system

Abstract

We provide the system. [Solution] A means of collecting data accessed by users on a communication network, A method for analyzing user reviews and extracting features that cause discomfort, A means for updating the selection criteria by performing learning based on the extracted features, A means for selecting data based on the aforementioned selection criteria and removing unpleasant information in a visual display device, A means for adjusting the selection criteria according to the user's settings, A means by which the user selects a mode appropriate to the situation and controls the type of information presented based on that selection, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 a modern communication network environment, a large number of information that is not beneficial to individual users or information that gives discomfort is displayed, increasing the burden on users' information selection. This is due to the difficulty of selective information display based on users' interests and situations. Furthermore, due to the lack of flexible information presentation according to the situation, for example, it is difficult to obtain the information necessary during dieting, and it also hinders obtaining information during a trip that should be enjoyable. There is a need for a technology that solves such problems and provides a comfortable information experience for users.

Means for Solving the Problems

[0005] This invention provides a system for appropriately selecting information accessed over a communication network. This system uses evaluation data obtained from users to extract features that cause discomfort and generates optimized selection criteria using machine learning. Terminal devices then select information in real time based on these criteria, removing unnecessary information. Furthermore, the system dynamically adjusts the criteria according to the user's settings, enabling information presentation under specific conditions, thereby prioritizing the provision of only the information the user requests. This reduces the burden on users and creates a comfortable information acquisition environment.

[0006] "User" refers to an individual or group that uses services or information through a communication network.

[0007] A "communication network" is a system connected via physical or wireless lines for sending and receiving information.

[0008] "Information" refers to data that includes various types of content accessible to users, such as text, images, links, and videos.

[0009] "Rating" refers to an indicator that shows the feedback or reaction that users give to specific information.

[0010] "Characteristics" refer to attributes or properties extracted from information, and specifically to elements that cause discomfort.

[0011] "Machine learning" is a technology that allows computers to analyze data and automatically learn specific patterns and rules.

[0012] "Selection criteria" refers to the rules and guidelines for selecting information generated by machine learning.

[0013] A "terminal device" refers to a device that a user can directly operate and use to display information.

[0014] "Situation setting" refers to information about the current state and environment that the user inputs or selects on the terminal device.

[0015] "Information presentation" refers to the act of presenting or notifying the selected information to the user.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiment for Carrying out the Invention

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

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

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

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

[0021] In the following embodiments, the numbered 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 disk (e.g., hard disk), or magnetic tape, etc.

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

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

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0037] This invention is a system that selects the information users receive on a communication network and provides a comfortable information environment according to individual preferences and circumstances. This system consists of a server, terminal devices, and users who utilize them.

[0038] First, the server collects information through the communication network. The collected information includes various types of content that users access on a daily basis. Furthermore, this server has the function of receiving ratings from users and analyzing them to extract characteristics of the information. In particular, it focuses on characteristics that may evoke discomfort and stores them in a database.

[0039] Next, the server uses machine learning techniques to automatically update selection criteria based on the collected feedback and information characteristics. These selection criteria become important rules for distinguishing between information that users want and information they don't. The server periodically reviews and optimizes these criteria.

[0040] The terminal device receives selection criteria transmitted from the server. Based on these received criteria, the terminal filters information retrieved from the network in real time, hiding unnecessary or offensive information. This filtering process is handled on the terminal, designed to allow users to enjoy a comfortable information experience without being aware of it.

[0041] Users can view information and provide feedback through their devices. They can also change their context settings. These settings reflect what kind of information users want to prioritize and when, and include options such as "on a diet" or "traveling." Based on these settings, the selection criteria are dynamically adjusted, allowing information to be presented exceptionally under specific conditions.

[0042] For example, if a user is on a diet, they can change their status setting to "on a diet." The server then uses this setting to provide criteria for hiding sweets-related information on the device. However, sweets information related to facilities the user plans to visit during their trip will be displayed as an exception.

[0043] In this way, the server and terminal work together, creating an environment where users can acquire information more comfortably. This invention aims to reduce the burden on users and provide a comfortable information browsing experience through this method.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects information accessed by users via the communication network. The collected data includes various types of content, such as text, images, and links.

[0047] Step 2:

[0048] The server receives evaluation data from users. The feedback provided by users includes labels such as "unpleasant" or "interesting," and this is recorded in the database.

[0049] Step 3:

[0050] The server analyzes the received evaluation data and extracts features of information that cause discomfort. These features may include specific keywords or image patterns.

[0051] Step 4:

[0052] The server uses machine learning technology to generate and update selection criteria based on extracted features. This enables information selection tailored to the user's preferences and circumstances.

[0053] Step 5:

[0054] The terminal receives the latest selection criteria sent from the server. These criteria will be used in the next information filtering process.

[0055] Step 6:

[0056] Based on criteria received from the server, the device filters out unnecessary or offensive information from the real-time data it receives and hides it.

[0057] Step 7:

[0058] Users contribute to improving the accuracy of selection criteria by viewing the displayed information and providing new feedback.

[0059] Step 8:

[0060] Users can change the status settings as needed. When the status changes, the selection criteria are dynamically adjusted based on those settings, and the displayed information is also updated.

[0061] Through this series of processes, the system is continuously optimized, providing users with a comfortable information environment.

[0062] (Example 1)

[0063] 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."

[0064] In modern communication networks, users have access to a vast amount of information, but this often includes information that is unpleasant or unnecessary to them. Therefore, users often spend a lot of time and effort trying to access the information they want. To solve this problem, a means is needed to select and provide appropriate information according to each user's individual preferences and circumstances.

[0065] 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.

[0066] In this invention, the server includes means for collecting information related to user activities via a communication network and obtaining content from information sources; means for analyzing evaluation information and extracting trends and features of the information using natural language processing technology; and means for updating optimized selection criteria based on the collected evaluation information and feature data using machine learning algorithms. This enables users to enjoy a comfortable information environment tailored to their preferences and circumstances.

[0067] A "communication network" refers to the entire network infrastructure used for sending and receiving information, and is the medium through which data is exchanged between computer devices.

[0068] An "information processing device" is a device that performs analysis and sorting based on received data, and its role is to provide users with appropriate information.

[0069] "Evaluation information" refers to the evaluations and feedback that users provide to specific data or content, and is used to optimize the system's selection criteria.

[0070] "Natural language processing technology" refers to the technology necessary for computers to understand and process the language that humans use on a daily basis, and is used to extract trends and characteristics of information.

[0071] A "machine learning algorithm" is a method for analyzing large amounts of data and automatically learning patterns and rules, and is used to optimize the selection criteria of a system.

[0072] "Selection criteria" are standards set to distinguish between information that is useful to the user and information that is unnecessary or unpleasant, and they serve as the rules by which the system selects information.

[0073] "Optimization" refers to adjusting systems and processes to achieve the greatest effect for a specific purpose, and is done to improve the user experience.

[0074] This system is primarily composed of three elements: a server, a terminal, and a user. In implementing the invention, the server plays a crucial role in collecting information and selecting information based on the user's preferences.

[0075] The server collects content from various sources via a communication network. The collected information is stored in a database and becomes the subject of analysis. The server uses natural language processing technology to extract trends and characteristics of the information, and then uses machine learning algorithms based on the evaluation information to optimize the selection criteria. This process could involve using programming languages ​​such as Python and leveraging machine learning libraries such as TENSORFLOW® and Scikit-learn.

[0076] The terminal filters received information in real time based on selection criteria sent from the server. Only appropriate information is presented to the user on the terminal, while unpleasant or unnecessary information is hidden. This process runs smoothly by utilizing the terminal's processing power.

[0077] Users can utilize the information obtained through their devices and provide feedback as needed. This feedback is sent to the server and used to review the selection criteria for the future. Users can also set statuses such as "on a diet" or "traveling," which dynamically adjusts the information selection criteria. These status settings can be easily changed through the device interface.

[0078] For example, when a user configures their travel settings, the server reflects this information, selects recommended destinations, and sends them to the user's device. Users can specifically utilize this feature by entering a prompt such as, "Please provide recommendations for places I plan to visit on my next trip."

[0079] This invention aims to provide an optimized information environment for users, achieving comfortable information access through a generative AI model.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server collects content from information sources via a communication network. It is given feed information and URLs of publicly available content as input. The server collects this using crawling techniques and stores it in a database. The output is raw, unprocessed data.

[0083] Step 2:

[0084] The server analyzes the collected information using natural language processing techniques. The input is the raw data collected in step 1. The server uses text analysis techniques to extract information features and topics and analyze the trends of the information. The output is an annotated dataset with metadata indicating the characteristics and trends of the information.

[0085] Step 3:

[0086] The server receives evaluation information provided by the user and optimizes it using a machine learning algorithm. The inputs are user feedback and analysis data from step 2. The server uses this feedback to update the machine learning model and readjust the selection criteria. The output is the updated selection criteria, which are stored in the database.

[0087] Step 4:

[0088] The terminal receives selection criteria sent from the server. The input is the updated selection criteria. Based on this, the terminal filters information retrieved from the network in real time and displays only the information relevant to the user. The output is the filtered information presented to the user.

[0089] Step 5:

[0090] Users provide feedback on the information provided using their terminals. The input is the information displayed on the terminal. User feedback is sent to the server and used to revise the selection criteria. The output is evaluation information stored on the server. In this step, the information environment can be dynamically optimized using context settings.

[0091] (Application Example 1)

[0092] 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."

[0093] The amount of information that individual users receive through communication networks is enormous, and it often contains content that can be offensive. This problem can hinder convenience and impede a comfortable information experience for users. Furthermore, because there is a lack of mechanisms to select and provide the most appropriate information according to the user's specific situation, users may not be able to find the information they are looking for due to information overload.

[0094] 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.

[0095] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing user evaluations and extracting features that cause discomfort, means for updating selection criteria that have been learned and optimized based on the extracted features, means for selecting data based on the selection criteria in a visual display device and removing unpleasant information, means for adjusting the selection criteria according to user settings, and means for the user to select a mode appropriate to the situation and control the type of information presented based on that. As a result, users receive information customized according to their situation and preferences at the time, and are freed from unnecessary information, enabling a comfortable and efficient information experience.

[0096] A "user" is an individual or organization that receives information via a communication network.

[0097] A "communication network" is an infrastructure for sending and receiving information, and includes technical means such as the internet and local networks.

[0098] "Data" refers to various types of information that users access, including text, images, and videos.

[0099] "Evaluation" refers to a user's reaction or feedback to the information they receive, including opinions on whether they liked or disliked it.

[0100] "Discomfort" refers to negative emotions or impressions that users experience when receiving information.

[0101] "Features" refer to specific patterns or properties contained in data, including elements that may cause discomfort.

[0102] "Optimized selection criteria" are the most effective information filtering rules tailored to the user's preferences and circumstances.

[0103] A "visual display device" is a device that presents information visually, and includes smartphones and eyeglasses.

[0104] "Selection" is the process of choosing necessary information according to specific criteria and removing unnecessary or offensive information.

[0105] "Settings" refers to the operations and options that allow users to customize the conditions and priorities for receiving information.

[0106] A "mode" is a function that temporarily changes the operation of the entire system according to the user's specific purpose.

[0107] The system that realizes this invention consists of a server, terminal devices, and users. The server collects diverse data through a communication network and analyzes the users' evaluations of that data. Based on the evaluations, it uses machine learning techniques to extract features from the data, focusing particularly on elements that are likely to cause discomfort. Based on these extracted features, the server generates optimized selection criteria and transmits them to the terminal devices.

[0108] The terminal device, acting as a visual display device, selects data acquired in real time based on selection criteria received from the server. This ensures that users can enjoy a comfortable information environment without receiving unnecessary or unpleasant information. Furthermore, by selecting a specific "mode," information appropriate to the current situation is prioritized. This mode switching allows for customization, for example, prioritizing educational content and displaying entertainment information sparingly when the user wants to concentrate on studying.

