system

Abstract

We provide the system. [Solution] A means by which a terminal collects user access history data, Means for sending the aforementioned access history data to the server, A means for statistically analyzing the access history data using a server-generated AI model and evaluating the bias in user information acquisition, A means of visualizing the evaluation results of the aforementioned bias and presenting them to the user, A means of presenting alternative sources of information for information that users may be biased towards, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

[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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

[0006] A "terminal" is an electronic device used by a user to input or retrieve information, and includes devices such as smartphones and computers.

[0007] "User" refers to an individual or organization that uses this system to obtain information.

[0008] "Access history data" refers to records of information about websites that users have accessed and applications they have used in the past.

[0009] A "server" is a central computer system that receives and processes data sent from terminals.

[0010] "Generative AI models" refer to artificial intelligence technologies used to analyze data, and include statistical methods and machine learning algorithms.

[0011] "Statistical analysis" is the process of evaluating the characteristics and trends of data using mathematical methods based on that data.

[0012] "Information bias" refers to a state in which users rely on specific information sources or opinions and do not adequately acquire other perspectives or information.

[0013] "Visualization" refers to the visual display of data and analysis results using graphs and diagrams, making them easily understandable to users.

[0014] "Alternative information sources" refer to multiple sources of information that provide users with different perspectives and opinions in order to eliminate information bias. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention includes a system for identifying information bias and providing diverse information sources. The system uses terminals, servers, and generative AI models to enable users to obtain unbiased information.

[0037] First, the device collects the user's web browsing and application usage history. This includes the URLs of visited websites, the time spent on each site, and the categories of content accessed. This data is securely transferred to the server with the user's permission.

[0038] The server uses a generative AI model to analyze the received access history data. Specifically, it identifies the type of information source (news, blogs, social media, etc.) and access patterns that are biased towards specific opinions or stances. This analysis helps determine whether users are dependent on specific categories or information sources.

[0039] The analysis results are visualized by the server and presented to the user. Users can intuitively understand their own information acquisition trends. Specifically, pie charts and bar graphs are used to show which information sources are used most frequently.

[0040] Next, the server suggests alternative information sources based on the user's information-gathering biases. For example, if it determines that the user is biased towards a particular news site, the server will present a list of other reliable news sites as recommendations. This suggestion serves as a guide for the user to obtain information from different perspectives.

[0041] Ultimately, by accepting this proposal and utilizing new sources of information, users can improve their information-gathering balance. This will prevent the formation of misunderstandings and biases, and provide an environment that promotes a broader and more objective understanding of information.

[0042] As described above, by implementing this invention, users can autonomously and proactively obtain diverse information, thereby reducing the risks associated with information bias. A specific example would be a case where, if a user only obtains political news from a particular site, the system recommends other neutral news sites, resulting in the user obtaining more balanced information.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device collects the user's browsing history and app usage data. This includes the URLs of websites visited, the time spent browsing, and the categories of content accessed. This provides information about the sources the user is accessing.

[0046] Step 2:

[0047] The device encrypts the data it collects and sends it to the server. Secure communication protocols are used for transmission to protect user privacy. Data is transmitted regularly (e.g., daily or weekly) to ensure the server receives the most up-to-date information.

[0048] Step 3:

[0049] The server passes the received data to a generating AI model to begin analysis. The AI ​​model statistically processes the data to calculate the access frequency for specific categories and information sources. This analysis reveals users' information acquisition patterns.

[0050] Step 4:

[0051] The server visualizes the bias in users' information acquisition based on the analysis results. The analysis results are visualized as graphs and charts, allowing users to intuitively understand their own information acquisition tendencies.

[0052] Step 5:

[0053] The server suggests specific alternative information sources. If biased information acquisition is detected, it creates and presents a list of other reliable sources for that category. At this stage, recommendations are made to provide diverse perspectives.

[0054] Step 6:

[0055] The user accepts the suggestion from the server and accesses new information sources. The user opens the suggested information sources in a browser or app and obtains information from different perspectives. As a result, the user can improve the balance of information and gain a multifaceted perspective.

[0056] (Example 1)

[0057] 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."

[0058] In modern society, the information sources that users access via the internet are extremely diverse, but this diversity can conversely lead to information bias. By frequently accessing specific information sources, users may unknowingly receive only biased information, resulting in the formation of misunderstandings and prejudices. It is necessary to improve this situation and enable users to obtain more multifaceted and balanced information.

