device, a server device, and an emotion engine.

[0720] First, the user launches a dedicated app on their device and selects categories of interest. This saves the user's interest information to the device. Furthermore, while using the app, an emotion engine recognizes the user's emotions in real time through facial expressions, voice, and other data. This emotion data is used to improve the user experience.

[0721] The terminal constructs acquired interest and sentiment data as data packets and sends them to the server device. The server receives this data and uses a generative AI model to collect and analyze relevant information based on the user's interests. Sentiment data is also considered when prioritizing the information so that it matches the user's current emotions.

[0722] Next, the server generates user-specific content based on the prioritized data. This content is optimized for the user's interests and emotions and is delivered in formats such as video clips, text, and images. The server formats this content and sends it to the terminal device.

[0723] The device notifies the user of received content and displays it within the app. The user can view this content and, if necessary, provide feedback through emotional changes constantly measured by the emotion engine and manual evaluations.

[0724] Finally, the feedback collected by the device is sent to a server device, which uses that feedback as training data for the generating AI model and emotion engine. This allows the system to continuously improve its ability to provide users with more appropriate content.

[0725] As a concrete example, let's assume a user is searching for information about "travel" but is feeling stressed. The emotion engine can recognize this stress and prioritize providing information about relaxing tourist spots or calming music. This makes the information provided to the user more personalized, resulting in a more satisfying user experience.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user launches a dedicated app on their device and selects information categories of interest. This selection information is saved on the device as a profile.

[0729] Step 2:

[0730] The device activates an emotion engine and recognizes emotions in real time through the user's facial expressions, voice, touch intensity, and other factors. The recognized emotion data is added to the user's profile.

[0731] Step 3:

[0732] The device packages user interest information and emotional data as data packets and sends them to the server device. This data reflects the user's current state and is necessary for generating personalized content.

[0733] Step 4:

[0734] The server analyzes the received data packets and activates a generative AI model to collect relevant information aligned with the user's interests. During this process, emotional data is also considered, with priority given to information that resonates with the user's emotions.

[0735] Step 5:

[0736] Based on the data collected by the server, personalized content is generated that responds to the user's emotions. For example, if the user wants to relax, content including related calming videos and music will be generated.

[0737] Step 6:

[0738] The server formats the generated content into a user-friendly format and sends it to the terminal.

[0739] Step 7:

[0740] The device receives the transmitted content, notifies the user, and displays it within the app. The user views the provided content, and the emotion engine continues to monitor their emotional state while they are viewing the content.

[0741] Step 8:

[0742] Users provide feedback on the content, including their opinions and feelings. This feedback is used to improve future content creation.

[0743] Step 9:

[0744] The device sends feedback to the server, which then incorporates it into the generated AI model and emotion engine training data, thereby improving the accuracy of future content delivery.

[0745] (Example 2)

[0746] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] Traditional information delivery systems had limitations in providing relevant information based on user interests, and it was difficult to deliver personalized content that took into account the user's real-time emotional state. Furthermore, there was a lack of mechanisms to efficiently utilize user feedback and improve the quality of the content provided, resulting in insufficient improvement in the user experience.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0749] In this invention, the server includes means for analyzing the user's facial expressions and voice to acquire emotional data, means for analyzing the user's interests and collecting and organizing relevant information using generative AI technology based on the acquired interest information and emotional data, and means for prioritizing information according to the user's emotions and generating user-friendly output. This makes it possible to provide more personalized content that simultaneously considers the user's interests and emotional state.

[0750] An "information processing device" is a device that selects information of interest to the user, constructs it as a data structure, and sends it to a server.

[0751] "Emotional data" refers to data that indicates a user's emotional state, obtained in real time by analyzing the user's facial expressions and voice.

[0752] "Generative AI technology" is a technology that analyzes, collects, and organizes relevant information based on users' interest information and sentiment data, and uses it to generate personalized content.

[0753] A "computational device" is a device that performs processing to analyze and organize related information based on acquired interest information and emotion data.