[0109] For example, if the user is a university student, they can select "Study Mode" on their smartphone app during exam periods. When this mode is turned on, information useful for exams and academic news are prioritized. In addition, by generating a prompt for the generating AI model such as, "Based on the user's current settings, set the filtering criteria for the necessary content," it is possible to create more appropriate selection criteria.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects diverse data accessed by users through the communication network. Input includes publicly available information on the network. The server analyzes this data and awaits user feedback. The output is a set of collected data.

[0113] Step 2:

[0114] The server receives and analyzes user ratings. User rating data and collected data are used as input. In this step, machine learning algorithms are used to extract features from the data and identify features that may cause discomfort. The output is a list of extracted features.

[0115] Step 3:

[0116] The server generates optimized selection criteria based on the extracted features and updates the selection criteria. The feature list and existing selection criteria are used as input. A generative AI model is used to dynamically optimize the selection criteria. New selection criteria are generated as output.

[0117] Step 4:

[0118] The terminal receives selection criteria sent from the server. The input includes the updated selection criteria. Based on this, the terminal performs data sorting on the visual display device in real time. The output is filtered information presented to the user.

[0119] Step 5:

[0120] The user accesses information using the terminal and selects a specific mode. Input includes user configuration information and pre-processed data. Based on the user's selection, the terminal changes and controls the type of information presented, displaying only the most relevant content. The output provides the user with appropriately selected and prioritized information.

[0121] In this way, through the specific data processing and calculations performed at each step, the entire system effectively filters information, creating a flow that provides users with a comfortable information experience.

[0122] 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.

[0123] This invention is a system that dynamically adjusts information selection criteria so that users can browse information on a communication network without experiencing discomfort. This system consists of a server, terminal devices, users, and an emotion engine.

[0124] The server collects information accessed by users via the communication network. This includes a wide variety of content, such as text, images, videos, and links. The server receives feedback from users and analyzes it to extract informational features. This feature analysis particularly considers elements that may cause discomfort.

[0125] Subsequently, the server uses an emotion engine to identify emotions from user feedback and browsing history. The emotion engine might, for example, determine that the user is feeling "stressed." Based on this emotion analysis, a machine learning algorithm generates and updates selection criteria, enabling the selection of customized information that reflects the user's emotional state.

[0126] The terminal device receives selection criteria transmitted from the server and uses them to filter information in real time. During the filtering process, the terminal device considers the user's emotional state and hides information that may cause discomfort. Furthermore, positive information identified by the emotion engine is displayed preferentially.

[0127] A concrete example is when a user wants to relax after spending a long time at work. The emotion engine analyzes this request and identifies the emotion of "wanting to relax." The server then generates selection criteria accordingly, and the terminal device prioritizes displaying content that is helpful for relaxation. Conversely, content that causes stress is filtered out to reduce the user's stress.

[0128] In this way, the server, emotion engine, and terminal device work together to provide an information experience adapted to the user's emotional state. This maintains a comfortable information environment for the user and improves the quality of information browsing.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server collects information accessed by users via the communication network. This information includes text, images, and videos, and is recorded in a database as the user's access history.

[0132] Step 2:

[0133] The server receives feedback from users and uses this feedback to evaluate the collected information. This feedback includes emotional responses such as "unpleasant" or "enjoyable."

[0134] Step 3:

[0135] The server uses an emotion engine to analyze received feedback and access history to identify the user's emotional state. For example, it can detect emotions such as "high stress" or "relaxed."

[0136] Step 4:

[0137] The server generates or updates selection criteria using machine learning algorithms based on the user's emotional state. This enables information selection that is appropriate to the user's emotions.

[0138] Step 5:

[0139] The terminal receives selection criteria sent from the server. These selection criteria are used to control the information displayed by the terminal.

[0140] Step 6:

[0141] The device scans information in real time according to selection criteria and hides unpleasant information identified by the sentiment engine. At the same time, positive information is prioritized.

[0142] Step 7:

[0143] Users view the information displayed on their devices and provide further feedback. This feedback will be used for future sentiment analysis and updating of selection criteria.

[0144] Step 8:

[0145] Users can change the status settings via their device as needed. For example, they can switch from "work mode" to "relax mode," and the emotion engine adjusts the information displayed accordingly.

[0146] Through this series of steps, the system provides a comfortable information environment optimized for the user's emotional state.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] In today's world, the overwhelming influx of information means that users are increasingly exposed to inappropriate or offensive information on communication networks. As a result, users may experience discomfort or stress during the information acquisition process. Furthermore, providing information tailored to individual emotional states becomes difficult, potentially leading to a decline in the quality of the user experience. There is a need to address these challenges and provide systems that allow users to acquire information comfortably.

[0150] 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.

[0151] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing emotion-based evaluations from users and extracting features that cause discomfort, and means for generating and updating selection criteria using a machine learning algorithm based on the extracted features. This makes it possible to provide customized information according to the emotional state of the user.

[0152] "Users" refer to individuals or organizations that acquire and use data using information systems.

[0153] A "communication network" refers to the entire system that constitutes the infrastructure for sending and receiving data, and includes the internet and intranets.

[0154] "Data" refers to all media formats used as information, including text, images, videos, and links.

[0155] An "emotion engine" refers to software or technology used to analyze user feedback and behavioral history to identify their emotional state.

[0156] A "machine learning algorithm" refers to a method for automating the process of learning patterns from data and making decisions.

[0157] "Selection criteria" refer to the rules and conditions used to select the information to be provided, and these are set with consideration for the user's emotional state.

[0158] A "terminal" is a device used to display or operate information, and includes personal computers and smartphones.

[0159] This invention is an information processing system that enables users to comfortably acquire information. Specific embodiments of this system are shown below.

[0160] Servers are responsible for collecting data accessed by users via communication networks. To effectively collect data, servers utilize web crawlers and APIs. In this process, servers handle a variety of data formats, including text, images, videos, and links.

[0161] The server receives feedback from users and analyzes it using an emotion engine. The emotion engine combines natural language processing (NLP) and machine learning algorithms to classify the user's emotional state into categories such as "stress" or "desire for relaxation." Specifically, it uses deep learning models to identify emotions.

[0162] Based on the results of this sentiment analysis, the server generates and updates selection criteria. These selection criteria are a set of rules for providing information that is tailored to the user's emotional state. The selection criteria are dynamically updated by a machine learning algorithm, prioritizing the presentation of positive information and filtering out negative information.

[0163] The device receives selection criteria sent from the server and performs data filtering in real time. The device takes the user's emotional state into consideration and hides data that may cause discomfort. Positive data is displayed preferentially, allowing the user to enjoy a less stressful information environment.

[0164] As a concrete example, consider a user who spends long hours at work and wants to relax. When the emotion engine identifies the emotion of "wanting to relax," the server generates selection criteria corresponding to this emotion, and the device prioritizes displaying content that helps with relaxation. For example, this could include displaying calming music or natural scenery.

[0165] An example of a prompt to input into the generative AI model is, "Suggest content to help the user relax after a long day of work." By coordinating the server, terminal, and emotion engine, information tailored to the user's individual emotional state can be provided.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server collects data accessed by users through the communication network. This input data includes text, images, videos, and links collected using web crawlers and APIs. The server structures the collected data and converts it into a format suitable for the next processing step. The output data is structured data stored in a storage system.

[0169] Step 2:

[0170] The server receives feedback from users. The input data consists of ratings and comments provided by the users. The server inputs this feedback into its sentiment engine and performs sentiment analysis using natural language processing (NLP) techniques. Specifically, it calculates sentiment scores for words through text analysis and identifies the overall emotional state. The output of this step is data indicating the user's emotional state, and is labeled with terms such as "stressed" or "relaxed."

[0171] Step 3:

[0172] The server generates and updates selection criteria using machine learning algorithms based on emotional state data from the emotion engine. Input data includes emotional states and the user's past browsing history. The server processes this data using algorithms to create a set of rules for selecting information relevant to the user. The output of this step is the customized selection criteria.

[0173] Step 4:

[0174] The terminal receives selection criteria sent from the server. The input data consists of these selection criteria. The terminal screens the data in real time, filtering information while considering the user's emotional state. Positive information is displayed preferentially, while unpleasant information is hidden. The output is processed information that appears on the display screen.

[0175] Step 5:

[0176] The user receives customized information through the device. The input data is filtered data displayed by the device. Based on this, the user can enjoy a pleasant information experience. The output of this step is user satisfaction and a comfortable information environment. For example, if the user is seeking relaxation, calming music or nature videos will be displayed.

[0177] (Application Example 2)

[0178] 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".

[0179] The various types of information provided over communication networks may not always align with a user's emotional state, which can cause discomfort. Therefore, there is a need for a system that can dynamically select and present information in accordance with the user's emotions and preferences.

[0180] 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. In this invention, the server includes means for collecting information, means for analyzing user evaluations and extracting characteristics, and means for dynamically displaying suitable suggestions based on the user's emotional state. This makes it possible to provide information that is in line with the user's emotional state and preferences.

[0181] A "communication network" refers to the infrastructure for sending and receiving data, and includes various connection methods, such as the internet.

[0182] "Data" refers to a collection of information transferred over a communication network, and includes various forms such as text, images, videos, and links.

[0183] "Evaluation" refers to feedback and comments provided by users, which are used to measure the usefulness and emotional impact of the data.

[0184] "Characteristics" refer to specific elements and attributes of data extracted from user evaluations, including those that may cause discomfort.

[0185] "Selection criteria" are a set of conditions used to select appropriate data that reflect the emotional state of the user.

[0186] A "terminal device" is a hardware device used by users to receive and view data, and includes smartphones and personal computers.

[0187] "Emotional state" refers to the user's mental state or mood, and includes things like stress and relaxation.

[0188] "Suggestions" refer to information presented by the system based on the user's emotional state, including options for meals and content.

[0189] This invention is a system that optimizes the provision of information tailored to the emotional state of users in a communication network. The system collects and analyzes data and prioritizes the display of information appropriate to the user's emotional state, thereby providing a comfortable information environment. A specific embodiment of this system is described below.

[0190] The server stores data (text, images, videos, etc.) collected via the communication network and analyzes feedback provided by users. The analysis uses sentiment analysis models such as TensorFlow to identify features that cause discomfort from the evaluation. Then, based on the identified features, selection criteria are optimized using machine learning algorithms.

[0191] The device uses these optimized selection criteria to filter data in real time. Through the Android® platform provided by Google® and the iOS platform provided by Apple, it dynamically displays suggestions tailored to the user's emotional state. For example, a user who wants to relax will be recommended menus with relaxing effects. The appropriateness of the suggestions is then used to improve future optimizations based on user feedback.

[0192] For example, in a food delivery app, if a user enters "I want to relax today," the emotion engine analyzes this emotional state and displays foods that align with relaxation with a higher priority. If the user orders "spicy food" from their order history, similar suggestions will be made later based on the user's preferences.

[0193] An example of a prompt for a generative AI model is, "Generate appropriate food delivery options based on the user's past feedback and emotional state." Based on this prompt, the AI ​​will make appropriate suggestions.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server collects data viewed by users via the communication network. This data includes text, images, videos, etc. The server stores this data in a database and simultaneously saves access log data. MySQL® is used for this database.

[0197] Step 2:

[0198] The server receives feedback from users and analyzes their evaluations. The feedback input consists of users expressing their feelings about the data in numerical or textual form. To analyze this evaluation data, the server uses Python to perform text mining and natural language processing to extract unpleasant characteristics. The output is the identified unpleasant characteristics.

[0199] Step 3:

[0200] The server performs sentiment analysis based on the extracted characteristics. It uses TensorFlow to execute machine learning algorithms and model the user's emotional state. The input is the extracted characteristics, and the output is the user's emotional state (e.g., stress, relaxation).

[0201] Step 4:

[0202] The server generates and updates selection criteria based on emotional states. Emotional states obtained through machine learning and past selection criterion information are used as input, and optimized selection criteria are output by an algorithm.

[0203] Step 5:

[0204] The terminal receives optimized selection criteria sent from the server. Based on these criteria, the terminal performs data sorting in real time. The input is the selection criteria from the server, and the output is a list of data to be displayed to the user.

[0205] Step 6:

[0206] The user operates the device and receives suggestions that match their emotional state. When the user presses a suggestion button, the device refers to the emotional analysis data and prioritizes displaying suitable information and offers. Emotional analysis data is used as input, and a specific list of suggestions is displayed on the user's screen as output.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system that selects the information users receive on a communication network and provides a comfortable information environment according to individual preferences and circumstances. This system consists of a server, terminal devices, and users who utilize them.

[0224] First, the server collects information through the communication network. The collected information includes various types of content that users access on a daily basis. Furthermore, this server has the function of receiving ratings from users and analyzing them to extract characteristics of the information. In particular, it focuses on characteristics that may evoke discomfort and stores them in a database.

[0225] Next, the server uses machine learning techniques to automatically update selection criteria based on the collected feedback and information characteristics. These selection criteria become important rules for distinguishing between information that users want and information they don't. The server periodically reviews and optimizes these criteria.

[0226] The terminal device receives selection criteria transmitted from the server. Based on these received criteria, the terminal filters information retrieved from the network in real time, hiding unnecessary or offensive information. This filtering process is handled on the terminal, designed to allow users to enjoy a comfortable information experience without being aware of it.

[0227] Users can view information and provide feedback through their devices. They can also change their context settings. These settings reflect what kind of information users want to prioritize and when, and include options such as "on a diet" or "traveling." Based on these settings, the selection criteria are dynamically adjusted, allowing information to be presented exceptionally under specific conditions.

[0228] For example, if a user is on a diet, they can change their status setting to "on a diet." The server then uses this setting to provide criteria for hiding sweets-related information on the device. However, sweets information related to facilities the user plans to visit during their trip will be displayed as an exception.

[0229] In this way, the server and terminal work together, creating an environment where users can acquire information more comfortably. This invention aims to reduce the burden on users and provide a comfortable information browsing experience through this method.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server collects information accessed by users via the communication network. The collected data includes various types of content, such as text, images, and links.

[0233] Step 2:

[0234] The server receives evaluation data from users. The feedback provided by users includes labels such as "unpleasant" or "interesting," and this is recorded in the database.

[0235] Step 3:

[0236] The server analyzes the received evaluation data and extracts features of information that cause discomfort. These features may include specific keywords or image patterns.

[0237] Step 4:

[0238] The server uses machine learning technology to generate and update selection criteria based on extracted features. This enables information selection tailored to the user's preferences and circumstances.

[0239] Step 5:

[0240] The terminal receives the latest selection criteria sent from the server. These criteria will be used in the next information filtering process.

[0241] Step 6:

[0242] Based on criteria received from the server, the device filters out unnecessary or offensive information from the real-time data it receives and hides it.

[0243] Step 7:

[0244] Users contribute to improving the accuracy of selection criteria by viewing the displayed information and providing new feedback.

[0245] Step 8:

[0246] Users can change the status settings as needed. When the status changes, the selection criteria are dynamically adjusted based on those settings, and the displayed information is also updated.

[0247] Through this series of processes, the system is continuously optimized, providing users with a comfortable information environment.

[0248] (Example 1)

[0249] 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".

[0250] In modern communication networks, users have access to a vast amount of information, but this often includes information that is unpleasant or unnecessary to them. Therefore, users often spend a lot of time and effort trying to access the information they want. To solve this problem, a means is needed to select and provide appropriate information according to each user's individual preferences and circumstances.

[0251] 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.

[0252] In this invention, the server includes means for collecting information related to user activities via a communication network and obtaining content from information sources; means for analyzing evaluation information and extracting trends and features of the information using natural language processing technology; and means for updating optimized selection criteria based on the collected evaluation information and feature data using machine learning algorithms. This enables users to enjoy a comfortable information environment tailored to their preferences and circumstances.

[0253] A "communication network" refers to the entire network infrastructure used for sending and receiving information, and is the medium through which data is exchanged between computer devices.

[0254] An "information processing device" is a device that performs analysis and sorting based on received data, and its role is to provide users with appropriate information.

[0255] "Evaluation information" refers to the evaluations and feedback that users provide to specific data or content, and is used to optimize the system's selection criteria.

[0256] "Natural language processing technology" refers to the technology necessary for computers to understand and process the language that humans use on a daily basis, and is used to extract trends and characteristics of information.

[0257] A "machine learning algorithm" is a method for analyzing large amounts of data and automatically learning patterns and rules, and is used to optimize the selection criteria of a system.

[0258] "Selection criteria" are standards set to distinguish between information that is useful to the user and information that is unnecessary or unpleasant, and they serve as the rules by which the system selects information.

[0259] "Optimization" refers to adjusting systems and processes to achieve the greatest effect for a specific purpose, and is done to improve the user experience.

[0260] This system is primarily composed of three elements: a server, a terminal, and a user. In implementing the invention, the server plays a crucial role in collecting information and selecting information based on the user's preferences.

[0261] The server collects content from various sources via a communication network. The collected information is stored in a database and becomes the subject of analysis. The server uses natural language processing technology to extract trends and characteristics of the information, and then uses machine learning algorithms based on the evaluation information to optimize the selection criteria. This process could involve using programming languages ​​such as Python and leveraging machine learning libraries such as TensorFlow and Scikit-learn.

[0262] The terminal filters received information in real time based on selection criteria sent from the server. Only appropriate information is presented to the user on the terminal, while unpleasant or unnecessary information is hidden. This process runs smoothly by utilizing the terminal's processing power.

[0263] Users can utilize the information obtained through their devices and provide feedback as needed. This feedback is sent to the server and used to review the selection criteria for the future. Users can also set statuses such as "on a diet" or "traveling," which dynamically adjusts the information selection criteria. These status settings can be easily changed through the device interface.

[0264] For example, when a user configures their travel settings, the server reflects this information, selects recommended destinations, and sends them to the user's device. Users can specifically utilize this feature by entering a prompt such as, "Please provide recommendations for places I plan to visit on my next trip."

[0265] This invention aims to provide an optimized information environment for users, achieving comfortable information access through a generative AI model.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server collects content from information sources via a communication network. It is given feed information and URLs of publicly available content as input. The server collects this using crawling techniques and stores it in a database. The output is raw, unprocessed data.

[0269] Step 2:

[0270] The server analyzes the collected information using natural language processing techniques. The input is the raw data collected in step 1. The server uses text analysis techniques to extract information features and topics and analyze the trends of the information. The output is an annotated dataset with metadata indicating the characteristics and trends of the information.

[0271] Step 3:

[0272] The server receives evaluation information provided by the user and optimizes it using a machine learning algorithm. The inputs are user feedback and analysis data from step 2. The server uses this feedback to update the machine learning model and readjust the selection criteria. The output is the updated selection criteria, which are stored in the database.

[0273] Step 4:

[0274] The terminal receives selection criteria sent from the server. The input is the updated selection criteria. Based on this, the terminal filters information retrieved from the network in real time and displays only the information relevant to the user. The output is the filtered information presented to the user.

[0275] Step 5:

[0276] Users provide feedback on the information provided using their terminals. The input is the information displayed on the terminal. User feedback is sent to the server and used to revise the selection criteria. The output is evaluation information stored on the server. In this step, the information environment can be dynamically optimized using context settings.

[0277] (Application Example 1)

[0278] 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 glasses 214 will be referred to as the "terminal."

[0279] The amount of information that individual users receive through communication networks is enormous, and it often contains content that can be offensive. This problem can hinder convenience and impede a comfortable information experience for users. Furthermore, because there is a lack of mechanisms to select and provide the most appropriate information according to the user's specific situation, users may not be able to find the information they are looking for due to information overload.

[0280] 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.

[0281] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing user evaluations and extracting features that cause discomfort, means for updating selection criteria that have been learned and optimized based on the extracted features, means for selecting data based on the selection criteria in a visual display device and removing unpleasant information, means for adjusting the selection criteria according to user settings, and means for the user to select a mode appropriate to the situation and control the type of information presented based on that. As a result, users receive information customized according to their situation and preferences at the time, and are freed from unnecessary information, enabling a comfortable and efficient information experience.

[0282] "User" refers to an individual or group that receives information via a communication network.

[0283] "Communication network" refers to the infrastructure for transmitting and receiving information, and is a technical means including the Internet and local networks.

[0284] "Data" refers to various types of information accessed by users, including text, images, videos, etc.

[0285] "Evaluation" refers to the reaction or feedback of users to the information received, including opinions on preference and discomfort.

[0286] "Discomfort" refers to the negative feelings or impressions that users have when receiving information.

[0287] "Feature" refers to the specific patterns or properties contained in the data, including elements that may cause discomfort.

[0288] "Optimized selection criteria" refers to the most effective information filtering rules according to users' preferences and situations.

[0289] "Visual display device" refers to a device that visually presents information, including smartphones and glasses-type devices.

[0290] "Selection" refers to the process of selecting necessary information according to specific criteria and removing unnecessary or uncomfortable information.

[0291] "Setting" refers to the operations or options for users to customize the conditions and priorities of information reception.

[0292] "Mode" refers to the function of temporarily changing the overall operation of the system according to specific purposes of users.

[0293] The system that realizes this invention consists of a server, terminal devices, and users. The server collects diverse data through a communication network and analyzes the users' evaluations of that data. Based on the evaluations, it uses machine learning techniques to extract features from the data, focusing particularly on elements that are likely to cause discomfort. Based on these extracted features, the server generates optimized selection criteria and transmits them to the terminal devices.

[0294] The terminal device, acting as a visual display device, selects data acquired in real time based on selection criteria received from the server. This ensures that users can enjoy a comfortable information environment without receiving unnecessary or unpleasant information. Furthermore, by selecting a specific "mode," information appropriate to the current situation is prioritized. This mode switching allows for customization, for example, prioritizing educational content and displaying entertainment information sparingly when the user wants to concentrate on studying.

[0295] For example, if the user is a university student, they can select "Study Mode" on their smartphone app during exam periods. When this mode is turned on, information useful for exams and academic news are prioritized. In addition, by generating a prompt for the generating AI model such as, "Based on the user's current settings, set the filtering criteria for the necessary content," it is possible to create more appropriate selection criteria.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects diverse data accessed by users through the communication network. Input includes publicly available information on the network. The server analyzes this data and awaits user feedback. The output is a set of collected data.

[0299] Step 2:

[0300] The server receives the evaluation from the user and performs analysis. As input, the user's evaluation data and the collected data are used. In this step, machine learning algorithms are used to extract the features of the data and identify the features that may cause discomfort. As output, a list of the extracted features is obtained.

[0301] Step 3:

[0302] The server generates optimized selection criteria based on the extracted features and updates the screening criteria. As input, the feature list and the existing selection criteria are used. Using a generative AI model, a process is performed to dynamically optimize the screening criteria. As output, new selection criteria are generated.

[0303] Step 4:

[0304] The terminal receives the selection criteria transmitted from the server. As input, the updated selection criteria are included. Based on this, the terminal performs real-time data screening processing on the visual display device. As output, filtered information presented to the user is generated.

[0305] Step 5:

[0306] The user browses the information accessed using the terminal and selects a specific mode. As input, the user's setting information and the preprocessed data are included. Based on the user's selection, the terminal changes and controls the type of information and presents only the optimal content. As output, appropriately screened and prioritized information is provided to the user.