[0754] "User-friendly output" refers to content that prioritizes and presents information in an easy-to-use format based on the user's emotions.

[0755] A "display device" is a device that provides generated content to users and operates through a user-specific application.

[0756] "Feedback" refers to the evaluations and reactions that users give to the content provided, and this information is used to improve the quality of future content creation.

[0757] This information processing system aims to provide personalized content based on the user's interests and emotions, and mainly consists of terminal devices, server devices, and an emotion recognition engine.

[0758] The terminal device is equipped with a dedicated application for user operation. Using this application, users can select categories of interest. This selection information is stored as a data structure within the device, and a sentiment recognition engine analyzes the user's facial expressions and voice in real time within the application to obtain user sentiment data. This sentiment data is a crucial element for improving the user experience.

[0759] The server device receives interest information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to collect and analyze information. The server selects and collects content related to the user's interests from the internet and databases. In this process, by prioritizing information based on the user's current emotions using sentiment data, it becomes possible to generate more appropriate content.

[0760] The server generates user-optimized content in the form of video clips, text, and images based on the generated data. This information is then formatted in a user-friendly format and sent to the device.

[0761] Users view content displayed on their devices and provide feedback on that content. This feedback is often expressed as changes in emotion or as an in-app rating. This feedback information is sent back from the device to the server and used as training data for the generative AI model and emotion recognition engine, contributing to improvements in the accuracy of future content generation.

[0762] As a concrete example, consider a situation where a user is seeking information related to "travel" and is also experiencing stress. In this case, the emotion recognition engine detects that the user is under stress and prioritizes providing information about relaxing tourist spots and calming music. In this way, the system provides a more highly personalized user experience.

[0763] An example of a prompt might be: "The user's interest category is 'travel,' and the emotion engine has detected stress. Please generate recommended content about relaxing destinations."

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] The user launches a dedicated app on their device and selects a category of interest. This interest information is saved as data in the device's memory. For example, the user can select the category "Cooking." The input is the user's selection, and the output is the selected interest information.

[0767] Step 2:

[0768] The device uses an emotion recognition engine built into the app to sense the user's facial expressions and voice and acquire emotion data in real time. This uses the device's camera and microphone. The input is the user's facial expressions and voice information, and the output is emotion data based on that. This emotion data forms the basis for more precisely customizing the user experience.

[0769] Step 3:

[0770] The device integrates stored interest information and acquired sentiment data, constructing them as data packets. These are then sent to a server via the internet. The input is interest information and sentiment data, and the output is the transmitted data packets. Specifically, the data packets are sent using the HTTP or HTTPS protocol.

[0771] Step 4:

[0772] The server analyzes data packets received from the terminal and uses a generative AI model to collect information related to the user's interests. It also takes sentiment data into consideration to determine the priority of the information. The input is data packets from the terminal, and the output is the prioritized information. Specifically, the generative AI model uses natural language processing techniques to generate prompt sentences and searches for information based on them.

[0773] Step 5:

[0774] The server generates user-optimized content and formats it into a user-friendly format. For example, it creates content in formats such as video, text, and images. The input is prioritized information, and the output is formatted content. Specifically, it can use an API to call video editing software and generate video clips.

[0775] Step 6:

[0776] The server sends the generated content to the device. The device notifies the user of this content and displays it within the app. The input is the content data from the server, and the output is the screen information displayed to the user. A push notification system is used for this specific operation.

[0777] Step 7:

[0778] The user views the presented content, and the emotion recognition engine measures their reaction again. Users can also manually enter their evaluation through the in-app feedback option. The input is the user's visual and auditory reactions, and the output is feedback information based on those reactions.

[0779] Step 8:

[0780] The device sends the collected feedback to the server. The server uses this as training material for the generative AI model and emotion recognition engine, and utilizes it to improve system performance. The input is feedback information from the device, and the output is information stored as training data. Specifically, the model is updated using a machine learning algorithm.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] While the massive supply of information in modern society offers convenience to users, it also presents the problem of causing stress due to the overwhelming amount of information received. Therefore, in order for users to lead more comfortable and satisfying lives, information needs to be provided in a way that suits not only individual interests and concerns but also their emotional states. Furthermore, traditional information distribution services have suffered from the problem of providing users with a simplistic experience because they do not consider their emotions.