[0307] In this way, through the specific data processing and data calculations performed in each step, a process is realized in which the entire system effectively filters information and provides the user with a comfortable information experience.

[0308] 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.

[0309] This invention is a system that dynamically adjusts information selection criteria so that users can browse information on a communication network without experiencing discomfort. This system consists of a server, terminal devices, users, and an emotion engine.

[0310] The server collects information accessed by users via the communication network. This includes a wide variety of content, such as text, images, videos, and links. The server receives feedback from users and analyzes it to extract informational features. This feature analysis particularly considers elements that may cause discomfort.

[0311] Subsequently, the server uses an emotion engine to identify emotions from user feedback and browsing history. The emotion engine might, for example, determine that the user is feeling "stressed." Based on this emotion analysis, a machine learning algorithm generates and updates selection criteria, enabling the selection of customized information that reflects the user's emotional state.

[0312] The terminal device receives selection criteria transmitted from the server and uses them to filter information in real time. During the filtering process, the terminal device considers the user's emotional state and hides information that may cause discomfort. Furthermore, positive information identified by the emotion engine is displayed preferentially.

[0313] A concrete example is when a user wants to relax after spending a long time at work. The emotion engine analyzes this request and identifies the emotion of "wanting to relax." The server then generates selection criteria accordingly, and the terminal device prioritizes displaying content that is helpful for relaxation. Conversely, content that causes stress is filtered out to reduce the user's stress.

[0314] In this way, the server, emotion engine, and terminal device work together to provide an information experience adapted to the user's emotional state. This maintains a comfortable information environment for the user and improves the quality of information browsing.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The server collects information accessed by users via the communication network. This information includes text, images, and videos, and is recorded in a database as the user's access history.

[0318] Step 2:

[0319] The server receives feedback from users and uses this feedback to evaluate the collected information. This feedback includes emotional responses such as "unpleasant" or "enjoyable."

[0320] Step 3:

[0321] The server uses an emotion engine to analyze received feedback and access history to identify the user's emotional state. For example, it can detect emotions such as "high stress" or "relaxed."

[0322] Step 4:

[0323] The server generates or updates selection criteria using machine learning algorithms based on the user's emotional state. This enables information selection that is appropriate to the user's emotions.

[0324] Step 5:

[0325] The terminal receives selection criteria sent from the server. These selection criteria are used to control the information displayed by the terminal.

[0326] Step 6:

[0327] The device scans information in real time according to selection criteria and hides unpleasant information identified by the sentiment engine. At the same time, positive information is prioritized.

[0328] Step 7:

[0329] Users view the information displayed on their devices and provide further feedback. This feedback will be used for future sentiment analysis and updating of selection criteria.

[0330] Step 8:

[0331] Users can change the status settings via their device as needed. For example, they can switch from "work mode" to "relax mode," and the emotion engine adjusts the information displayed accordingly.

[0332] Through this series of steps, the system provides a comfortable information environment optimized for the user's emotional state.

[0333] (Example 2)

[0334] 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".

[0335] In today's world, the overwhelming influx of information means that users are increasingly exposed to inappropriate or offensive information on communication networks. As a result, users may experience discomfort or stress during the information acquisition process. Furthermore, providing information tailored to individual emotional states becomes difficult, potentially leading to a decline in the quality of the user experience. There is a need to address these challenges and provide systems that allow users to acquire information comfortably.

[0336] 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.

[0337] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing emotion-based evaluations from users and extracting features that cause discomfort, and means for generating and updating selection criteria using a machine learning algorithm based on the extracted features. This makes it possible to provide customized information according to the emotional state of the user.

[0338] "Users" refer to individuals or organizations that acquire and use data using information systems.

[0339] A "communication network" refers to the entire system that constitutes the infrastructure for sending and receiving data, and includes the internet and intranets.

[0340] "Data" refers to all media formats used as information, including text, images, videos, and links.

[0341] An "emotion engine" refers to software or technology used to analyze user feedback and behavioral history to identify their emotional state.

[0342] A "machine learning algorithm" refers to a method for automating the process of learning patterns from data and making decisions.

[0343] "Selection criteria" refer to the rules and conditions used to select the information to be provided, and these are set with consideration for the user's emotional state.

[0344] A "terminal" is a device used to display or operate information, and includes personal computers and smartphones.

[0345] This invention is an information processing system that enables users to comfortably acquire information. Specific embodiments of this system are shown below.

[0346] Servers are responsible for collecting data accessed by users via communication networks. To effectively collect data, servers utilize web crawlers and APIs. In this process, servers handle a variety of data formats, including text, images, videos, and links.

[0347] The server receives feedback from users and analyzes it using an emotion engine. The emotion engine combines natural language processing (NLP) and machine learning algorithms to classify the user's emotional state into categories such as "stress" or "desire for relaxation." Specifically, it uses deep learning models to identify emotions.

[0348] Based on the results of this sentiment analysis, the server generates and updates selection criteria. These selection criteria are a set of rules for providing information that is tailored to the user's emotional state. The selection criteria are dynamically updated by a machine learning algorithm, prioritizing the presentation of positive information and filtering out negative information.

[0349] The device receives selection criteria sent from the server and performs data filtering in real time. The device takes the user's emotional state into consideration and hides data that may cause discomfort. Positive data is displayed preferentially, allowing the user to enjoy a less stressful information environment.

[0350] As a concrete example, consider a user who spends long hours at work and wants to relax. When the emotion engine identifies the emotion of "wanting to relax," the server generates selection criteria corresponding to this emotion, and the device prioritizes displaying content that helps with relaxation. For example, this could include displaying calming music or natural scenery.

[0351] An example of a prompt to input into the generative AI model is, "Suggest content to help the user relax after a long day of work." By coordinating the server, terminal, and emotion engine, information tailored to the user's individual emotional state can be provided.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server collects data accessed by users through the communication network. This input data includes text, images, videos, and links collected using web crawlers and APIs. The server structures the collected data and converts it into a format suitable for the next processing step. The output data is structured data stored in a storage system.

[0355] Step 2:

[0356] The server receives feedback from users. The input data consists of ratings and comments provided by the users. The server inputs this feedback into its sentiment engine and performs sentiment analysis using natural language processing (NLP) techniques. Specifically, it calculates sentiment scores for words through text analysis and identifies the overall emotional state. The output of this step is data indicating the user's emotional state, and is labeled with terms such as "stressed" or "relaxed."

[0357] Step 3:

[0358] The server generates and updates selection criteria using machine learning algorithms based on emotional state data from the emotion engine. Input data includes emotional states and the user's past browsing history. The server processes this data using algorithms to create a set of rules for selecting information relevant to the user. The output of this step is the customized selection criteria.

[0359] Step 4:

[0360] The terminal receives selection criteria sent from the server. The input data consists of these selection criteria. The terminal screens the data in real time, filtering information while considering the user's emotional state. Positive information is displayed preferentially, while unpleasant information is hidden. The output is processed information that appears on the display screen.

[0361] Step 5:

[0362] The user receives customized information through the device. The input data is filtered data displayed by the device. Based on this, the user can enjoy a pleasant information experience. The output of this step is user satisfaction and a comfortable information environment. For example, if the user is seeking relaxation, calming music or nature videos will be displayed.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] The various types of information provided over communication networks may not always align with a user's emotional state, which can cause discomfort. Therefore, there is a need for a system that can dynamically select and present information in accordance with the user's emotions and preferences.

[0366] 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. In this invention, the server includes means for collecting information, means for analyzing user evaluations and extracting characteristics, and means for dynamically displaying suitable suggestions based on the user's emotional state. This makes it possible to provide information that is in line with the user's emotional state and preferences.

[0367] A "communication network" refers to the infrastructure for sending and receiving data, and includes various connection methods, such as the internet.

[0368] "Data" refers to a collection of information transferred over a communication network, and includes various forms such as text, images, videos, and links.

[0369] "Evaluation" refers to feedback and comments provided by users, which are used to measure the usefulness and emotional impact of the data.

[0370] "Characteristics" refer to specific elements and attributes of data extracted from user evaluations, including those that may cause discomfort.

[0371] "Selection criteria" are a set of conditions used to select appropriate data that reflect the emotional state of the user.

[0372] A "terminal device" is a hardware device used by users to receive and view data, and includes smartphones and personal computers.

[0373] "Emotional state" refers to the user's mental state or mood, and includes things like stress and relaxation.

[0374] "Suggestions" refer to information presented by the system based on the user's emotional state, including options for meals and content.

[0375] This invention is a system that optimizes the provision of information tailored to the emotional state of users in a communication network. The system collects and analyzes data and prioritizes the display of information appropriate to the user's emotional state, thereby providing a comfortable information environment. A specific embodiment of this system is described below.

[0376] The server stores data (text, images, videos, etc.) collected via the communication network and analyzes feedback provided by users. The analysis uses sentiment analysis models such as TensorFlow to identify features that cause discomfort from the evaluation. Then, based on the identified features, selection criteria are optimized using machine learning algorithms.

[0377] The device uses these optimized selection criteria to filter data in real time. Through the Android platform provided by Google and the iOS platform provided by Apple, it dynamically displays suggestions tailored to the user's emotional state. For example, a user who wants to relax will be recommended menus with relaxing effects. The appropriateness of the suggestions is then used to improve future optimizations based on user feedback.

[0378] For example, in a food delivery app, if a user enters "I want to relax today," the emotion engine analyzes this emotional state and displays foods that align with relaxation with a higher priority. If the user orders "spicy food" from their order history, similar suggestions will be made later based on the user's preferences.

[0379] An example of a prompt for a generative AI model is, "Generate appropriate food delivery options based on the user's past feedback and emotional state." Based on this prompt, the AI ​​will make appropriate suggestions.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server collects data viewed by users via the communication network. This data includes text, images, videos, etc. The server stores this data in a database and simultaneously saves access log data. MySQL is used for this database.

[0383] Step 2:

[0384] The server receives feedback from users and analyzes their evaluations. The feedback input consists of users expressing their feelings about the data in numerical or textual form. To analyze this evaluation data, the server uses Python to perform text mining and natural language processing to extract unpleasant characteristics. The output is the identified unpleasant characteristics.

[0385] Step 3:

[0386] The server performs sentiment analysis based on the extracted characteristics. It uses TensorFlow to execute machine learning algorithms and model the user's emotional state. The input is the extracted characteristics, and the output is the user's emotional state (e.g., stress, relaxation).

[0387] Step 4:

[0388] The server generates and updates selection criteria based on emotional states. Emotional states obtained through machine learning and past selection criterion information are used as input, and optimized selection criteria are output by an algorithm.

[0389] Step 5:

[0390] The terminal receives optimized selection criteria sent from the server. Based on these criteria, the terminal performs data sorting in real time. The input is the selection criteria from the server, and the output is a list of data to be displayed to the user.

[0391] Step 6:

[0392] The user operates the device and receives suggestions that match their emotional state. When the user presses a suggestion button, the device refers to the emotional analysis data and prioritizes displaying suitable information and offers. Emotional analysis data is used as input, and a specific list of suggestions is displayed on the user's screen as output.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system that selects the information users receive on a communication network and provides a comfortable information environment according to individual preferences and circumstances. This system consists of a server, terminal devices, and users who utilize them.

[0410] First, the server collects information through the communication network. The collected information includes various types of content that users access on a daily basis. Furthermore, this server has the function of receiving ratings from users and analyzing them to extract characteristics of the information. In particular, it focuses on characteristics that may evoke discomfort and stores them in a database.

[0411] Next, the server uses machine learning techniques to automatically update selection criteria based on the collected feedback and information characteristics. These selection criteria become important rules for distinguishing between information that users want and information they don't. The server periodically reviews and optimizes these criteria.

[0412] The terminal device receives selection criteria transmitted from the server. Based on these received criteria, the terminal filters information retrieved from the network in real time, hiding unnecessary or offensive information. This filtering process is handled on the terminal, designed to allow users to enjoy a comfortable information experience without being aware of it.

[0413] Users can view information and provide feedback through their devices. They can also change their context settings. These settings reflect what kind of information users want to prioritize and when, and include options such as "on a diet" or "traveling." Based on these settings, the selection criteria are dynamically adjusted, allowing information to be presented exceptionally under specific conditions.

[0414] For example, if a user is on a diet, they can change their status setting to "on a diet." The server then uses this setting to provide criteria for hiding sweets-related information on the device. However, sweets information related to facilities the user plans to visit during their trip will be displayed as an exception.

[0415] In this way, the server and terminal work together, creating an environment where users can acquire information more comfortably. This invention aims to reduce the burden on users and provide a comfortable information browsing experience through this method.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server collects information accessed by users via the communication network. The collected data includes various types of content, such as text, images, and links.

[0419] Step 2:

[0420] The server receives evaluation data from users. The feedback provided by users includes labels such as "unpleasant" or "interesting," and this is recorded in the database.

[0421] Step 3:

[0422] The server analyzes the received evaluation data and extracts features of information that cause discomfort. These features may include specific keywords or image patterns.

[0423] Step 4:

[0424] The server uses machine learning technology to generate and update selection criteria based on extracted features. This enables information selection tailored to the user's preferences and circumstances.

[0425] Step 5:

[0426] The terminal receives the latest selection criteria sent from the server. These criteria will be used in the next information filtering process.

[0427] Step 6:

[0428] Based on criteria received from the server, the device filters out unnecessary or offensive information from the real-time data it receives and hides it.

[0429] Step 7:

[0430] Users contribute to improving the accuracy of selection criteria by viewing the displayed information and providing new feedback.

[0431] Step 8:

[0432] Users can change the status settings as needed. When the status changes, the selection criteria are dynamically adjusted based on those settings, and the displayed information is also updated.

[0433] Through this series of processes, the system is continuously optimized, providing users with a comfortable information environment.

[0434] (Example 1)

[0435] 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."

[0436] In modern communication networks, users have access to a vast amount of information, but this often includes information that is unpleasant or unnecessary to them. Therefore, users often spend a lot of time and effort trying to access the information they want. To solve this problem, a means is needed to select and provide appropriate information according to each user's individual preferences and circumstances.

[0437] 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.

[0438] In this invention, the server includes means for collecting information related to user activities via a communication network and obtaining content from information sources; means for analyzing evaluation information and extracting trends and features of the information using natural language processing technology; and means for updating optimized selection criteria based on the collected evaluation information and feature data using machine learning algorithms. This enables users to enjoy a comfortable information environment tailored to their preferences and circumstances.

[0439] A "communication network" refers to the entire network infrastructure used for sending and receiving information, and is the medium through which data is exchanged between computer devices.

[0440] An "information processing device" is a device that performs analysis and sorting based on received data, and its role is to provide users with appropriate information.

[0441] "Evaluation information" refers to the evaluations and feedback that users provide to specific data or content, and is used to optimize the system's selection criteria.

[0442] "Natural language processing technology" refers to the technology necessary for computers to understand and process the language that humans use on a daily basis, and is used to extract trends and characteristics of information.

[0443] A "machine learning algorithm" is a method for analyzing large amounts of data and automatically learning patterns and rules, and is used to optimize the selection criteria of a system.

[0444] "Selection criteria" are standards set to distinguish between information that is useful to the user and information that is unnecessary or unpleasant, and they serve as the rules by which the system selects information.

[0445] "Optimization" refers to adjusting systems and processes to achieve the greatest effect for a specific purpose, and is done to improve the user experience.

[0446] This system is primarily composed of three elements: a server, a terminal, and a user. In implementing the invention, the server plays a crucial role in collecting information and selecting information based on the user's preferences.

[0447] The server collects content from various sources via a communication network. The collected information is stored in a database and becomes the subject of analysis. The server uses natural language processing technology to extract trends and characteristics of the information, and then uses machine learning algorithms based on the evaluation information to optimize the selection criteria. This process could involve using programming languages ​​such as Python and leveraging machine learning libraries such as TensorFlow and Scikit-learn.

[0448] The terminal filters received information in real time based on selection criteria sent from the server. Only appropriate information is presented to the user on the terminal, while unpleasant or unnecessary information is hidden. This process runs smoothly by utilizing the terminal's processing power.

[0449] Users can utilize the information obtained through their devices and provide feedback as needed. This feedback is sent to the server and used to review the selection criteria for the future. Users can also set statuses such as "on a diet" or "traveling," which dynamically adjusts the information selection criteria. These status settings can be easily changed through the device interface.

[0450] For example, when a user configures their travel settings, the server reflects this information, selects recommended destinations, and sends them to the user's device. Users can specifically utilize this feature by entering a prompt such as, "Please provide recommendations for places I plan to visit on my next trip."

[0451] This invention aims to provide an optimized information environment for users, achieving comfortable information access through a generative AI model.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The server collects content from information sources via a communication network. It is given feed information and URLs of publicly available content as input. The server collects this using crawling techniques and stores it in a database. The output is raw, unprocessed data.

[0455] Step 2:

[0456] The server analyzes the collected information using natural language processing techniques. The input is the raw data collected in step 1. The server uses text analysis techniques to extract information features and topics and analyze the trends of the information. The output is an annotated dataset with metadata indicating the characteristics and trends of the information.

[0457] Step 3:

[0458] The server receives evaluation information provided by the user and optimizes it using a machine learning algorithm. The inputs are user feedback and analysis data from step 2. The server uses this feedback to update the machine learning model and readjust the selection criteria. The output is the updated selection criteria, which are stored in the database.

[0459] Step 4:

[0460] The terminal receives selection criteria sent from the server. The input is the updated selection criteria. Based on this, the terminal filters information retrieved from the network in real time and displays only the information relevant to the user. The output is the filtered information presented to the user.

[0461] Step 5:

[0462] Users provide feedback on the information provided using their terminals. The input is the information displayed on the terminal. User feedback is sent to the server and used to revise the selection criteria. The output is evaluation information stored on the server. In this step, the information environment can be dynamically optimized using context settings.

[0463] (Application Example 1)

[0464] 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."

[0465] The amount of information that individual users receive through communication networks is enormous, and it often contains content that can be offensive. This problem can hinder convenience and impede a comfortable information experience for users. Furthermore, because there is a lack of mechanisms to select and provide the most appropriate information according to the user's specific situation, users may not be able to find the information they are looking for due to information overload.

[0466] 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.

[0467] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing user evaluations and extracting features that cause discomfort, means for updating selection criteria that have been learned and optimized based on the extracted features, means for selecting data based on the selection criteria in a visual display device and removing unpleasant information, means for adjusting the selection criteria according to user settings, and means for the user to select a mode appropriate to the situation and control the type of information presented based on that. As a result, users receive information customized according to their situation and preferences at the time, and are freed from unnecessary information, enabling a comfortable and efficient information experience.

[0468] A "user" is an individual or organization that receives information via a communication network.

[0469] A "communication network" is an infrastructure for sending and receiving information, and includes technical means such as the internet and local networks.

[0470] "Data" refers to various types of information that users access, including text, images, and videos.

[0471] "Evaluation" refers to a user's reaction or feedback to the information they receive, including opinions on whether they liked or disliked it.

[0472] "Discomfort" refers to negative emotions or impressions that users experience when receiving information.

[0473] "Features" refer to specific patterns or properties contained in data, including elements that may cause discomfort.

[0474] "Optimized selection criteria" are the most effective information filtering rules tailored to the user's preferences and circumstances.

[0475] A "visual display device" is a device that presents information visually, and includes smartphones and eyeglasses.

[0476] "Selection" is the process of choosing necessary information according to specific criteria and removing unnecessary or offensive information.

[0477] "Settings" refers to the operations and options that allow users to customize the conditions and priorities for receiving information.

[0478] A "mode" is a function that temporarily changes the operation of the entire system according to the user's specific purpose.

[0479] The system that realizes this invention consists of a server, terminal devices, and users. The server collects diverse data through a communication network and analyzes the users' evaluations of that data. Based on the evaluations, it uses machine learning techniques to extract features from the data, focusing particularly on elements that are likely to cause discomfort. Based on these extracted features, the server generates optimized selection criteria and transmits them to the terminal devices.

[0480] The terminal device, acting as a visual display device, selects data acquired in real time based on selection criteria received from the server. This ensures that users can enjoy a comfortable information environment without receiving unnecessary or unpleasant information. Furthermore, by selecting a specific "mode," information appropriate to the current situation is prioritized. This mode switching allows for customization, for example, prioritizing educational content and displaying entertainment information sparingly when the user wants to concentrate on studying.

[0481] For example, if the user is a university student, they can select "Study Mode" on their smartphone app during exam periods. When this mode is turned on, information useful for exams and academic news are prioritized. In addition, by generating a prompt for the generating AI model such as, "Based on the user's current settings, set the filtering criteria for the necessary content," it is possible to create more appropriate selection criteria.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The server collects diverse data accessed by users through the communication network. Input includes publicly available information on the network. The server analyzes this data and awaits user feedback. The output is a set of collected data.

[0485] Step 2:

[0486] The server receives and analyzes user ratings. User rating data and collected data are used as input. In this step, machine learning algorithms are used to extract features from the data and identify features that may cause discomfort. The output is a list of extracted features.

[0487] Step 3:

[0488] The server generates optimized selection criteria based on the extracted features and updates the selection criteria. The feature list and existing selection criteria are used as input. A generative AI model is used to dynamically optimize the selection criteria. New selection criteria are generated as output.

[0489] Step 4:

[0490] The terminal receives selection criteria sent from the server. The input includes the updated selection criteria. Based on this, the terminal performs data sorting on the visual display device in real time. The output is filtered information presented to the user.

[0491] Step 5:

[0492] The user accesses information using the terminal and selects a specific mode. Input includes user configuration information and pre-processed data. Based on the user's selection, the terminal changes and controls the type of information presented, displaying only the most relevant content. The output provides the user with appropriately selected and prioritized information.

[0493] In this way, through the specific data processing and calculations performed at each step, the entire system effectively filters information, creating a flow that provides users with a comfortable information experience.

[0494] 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.

[0495] This invention is a system that dynamically adjusts information selection criteria so that users can browse information on a communication network without experiencing discomfort. This system consists of a server, terminal devices, users, and an emotion engine.

[0496] The server collects information accessed by users via the communication network. This includes a wide variety of content, such as text, images, videos, and links. The server receives feedback from users and analyzes it to extract informational features. This feature analysis particularly considers elements that may cause discomfort.

[0497] Subsequently, the server uses an emotion engine to identify emotions from user feedback and browsing history. The emotion engine might, for example, determine that the user is feeling "stressed." Based on this emotion analysis, a machine learning algorithm generates and updates selection criteria, enabling the selection of customized information that reflects the user's emotional state.

[0498] The terminal device receives selection criteria transmitted from the server and uses them to filter information in real time. During the filtering process, the terminal device considers the user's emotional state and hides information that may cause discomfort. Furthermore, positive information identified by the emotion engine is displayed preferentially.

[0499] A concrete example is when a user wants to relax after spending a long time at work. The emotion engine analyzes this request and identifies the emotion of "wanting to relax." The server then generates selection criteria accordingly, and the terminal device prioritizes displaying content that is helpful for relaxation. Conversely, content that causes stress is filtered out to reduce the user's stress.

[0500] In this way, the server, emotion engine, and terminal device work together to provide an information experience adapted to the user's emotional state. This maintains a comfortable information environment for the user and improves the quality of information browsing.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The server collects information accessed by users via the communication network. This information includes text, images, and videos, and is recorded in a database as the user's access history.