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

[0785] In this invention, the server includes terminal means for transmitting information about the user's interests as data packets, means for analyzing the user's interests based on the received data packets, collecting and prioritizing relevant information, and an emotion engine for recognizing the user's emotional state and adjusting the set of information provided according to that emotional state. This enables the provision of more personalized and stress-reducing information tailored to the user's interests and emotional state.

[0786] "Information about user interests" refers to information related to categories and topics that users are interested in.

[0787] A "data packet" is a unit of data used when sending and receiving information over a communication network.

[0788] A "terminal device" is a device used by a user to input or receive information.

[0789] A "server" is a computing device used to process data, collect and analyze information, and transmit it.

[0790] An "information collection" is a collection of content provided to individual users.

[0791] A "user-friendly format" is a display format designed to be intuitive and easy for users to use.

[0792] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0793] "Feedback" refers to the actions or information that users provide in response to or evaluate the content they have been given.

[0794] A "generative AI model" is a system that uses artificial intelligence to generate appropriate information and ideas.

[0795] To realize this invention, the system has a server at its core equipped with a generative AI model and an emotion engine. The server receives information about the user's interests as data packets from the terminal. The terminal functions as a smartphone or tablet and can be used by the user after installing a dedicated application in advance. The terminal has an emotion engine that analyzes the user's facial expressions and voice in real time, thereby recognizing the user's emotional state.

[0796] The server analyzes and processes received data on interests and emotions, and generates relevant information sets in a user-friendly format. Using a generative AI model during the analysis process, the server effectively collects and prioritizes information that aligns with the user's interests. Furthermore, by tailoring the information according to the user's emotions, it can deliver an experience optimized for each individual user.

[0797] For example, when a user is in a cheerful mood, the emotion engine recognizes this state and prioritizes recommending highly entertaining content. Conversely, when the user wants to calm down, it recommends relaxing content. An example of a prompt message generated by the generating AI model would be: "The user is interested in comedy movies, and their current emotional state is to relax. Please recommend relaxing movies."

[0798] Ultimately, the generated content is sent from the server to the device and provided to the user. The user reviews the content and provides feedback if any. This feedback is collected again on the server side and used to improve the accuracy of future content generation. This cycle is designed to progressively improve the user experience.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The user launches a dedicated application on their device and selects a category of interest. The user's selected interest information is entered into the device and sent to the server as a data packet. At this time, the device analyzes the user's facial expressions and voice using an emotion engine, and similarly includes the emotional state in the data packet before sending it.

[0802] Step 2:

[0803] The server analyzes data packets received from the terminal. Using interest information and emotion data as input, the server utilizes a generative AI model to collect information based on the user's interests. Data prioritization is performed so that the collected information matches the user's current emotional state. The server outputs a set of information prioritized according to interests and emotions.

[0804] Step 3:

[0805] The server uses this prioritized information set to generate content in a user-friendly format. During this process, the generating AI model generates prompts as needed, gathering and refining information. Specifically, it might generate a prompt such as, "The user's interest is in comedy films, and their current emotional state is to relax. Please recommend relaxing films." The output is a formatted information set in a specific format.

[0806] Step 4:

[0807] The server sends the generated information set to the device. The device notifies the user of the received content and displays it within the app. The user can review the content and, if they have any feedback, enter it into the device. The device then sends that feedback back to the server as a data packet.

[0808] Step 5:

[0809] The server receives feedback data sent by users and uses it as training data for future content generation. Specifically, it analyzes the feedback information using a generative AI model and integrates it as foundational data to improve accuracy in future information collection and distribution. Through this step, continuous service improvement is achieved.

[0810] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0811] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0812] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0813] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0814] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0815] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0816] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0817] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0818] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0819] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0820] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0821] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0822] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0823] 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.