[0504] Step 2:

[0505] The server receives feedback from users and uses this feedback to evaluate the collected information. This feedback includes emotional responses such as "unpleasant" or "enjoyable."

[0506] Step 3:

[0507] The server uses an emotion engine to analyze received feedback and access history to identify the user's emotional state. For example, it can detect emotions such as "high stress" or "relaxed."

[0508] Step 4:

[0509] The server generates or updates selection criteria using machine learning algorithms based on the user's emotional state. This enables information selection that is appropriate to the user's emotions.

[0510] Step 5:

[0511] The terminal receives selection criteria sent from the server. These selection criteria are used to control the information displayed by the terminal.

[0512] Step 6:

[0513] The device scans information in real time according to selection criteria and hides unpleasant information identified by the sentiment engine. At the same time, positive information is prioritized.

[0514] Step 7:

[0515] Users view the information displayed on their devices and provide further feedback. This feedback will be used for future sentiment analysis and updating of selection criteria.

[0516] Step 8:

[0517] Users can change the status settings via their device as needed. For example, they can switch from "work mode" to "relax mode," and the emotion engine adjusts the information displayed accordingly.

[0518] Through this series of steps, the system provides a comfortable information environment optimized for the user's emotional state.

[0519] (Example 2)

[0520] 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."

[0521] In today's world, the overwhelming influx of information means that users are increasingly exposed to inappropriate or offensive information on communication networks. As a result, users may experience discomfort or stress during the information acquisition process. Furthermore, providing information tailored to individual emotional states becomes difficult, potentially leading to a decline in the quality of the user experience. There is a need to address these challenges and provide systems that allow users to acquire information comfortably.

[0522] 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.

[0523] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing emotion-based evaluations from users and extracting features that cause discomfort, and means for generating and updating selection criteria using a machine learning algorithm based on the extracted features. This makes it possible to provide customized information according to the emotional state of the user.

[0524] "Users" refer to individuals or organizations that acquire and use data using information systems.

[0525] A "communication network" refers to the entire system that constitutes the infrastructure for sending and receiving data, and includes the internet and intranets.

[0526] "Data" refers to all media formats used as information, including text, images, videos, and links.

[0527] An "emotion engine" refers to software or technology used to analyze user feedback and behavioral history to identify their emotional state.

[0528] A "machine learning algorithm" refers to a method for automating the process of learning patterns from data and making decisions.

[0529] "Selection criteria" refer to the rules and conditions used to select the information to be provided, and these are set with consideration for the user's emotional state.

[0530] A "terminal" is a device used to display or operate information, and includes personal computers and smartphones.

[0531] This invention is an information processing system that enables users to comfortably acquire information. Specific embodiments of this system are shown below.

[0532] Servers are responsible for collecting data accessed by users via communication networks. To effectively collect data, servers utilize web crawlers and APIs. In this process, servers handle a variety of data formats, including text, images, videos, and links.

[0533] The server receives feedback from users and analyzes it using an emotion engine. The emotion engine combines natural language processing (NLP) and machine learning algorithms to classify the user's emotional state into categories such as "stress" or "desire for relaxation." Specifically, it uses deep learning models to identify emotions.

[0534] Based on the results of this sentiment analysis, the server generates and updates selection criteria. These selection criteria are a set of rules for providing information that is tailored to the user's emotional state. The selection criteria are dynamically updated by a machine learning algorithm, prioritizing the presentation of positive information and filtering out negative information.

[0535] The device receives selection criteria sent from the server and performs data filtering in real time. The device takes the user's emotional state into consideration and hides data that may cause discomfort. Positive data is displayed preferentially, allowing the user to enjoy a less stressful information environment.

[0536] As a concrete example, consider a user who spends long hours at work and wants to relax. When the emotion engine identifies the emotion of "wanting to relax," the server generates selection criteria corresponding to this emotion, and the device prioritizes displaying content that helps with relaxation. For example, this could include displaying calming music or natural scenery.

[0537] An example of a prompt to input into the generative AI model is, "Suggest content to help the user relax after a long day of work." By coordinating the server, terminal, and emotion engine, information tailored to the user's individual emotional state can be provided.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The server collects data accessed by users through the communication network. This input data includes text, images, videos, and links collected using web crawlers and APIs. The server structures the collected data and converts it into a format suitable for the next processing step. The output data is structured data stored in a storage system.

[0541] Step 2:

[0542] The server receives feedback from users. The input data consists of ratings and comments provided by the users. The server inputs this feedback into its sentiment engine and performs sentiment analysis using natural language processing (NLP) techniques. Specifically, it calculates sentiment scores for words through text analysis and identifies the overall emotional state. The output of this step is data indicating the user's emotional state, and is labeled with terms such as "stressed" or "relaxed."

[0543] Step 3:

[0544] The server generates and updates selection criteria using machine learning algorithms based on emotional state data from the emotion engine. Input data includes emotional states and the user's past browsing history. The server processes this data using algorithms to create a set of rules for selecting information relevant to the user. The output of this step is the customized selection criteria.

[0545] Step 4:

[0546] The terminal receives selection criteria sent from the server. The input data consists of these selection criteria. The terminal screens the data in real time, filtering information while considering the user's emotional state. Positive information is displayed preferentially, while unpleasant information is hidden. The output is processed information that appears on the display screen.

[0547] Step 5:

[0548] The user receives customized information through the device. The input data is filtered data displayed by the device. Based on this, the user can enjoy a pleasant information experience. The output of this step is user satisfaction and a comfortable information environment. For example, if the user is seeking relaxation, calming music or nature videos will be displayed.

[0549] (Application Example 2)

[0550] 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."

[0551] The various types of information provided over communication networks may not always align with a user's emotional state, which can cause discomfort. Therefore, there is a need for a system that can dynamically select and present information in accordance with the user's emotions and preferences.

[0552] 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. In this invention, the server includes means for collecting information, means for analyzing user evaluations and extracting characteristics, and means for dynamically displaying suitable suggestions based on the user's emotional state. This makes it possible to provide information that is in line with the user's emotional state and preferences.

[0553] A "communication network" refers to the infrastructure for sending and receiving data, and includes various connection methods, such as the internet.

[0554] "Data" refers to a collection of information transferred over a communication network, and includes various forms such as text, images, videos, and links.

[0555] "Evaluation" refers to feedback and comments provided by users, which are used to measure the usefulness and emotional impact of the data.

[0556] "Characteristics" refer to specific elements and attributes of data extracted from user evaluations, including those that may cause discomfort.

[0557] "Selection criteria" are a set of conditions used to select appropriate data that reflect the emotional state of the user.

[0558] A "terminal device" is a hardware device used by users to receive and view data, and includes smartphones and personal computers.

[0559] "Emotional state" refers to the user's mental state or mood, and includes things like stress and relaxation.

[0560] "Suggestions" refer to information presented by the system based on the user's emotional state, including options for meals and content.

[0561] This invention is a system that optimizes the provision of information tailored to the emotional state of users in a communication network. The system collects and analyzes data and prioritizes the display of information appropriate to the user's emotional state, thereby providing a comfortable information environment. A specific embodiment of this system is described below.

[0562] The server stores data (text, images, videos, etc.) collected via the communication network and analyzes feedback provided by users. The analysis uses sentiment analysis models such as TensorFlow to identify features that cause discomfort from the evaluation. Then, based on the identified features, selection criteria are optimized using machine learning algorithms.

[0563] The device uses these optimized selection criteria to filter data in real time. Through the Android platform provided by Google and the iOS platform provided by Apple, it dynamically displays suggestions tailored to the user's emotional state. For example, a user who wants to relax will be recommended menus with relaxing effects. The appropriateness of the suggestions is then used to improve future optimizations based on user feedback.

[0564] For example, in a food delivery app, if a user enters "I want to relax today," the emotion engine analyzes this emotional state and displays foods that align with relaxation with a higher priority. If the user orders "spicy food" from their order history, similar suggestions will be made later based on the user's preferences.

[0565] An example of a prompt for a generative AI model is, "Generate appropriate food delivery options based on the user's past feedback and emotional state." Based on this prompt, the AI ​​will make appropriate suggestions.

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server collects data viewed by users via the communication network. This data includes text, images, videos, etc. The server stores this data in a database and simultaneously saves access log data. MySQL is used for this database.

[0569] Step 2:

[0570] The server receives feedback from users and analyzes their evaluations. The feedback input consists of users expressing their feelings about the data in numerical or textual form. To analyze this evaluation data, the server uses Python to perform text mining and natural language processing to extract unpleasant characteristics. The output is the identified unpleasant characteristics.

[0571] Step 3:

[0572] The server performs sentiment analysis based on the extracted characteristics. It uses TensorFlow to execute machine learning algorithms and model the user's emotional state. The input is the extracted characteristics, and the output is the user's emotional state (e.g., stress, relaxation).

[0573] Step 4:

[0574] The server generates and updates selection criteria based on emotional states. Emotional states obtained through machine learning and past selection criterion information are used as input, and optimized selection criteria are output by an algorithm.

[0575] Step 5:

[0576] The terminal receives optimized selection criteria sent from the server. Based on these criteria, the terminal performs data sorting in real time. The input is the selection criteria from the server, and the output is a list of data to be displayed to the user.

[0577] Step 6:

[0578] The user operates the device and receives suggestions that match their emotional state. When the user presses a suggestion button, the device refers to the emotional analysis data and prioritizes displaying suitable information and offers. Emotional analysis data is used as input, and a specific list of suggestions is displayed on the user's screen as output.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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".

[0596] This invention is a system that selects the information users receive on a communication network and provides a comfortable information environment according to individual preferences and circumstances. This system consists of a server, terminal devices, and users who utilize them.

[0597] First, the server collects information through the communication network. The collected information includes various types of content that users access on a daily basis. Furthermore, this server has the function of receiving ratings from users and analyzing them to extract characteristics of the information. In particular, it focuses on characteristics that may evoke discomfort and stores them in a database.

[0598] Next, the server uses machine learning techniques to automatically update selection criteria based on the collected feedback and information characteristics. These selection criteria become important rules for distinguishing between information that users want and information they don't. The server periodically reviews and optimizes these criteria.

[0599] The terminal device receives selection criteria transmitted from the server. Based on these received criteria, the terminal filters information retrieved from the network in real time, hiding unnecessary or offensive information. This filtering process is handled on the terminal, designed to allow users to enjoy a comfortable information experience without being aware of it.

[0600] Users can view information and provide feedback through their devices. They can also change their context settings. These settings reflect what kind of information users want to prioritize and when, and include options such as "on a diet" or "traveling." Based on these settings, the selection criteria are dynamically adjusted, allowing information to be presented exceptionally under specific conditions.

[0601] For example, if a user is on a diet, they can change their status setting to "on a diet." The server then uses this setting to provide criteria for hiding sweets-related information on the device. However, sweets information related to facilities the user plans to visit during their trip will be displayed as an exception.

[0602] In this way, the server and terminal work together, creating an environment where users can acquire information more comfortably. This invention aims to reduce the burden on users and provide a comfortable information browsing experience through this method.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The server collects information accessed by users via the communication network. The collected data includes various types of content, such as text, images, and links.

[0606] Step 2:

[0607] The server receives evaluation data from users. The feedback provided by users includes labels such as "unpleasant" or "interesting," and this is recorded in the database.

[0608] Step 3:

[0609] The server analyzes the received evaluation data and extracts features of information that cause discomfort. These features may include specific keywords or image patterns.

[0610] Step 4:

[0611] The server uses machine learning technology to generate and update selection criteria based on extracted features. This enables information selection tailored to the user's preferences and circumstances.

[0612] Step 5:

[0613] The terminal receives the latest selection criteria sent from the server. These criteria will be used in the next information filtering process.

[0614] Step 6:

[0615] Based on criteria received from the server, the device filters out unnecessary or offensive information from the real-time data it receives and hides it.

[0616] Step 7:

[0617] Users contribute to improving the accuracy of selection criteria by viewing the displayed information and providing new feedback.

[0618] Step 8:

[0619] Users can change the status settings as needed. When the status changes, the selection criteria are dynamically adjusted based on those settings, and the displayed information is also updated.

[0620] Through this series of processes, the system is continuously optimized, providing users with a comfortable information environment.

[0621] (Example 1)

[0622] 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".

[0623] In modern communication networks, users have access to a vast amount of information, but this often includes information that is unpleasant or unnecessary to them. Therefore, users often spend a lot of time and effort trying to access the information they want. To solve this problem, a means is needed to select and provide appropriate information according to each user's individual preferences and circumstances.

[0624] 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.

[0625] In this invention, the server includes means for collecting information related to user activities via a communication network and obtaining content from information sources; means for analyzing evaluation information and extracting trends and features of the information using natural language processing technology; and means for updating optimized selection criteria based on the collected evaluation information and feature data using machine learning algorithms. This enables users to enjoy a comfortable information environment tailored to their preferences and circumstances.

[0626] A "communication network" refers to the entire network infrastructure used for sending and receiving information, and is the medium through which data is exchanged between computer devices.

[0627] An "information processing device" is a device that performs analysis and sorting based on received data, and its role is to provide users with appropriate information.

[0628] "Evaluation information" refers to the evaluations and feedback that users provide to specific data or content, and is used to optimize the system's selection criteria.

[0629] "Natural language processing technology" refers to the technology necessary for computers to understand and process the language that humans use on a daily basis, and is used to extract trends and characteristics of information.

[0630] A "machine learning algorithm" is a method for analyzing large amounts of data and automatically learning patterns and rules, and is used to optimize the selection criteria of a system.

[0631] "Selection criteria" are standards set to distinguish between information that is useful to the user and information that is unnecessary or unpleasant, and they serve as the rules by which the system selects information.

[0632] "Optimization" refers to adjusting systems and processes to achieve the greatest effect for a specific purpose, and is done to improve the user experience.

[0633] This system is primarily composed of three elements: a server, a terminal, and a user. In implementing the invention, the server plays a crucial role in collecting information and selecting information based on the user's preferences.

[0634] The server collects content from various sources via a communication network. The collected information is stored in a database and becomes the subject of analysis. The server uses natural language processing technology to extract trends and characteristics of the information, and then uses machine learning algorithms based on the evaluation information to optimize the selection criteria. This process could involve using programming languages ​​such as Python and leveraging machine learning libraries such as TensorFlow and Scikit-learn.

[0635] The terminal filters received information in real time based on selection criteria sent from the server. Only appropriate information is presented to the user on the terminal, while unpleasant or unnecessary information is hidden. This process runs smoothly by utilizing the terminal's processing power.

[0636] Users can utilize the information obtained through their devices and provide feedback as needed. This feedback is sent to the server and used to review the selection criteria for the future. Users can also set statuses such as "on a diet" or "traveling," which dynamically adjusts the information selection criteria. These status settings can be easily changed through the device interface.

[0637] For example, when a user configures their travel settings, the server reflects this information, selects recommended destinations, and sends them to the user's device. Users can specifically utilize this feature by entering a prompt such as, "Please provide recommendations for places I plan to visit on my next trip."

[0638] This invention aims to provide an optimized information environment for users, achieving comfortable information access through a generative AI model.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] The server collects content from information sources via a communication network. It is given feed information and URLs of publicly available content as input. The server collects this using crawling techniques and stores it in a database. The output is raw, unprocessed data.

[0642] Step 2:

[0643] The server analyzes the collected information using natural language processing techniques. The input is the raw data collected in step 1. The server uses text analysis techniques to extract information features and topics and analyze the trends of the information. The output is an annotated dataset with metadata indicating the characteristics and trends of the information.

[0644] Step 3:

[0645] The server receives evaluation information provided by the user and optimizes it using a machine learning algorithm. The inputs are user feedback and analysis data from step 2. The server uses this feedback to update the machine learning model and readjust the selection criteria. The output is the updated selection criteria, which are stored in the database.

[0646] Step 4:

[0647] The terminal receives selection criteria sent from the server. The input is the updated selection criteria. Based on this, the terminal filters information retrieved from the network in real time and displays only the information relevant to the user. The output is the filtered information presented to the user.

[0648] Step 5:

[0649] Users provide feedback on the information provided using their terminals. The input is the information displayed on the terminal. User feedback is sent to the server and used to revise the selection criteria. The output is evaluation information stored on the server. In this step, the information environment can be dynamically optimized using context settings.

[0650] (Application Example 1)

[0651] 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".

[0652] The amount of information that individual users receive through communication networks is enormous, and it often contains content that can be offensive. This problem can hinder convenience and impede a comfortable information experience for users. Furthermore, because there is a lack of mechanisms to select and provide the most appropriate information according to the user's specific situation, users may not be able to find the information they are looking for due to information overload.

[0653] 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.

[0654] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing user evaluations and extracting features that cause discomfort, means for updating selection criteria that have been learned and optimized based on the extracted features, means for selecting data based on the selection criteria in a visual display device and removing unpleasant information, means for adjusting the selection criteria according to user settings, and means for the user to select a mode appropriate to the situation and control the type of information presented based on that. As a result, users receive information customized according to their situation and preferences at the time, and are freed from unnecessary information, enabling a comfortable and efficient information experience.

[0655] A "user" is an individual or organization that receives information via a communication network.

[0656] A "communication network" is an infrastructure for sending and receiving information, and includes technical means such as the internet and local networks.

[0657] "Data" refers to various types of information that users access, including text, images, and videos.

[0658] "Evaluation" refers to a user's reaction or feedback to the information they receive, including opinions on whether they liked or disliked it.

[0659] "Discomfort" refers to negative emotions or impressions that users experience when receiving information.

[0660] "Features" refer to specific patterns or properties contained in data, including elements that may cause discomfort.

[0661] "Optimized selection criteria" are the most effective information filtering rules tailored to the user's preferences and circumstances.

[0662] A "visual display device" is a device that presents information visually, and includes smartphones and eyeglasses.

[0663] "Selection" is the process of choosing necessary information according to specific criteria and removing unnecessary or offensive information.

[0664] "Settings" refers to the operations and options that allow users to customize the conditions and priorities for receiving information.

[0665] A "mode" is a function that temporarily changes the operation of the entire system according to the user's specific purpose.

[0666] The system that realizes this invention consists of a server, terminal devices, and users. The server collects diverse data through a communication network and analyzes the users' evaluations of that data. Based on the evaluations, it uses machine learning techniques to extract features from the data, focusing particularly on elements that are likely to cause discomfort. Based on these extracted features, the server generates optimized selection criteria and transmits them to the terminal devices.

[0667] The terminal device, acting as a visual display device, selects data acquired in real time based on selection criteria received from the server. This ensures that users can enjoy a comfortable information environment without receiving unnecessary or unpleasant information. Furthermore, by selecting a specific "mode," information appropriate to the current situation is prioritized. This mode switching allows for customization, for example, prioritizing educational content and displaying entertainment information sparingly when the user wants to concentrate on studying.

[0668] For example, if the user is a university student, they can select "Study Mode" on their smartphone app during exam periods. When this mode is turned on, information useful for exams and academic news are prioritized. In addition, by generating a prompt for the generating AI model such as, "Based on the user's current settings, set the filtering criteria for the necessary content," it is possible to create more appropriate selection criteria.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The server collects diverse data accessed by users through the communication network. Input includes publicly available information on the network. The server analyzes this data and awaits user feedback. The output is a set of collected data.

[0672] Step 2:

[0673] The server receives and analyzes user ratings. User rating data and collected data are used as input. In this step, machine learning algorithms are used to extract features from the data and identify features that may cause discomfort. The output is a list of extracted features.

[0674] Step 3:

[0675] The server generates optimized selection criteria based on the extracted features and updates the selection criteria. The feature list and existing selection criteria are used as input. A generative AI model is used to dynamically optimize the selection criteria. New selection criteria are generated as output.

[0676] Step 4:

[0677] The terminal receives selection criteria sent from the server. The input includes the updated selection criteria. Based on this, the terminal performs data sorting on the visual display device in real time. The output is filtered information presented to the user.

[0678] Step 5:

[0679] The user accesses information using the terminal and selects a specific mode. Input includes user configuration information and pre-processed data. Based on the user's selection, the terminal changes and controls the type of information presented, displaying only the most relevant content. The output provides the user with appropriately selected and prioritized information.

[0680] In this way, through the specific data processing and calculations performed at each step, the entire system effectively filters information, creating a flow that provides users with a comfortable information experience.

[0681] 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.

[0682] This invention is a system that dynamically adjusts information selection criteria so that users can browse information on a communication network without experiencing discomfort. This system consists of a server, terminal devices, users, and an emotion engine.

[0683] The server collects information accessed by users via the communication network. This includes a wide variety of content, such as text, images, videos, and links. The server receives feedback from users and analyzes it to extract informational features. This feature analysis particularly considers elements that may cause discomfort.

[0684] Subsequently, the server uses an emotion engine to identify emotions from user feedback and browsing history. The emotion engine might, for example, determine that the user is feeling "stressed." Based on this emotion analysis, a machine learning algorithm generates and updates selection criteria, enabling the selection of customized information that reflects the user's emotional state.

[0685] The terminal device receives selection criteria transmitted from the server and uses them to filter information in real time. During the filtering process, the terminal device considers the user's emotional state and hides information that may cause discomfort. Furthermore, positive information identified by the emotion engine is displayed preferentially.

[0686] A concrete example is when a user wants to relax after spending a long time at work. The emotion engine analyzes this request and identifies the emotion of "wanting to relax." The server then generates selection criteria accordingly, and the terminal device prioritizes displaying content that is helpful for relaxation. Conversely, content that causes stress is filtered out to reduce the user's stress.

[0687] In this way, the server, emotion engine, and terminal device work together to provide an information experience adapted to the user's emotional state. This maintains a comfortable information environment for the user and improves the quality of information browsing.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The server collects information accessed by users via the communication network. This information includes text, images, and videos, and is recorded in a database as the user's access history.

[0691] Step 2:

[0692] The server receives feedback from users and uses this feedback to evaluate the collected information. This feedback includes emotional responses such as "unpleasant" or "enjoyable."

[0693] Step 3:

[0694] The server uses an emotion engine to analyze received feedback and access history to identify the user's emotional state. For example, it can detect emotions such as "high stress" or "relaxed."

[0695] Step 4:

[0696] The server generates or updates selection criteria using machine learning algorithms based on the user's emotional state. This enables information selection that is appropriate to the user's emotions.

[0697] Step 5:

[0698] The terminal receives selection criteria sent from the server. These selection criteria are used to control the information displayed by the terminal.

[0699] Step 6:

[0700] The device scans information in real time according to selection criteria and hides unpleasant information identified by the sentiment engine. At the same time, positive information is prioritized.

[0701] Step 7:

[0702] Users view the information displayed on their devices and provide further feedback. This feedback will be used for future sentiment analysis and updating of selection criteria.

[0703] Step 8:

[0704] Users can change the status settings via their device as needed. For example, they can switch from "work mode" to "relax mode," and the emotion engine adjusts the information displayed accordingly.

[0705] Through this series of steps, the system provides a comfortable information environment optimized for the user's emotional state.

[0706] (Example 2)

[0707] 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".

[0708] In today's world, the overwhelming influx of information means that users are increasingly exposed to inappropriate or offensive information on communication networks. As a result, users may experience discomfort or stress during the information acquisition process. Furthermore, providing information tailored to individual emotional states becomes difficult, potentially leading to a decline in the quality of the user experience. There is a need to address these challenges and provide systems that allow users to acquire information comfortably.