[0824] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0825] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0826] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0827] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0828] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0829] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0830] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A terminal device that selects information related to the user's interests and transmits that information as a data packet,

[0834] A server device that analyzes user interests based on received data packets, collects and prioritizes relevant information,

[0835] A server device that generates user-specific content and formats it into a user-friendly format,

[0836] A server device that sends the generated content to a terminal device and notifies the user,

[0837] Terminal and server devices that collect user feedback and use it to improve the accuracy of future content generation,

[0838] An information processing system that includes this.

[0839] (Claim 2)

[0840] The information processing system according to claim 1, which updates the training data of a generated AI model based on the user's selection of interest information.

[0841] (Claim 3)

[0842] The information processing system according to claim 1, wherein the terminal device operates via a user-specific application.

[0843] "Example 1"

[0844] (Claim 1)

[0845] A device that selects information related to the user's areas of interest and transmits that information in data format,

[0846] A computing device that analyzes user interests based on received data, and acquires and ranks relevant information,

[0847] A device that creates user-specific information and converts it into a format that is easy for users to understand,

[0848] A device that transmits the generated information to the device and presents it to the user,

[0849] A device that collects user feedback and contributes to improving the accuracy of future information creation,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which updates the learning information of a generative AI model based on the user's selection of areas of interest.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein the device operates via user-specific software.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] A computer device that selects information related to the user's interests and transmits it as a set of information data,

[0858] A processing device that analyzes user interests based on received information data, collects and ranks related information,

[0859] A processing device that generates media content tailored to the user and converts it into a format that is easy for the user to handle,

[0860] A processing unit that transmits the generated media content to a computer and notifies the user,

[0861] A computer and processing device that collects user feedback and uses it to improve the accuracy of future media content generation,

[0862] A device characterized by providing the latest information and video clips based on the user's selection of interest categories,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, which updates the training data of a generated AI model based on the user's selection of interest information.

[0866] (Claim 3)

[0867] The system according to claim 1, wherein the computer device operates via a user-specific application.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] An information processing device that selects user interest information, constructs that information as a data structure, and transmits it,

[0871] A means of analyzing the user's facial expressions and voice to acquire emotional data,

[0872] A computing device that analyzes user interests and collects and organizes related information using generative AI technology based on acquired interest information and sentiment data.

[0873] A means of prioritizing information according to the user's emotions and generating user-friendly output,

[0874] A means for transmitting the generated content to a display device and notifying the user,

[0875] An information processing device that collects user feedback and uses it as training data to improve the accuracy of future content generation,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which updates the model of the generative AI technology based on user interest information and sentiment data.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein the display device operates via a user-specific application.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] A terminal means that selects information related to the user's interests and transmits that information as a data packet,

[0884] A server means that analyzes user interests based on received data packets, collects and prioritizes relevant information,

[0885] A server mechanism that generates user-specific information sets and formats them into a user-friendly format,

[0886] A server means that transmits the generated information set to a terminal device and notifies the user,

[0887] An emotion engine that recognizes the user's emotional state and adjusts the set of information provided according to that emotional state,

[0888] Terminal and server means for collecting user feedback and using it to improve the accuracy of information generation in the future,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which updates the training data of a generative AI model based on the user's selection of interest information and emotional state.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the terminal means operates via a user-specific application. [Explanation of symbols]

[0894] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computer device that selects information related to the user's interests and transmits it as a set of information data, A processing device that analyzes user interests based on received information data, collects and ranks related information, A processing device that generates media content tailored to the user and converts it into a format that is easy for the user to handle, A processing unit that transmits the generated media content to a computer and notifies the user, A computer and processing device that collects user feedback and uses it to improve the accuracy of future media content generation, A device characterized by providing the latest information and video clips based on the user's selection of interest categories, A system that includes this.

2. The system according to claim 1, which updates the training data of a generated AI model based on the user's selection of interest information.

3. The system according to claim 1, wherein the computer operates via a user-specific application.

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