[0709] 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.

[0710] In this invention, the server includes means for collecting data accessed by users on a communication network, means for analyzing emotion-based evaluations from users and extracting features that cause discomfort, and means for generating and updating selection criteria using a machine learning algorithm based on the extracted features. This makes it possible to provide customized information according to the emotional state of the user.

[0711] "Users" refer to individuals or organizations that acquire and use data using information systems.

[0712] A "communication network" refers to the entire system that constitutes the infrastructure for sending and receiving data, and includes the internet and intranets.

[0713] "Data" refers to all media formats used as information, including text, images, videos, and links.

[0714] An "emotion engine" refers to software or technology used to analyze user feedback and behavioral history to identify their emotional state.

[0715] A "machine learning algorithm" refers to a method for automating the process of learning patterns from data and making decisions.

[0716] "Selection criteria" refer to the rules and conditions used to select the information to be provided, and these are set with consideration for the user's emotional state.

[0717] A "terminal" is a device used to display or operate information, and includes personal computers and smartphones.

[0718] This invention is an information processing system that enables users to comfortably acquire information. Specific embodiments of this system are shown below.

[0719] Servers are responsible for collecting data accessed by users via communication networks. To effectively collect data, servers utilize web crawlers and APIs. In this process, servers handle a variety of data formats, including text, images, videos, and links.

[0720] The server receives feedback from users and analyzes it using an emotion engine. The emotion engine combines natural language processing (NLP) and machine learning algorithms to classify the user's emotional state into categories such as "stress" or "desire for relaxation." Specifically, it uses deep learning models to identify emotions.

[0721] Based on the results of this sentiment analysis, the server generates and updates selection criteria. These selection criteria are a set of rules for providing information that is tailored to the user's emotional state. The selection criteria are dynamically updated by a machine learning algorithm, prioritizing the presentation of positive information and filtering out negative information.

[0722] The device receives selection criteria sent from the server and performs data filtering in real time. The device takes the user's emotional state into consideration and hides data that may cause discomfort. Positive data is displayed preferentially, allowing the user to enjoy a less stressful information environment.

[0723] As a concrete example, consider a user who spends long hours at work and wants to relax. When the emotion engine identifies the emotion of "wanting to relax," the server generates selection criteria corresponding to this emotion, and the device prioritizes displaying content that helps with relaxation. For example, this could include displaying calming music or natural scenery.

[0724] An example of a prompt to input into the generative AI model is, "Suggest content to help the user relax after a long day of work." By coordinating the server, terminal, and emotion engine, information tailored to the user's individual emotional state can be provided.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The server collects data accessed by users through the communication network. This input data includes text, images, videos, and links collected using web crawlers and APIs. The server structures the collected data and converts it into a format suitable for the next processing step. The output data is structured data stored in a storage system.

[0728] Step 2:

[0729] The server receives feedback from users. The input data consists of ratings and comments provided by the users. The server inputs this feedback into its sentiment engine and performs sentiment analysis using natural language processing (NLP) techniques. Specifically, it calculates sentiment scores for words through text analysis and identifies the overall emotional state. The output of this step is data indicating the user's emotional state, and is labeled with terms such as "stressed" or "relaxed."

[0730] Step 3:

[0731] The server generates and updates selection criteria using machine learning algorithms based on emotional state data from the emotion engine. Input data includes emotional states and the user's past browsing history. The server processes this data using algorithms to create a set of rules for selecting information relevant to the user. The output of this step is the customized selection criteria.

[0732] Step 4:

[0733] The terminal receives selection criteria sent from the server. The input data consists of these selection criteria. The terminal screens the data in real time, filtering information while considering the user's emotional state. Positive information is displayed preferentially, while unpleasant information is hidden. The output is processed information that appears on the display screen.

[0734] Step 5:

[0735] The user receives customized information through the device. The input data is filtered data displayed by the device. Based on this, the user can enjoy a pleasant information experience. The output of this step is user satisfaction and a comfortable information environment. For example, if the user is seeking relaxation, calming music or nature videos will be displayed.

[0736] (Application Example 2)

[0737] 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".

[0738] The various types of information provided over communication networks may not always align with a user's emotional state, which can cause discomfort. Therefore, there is a need for a system that can dynamically select and present information in accordance with the user's emotions and preferences.

[0739] 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. In this invention, the server includes means for collecting information, means for analyzing user evaluations and extracting characteristics, and means for dynamically displaying suitable suggestions based on the user's emotional state. This makes it possible to provide information that is in line with the user's emotional state and preferences.

[0740] A "communication network" refers to the infrastructure for sending and receiving data, and includes various connection methods, such as the internet.

[0741] "Data" refers to a collection of information transferred over a communication network, and includes various forms such as text, images, videos, and links.

[0742] "Evaluation" refers to feedback and comments provided by users, which are used to measure the usefulness and emotional impact of the data.

[0743] "Characteristics" refer to specific elements and attributes of data extracted from user evaluations, including those that may cause discomfort.

[0744] "Selection criteria" are a set of conditions used to select appropriate data that reflect the emotional state of the user.

[0745] A "terminal device" is a hardware device used by users to receive and view data, and includes smartphones and personal computers.

[0746] "Emotional state" refers to the user's mental state or mood, and includes things like stress and relaxation.

[0747] "Suggestions" refer to information presented by the system based on the user's emotional state, including options for meals and content.

[0748] This invention is a system that optimizes the provision of information tailored to the emotional state of users in a communication network. The system collects and analyzes data and prioritizes the display of information appropriate to the user's emotional state, thereby providing a comfortable information environment. A specific embodiment of this system is described below.

[0749] The server stores data (text, images, videos, etc.) collected via the communication network and analyzes feedback provided by users. The analysis uses sentiment analysis models such as TensorFlow to identify features that cause discomfort from the evaluation. Then, based on the identified features, selection criteria are optimized using machine learning algorithms.

[0750] The device uses these optimized selection criteria to filter data in real time. Through the Android platform provided by Google and the iOS platform provided by Apple, it dynamically displays suggestions tailored to the user's emotional state. For example, a user who wants to relax will be recommended menus with relaxing effects. The appropriateness of the suggestions is then used to improve future optimizations based on user feedback.

[0751] For example, in a food delivery app, if a user enters "I want to relax today," the emotion engine analyzes this emotional state and displays foods that align with relaxation with a higher priority. If the user orders "spicy food" from their order history, similar suggestions will be made later based on the user's preferences.

[0752] An example of a prompt for a generative AI model is, "Generate appropriate food delivery options based on the user's past feedback and emotional state." Based on this prompt, the AI ​​will make appropriate suggestions.

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The server collects data viewed by users via the communication network. This data includes text, images, videos, etc. The server stores this data in a database and simultaneously saves access log data. MySQL is used for this database.

[0756] Step 2:

[0757] The server receives feedback from users and analyzes their evaluations. The feedback input consists of users expressing their feelings about the data in numerical or textual form. To analyze this evaluation data, the server uses Python to perform text mining and natural language processing to extract unpleasant characteristics. The output is the identified unpleasant characteristics.

[0758] Step 3:

[0759] The server performs sentiment analysis based on the extracted characteristics. It uses TensorFlow to execute machine learning algorithms and model the user's emotional state. The input is the extracted characteristics, and the output is the user's emotional state (e.g., stress, relaxation).

[0760] Step 4:

[0761] The server generates and updates selection criteria based on emotional states. Emotional states obtained through machine learning and past selection criterion information are used as input, and optimized selection criteria are output by an algorithm.

[0762] Step 5:

[0763] The terminal receives optimized selection criteria sent from the server. Based on these criteria, the terminal performs data sorting in real time. The input is the selection criteria from the server, and the output is a list of data to be displayed to the user.

[0764] Step 6:

[0765] The user operates the device and receives suggestions that match their emotional state. When the user presses a suggestion button, the device refers to the emotional analysis data and prioritizes displaying suitable information and offers. Emotional analysis data is used as input, and a specific list of suggestions is displayed on the user's screen as output.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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."

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means of collecting information accessed by users on a communication network,

[0790] A method for analyzing user reviews and extracting features that cause discomfort,

[0791] A means for updating the selection criteria by performing learning based on the extracted features,

[0792] A terminal device provides means for selecting information based on the aforementioned selection criteria and removing unpleasant information,

[0793] A means for adjusting the selection criteria according to the user's settings,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, which can present information without being affected under specific conditions, depending on the user's situation.

[0797] (Claim 3)

[0798] The system according to claim 1, which updates the sorting results in real time based on information received by the terminal device.

[0799] "Example 1"

[0800] (Claim 1)

[0801] A means of collecting information related to user activities via a communication network and obtaining content from information sources,

[0802] A means of analyzing evaluation information and extracting trends and characteristics of the information using natural language processing technology,

[0803] A means for updating optimized selection criteria based on collected evaluation information and feature data using a machine learning algorithm,

[0804] An information processing device includes means for selecting information based on the aforementioned selection criteria and hiding unnecessary or unpleasant information,

[0805] A means for dynamically adjusting the selection criteria according to the user's settings,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which can adaptively change the selection criteria under specific conditions and present information by setting specific circumstances.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the information processing device updates the sorting results in real time based on the received information.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] A means of collecting data accessed by users on a communication network,

[0814] A method for analyzing user reviews and extracting features that cause discomfort,

[0815] A means for updating the selection criteria by performing learning based on the extracted features,

[0816] A means for selecting data based on the aforementioned selection criteria and removing unpleasant information in a visual display device,

[0817] A means for adjusting the selection criteria according to the user's settings,

[0818] A means by which the user selects a mode appropriate to the situation and controls the type of information presented based on that selection,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, which can present data without being affected under specific conditions, depending on the user's situation.

[0822] (Claim 3)

[0823] The system according to claim 1, which updates the sorting results in real time based on data received by a visual display device.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A means of collecting data accessed by users on a communication network,

[0827] A means of analyzing user-based evaluations and extracting features that cause discomfort,

[0828] A means for generating and updating selection criteria using a machine learning algorithm based on the extracted features,

[0829] A means for selecting data on a terminal based on the aforementioned selection criteria, removing unpleasant data and prioritizing the display of positive data,

[0830] A means for dynamically adjusting the selection criteria according to the user's emotional state,

[0831] An information processing system that includes this.

[0832] (Claim 2)

[0833] The information processing system according to claim 1, which can use an emotion engine to present data without being affected by the user's emotional state under specific conditions.

[0834] (Claim 3)

[0835] The information processing system according to claim 1, further comprising a function to update the sorting results in real time based on data received by the terminal.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] A means of collecting data accessed by users on a communication network,

[0839] A means of analyzing user feedback and extracting characteristics that cause discomfort,

[0840] A means for updating the selection criteria by performing learning based on the extracted characteristics,

[0841] A terminal device provides means for selecting data based on the aforementioned selection criteria and removing unwanted data,

[0842] A means for adjusting the selection criteria according to the user's settings,

[0843] A means of dynamically displaying suitable suggestions based on the user's emotional state,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which can present data without being affected under specific conditions, depending on the user's situation.

[0847] (Claim 3)

[0848] The system according to claim 1, which updates the selection results in real time based on data received by the terminal device and adjusts the suggestions based on the emotional state. [Explanation of symbols]

[0849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data accessed by users on a communication network, A method for analyzing user reviews and extracting features that cause discomfort, A means for updating the selection criteria by performing learning based on the extracted features, A means for selecting data based on the aforementioned selection criteria and removing unpleasant information in a visual display device, A means for adjusting the selection criteria according to the user's settings, A means by which the user selects a mode appropriate to the situation and controls the type of information presented based on that selection, A system that includes this.

2. The system according to claim 1, which can present data without being affected under specific conditions, depending on the user's situation.

3. The system according to claim 1, which updates the sorting results in real time based on data received by a visual display device.

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