[0059] 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.

[0060] In this invention, the server includes means for statistically analyzing access history information using a generative AI model and evaluating the user's information acquisition bias, means for visualizing and presenting the evaluation results to the user, and means for presenting alternative information sources to biased information sources. This enables the user to recognize their own information acquisition bias and obtain information from diverse information sources.

[0061] A "terminal" is an electronic device used by a user to collect access history information.

[0062] "Access history information" refers to data that includes information about the websites a user has visited and the content they have viewed.

[0063] A "server" is a computer system that receives and analyzes access history information sent from terminals.

[0064] A "generative AI model" is an artificial intelligence algorithm used to analyze access history information, designed to identify bias in the information.

[0065] "Information bias" refers to the bias in information that arises when users access information from a particular source.

[0066] "Visualization" is a method of making information intuitively understandable to users by visually representing the results of the analysis.

[0067] An "alternative information source" is another information source that offers a different perspective or content from the information source that the user is currently referring to.

[0068] A "report" is a document that summarizes the results of an analysis regarding the bias in the information retrieved by the server.

[0069] This invention is a system that evaluates bias in information acquisition and provides users with diverse information. This system mainly consists of a terminal, a server, and a generative AI model.

[0070] First, the device collects the user's website browsing and application usage history. Hardware used for this includes electronic devices such as smartphones, tablets, and computers. The device temporarily stores the access history information obtained through this collection process and securely transmits it to a server.

[0071] Next, the server processes the received access history information and analyzes the data using a generative AI model. This generative AI model utilizes machine learning algorithms to classify information sources and identify biases in user information acquisition. Specifically, it uses natural language processing techniques to analyze the data and determine whether it is biased towards particular news sites or social media.

[0072] The analysis results are presented to the user in an intuitively understandable format by the server. Visualization methods such as pie charts and bar graphs allow users to easily grasp which information sources they rely on most.

[0073] Furthermore, the server has a function that suggests alternative information sources based on the user's information acquisition biases. This allows users to gain different perspectives and broaden the scope of their information acquisition. For example, if a user is biased towards a particular site regarding political news, the server will recommend other neutral news sites. In this case, an example of a prompt message to the generating AI model would be, "Based on this access history, identify the bias in the information sources the user relies on and suggest alternative information sources."

[0074] Ultimately, the system of the present invention allows users to access diverse information with less bias, thereby improving the quality of information acquisition.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device collects the user's website browsing history and application usage history. Specifically, it records the URLs of websites the user accessed, the time spent on each site, and the categories of content viewed. Input is obtained from web browsers and applications, and output is a dataset of collected access history information.

[0078] Step 2:

[0079] The terminal transfers the collected access history information to the server using a secure communication protocol (e.g., HTTPS). The input is the dataset obtained in step 1, and the output is encrypted transmitted data. This protects the user's personal information.

[0080] Step 3:

[0081] The server inputs the received access history information into a generating AI model, which then statistically analyzes the data. Specifically, it uses natural language processing techniques to identify the type and frequency of use of information sources and determine whether they are biased towards a particular opinion. The input is access data sent from the terminal, and the output is the analysis results regarding information bias.

[0082] Step 4:

[0083] The server visualizes the analysis results using a visualization tool and presents them to the user in graph format. The input is the analysis results from step 3, and the output is a pie chart or bar graph displayed in the user interface. This allows the user to intuitively understand their own information acquisition trends.

[0084] Step 5:

[0085] The server performs a function that suggests alternative information sources to the user based on the analysis results. Specifically, it uses a generative AI model to generate prompt sentences and list various information sources. The input is the analysis results from step 3, and the output is the list of alternative information sources presented to the user.

[0086] Step 6:

[0087] The user accesses new information sources based on the alternative information sources presented by the server. Thus, the input is the list of information sources provided in step 5, and the output is the newly obtained information. This increases the diversity of information retrieval for the user.

[0088] (Application Example 1)

[0089] 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."

[0090] In today's information-driven society, users tend to rely heavily on specific information sources when browsing the web or consuming content. This bias can narrow users' perspectives and hinder the broad and objective understanding that diverse information can provide. Therefore, it is crucial to enable users to obtain information from a variety of sources without bias and to make more balanced information judgments.

[0091] 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.

[0092] In this invention, the server includes means for analyzing the user's content access history information using a generative AI model and determining information acquisition bias; means for visually displaying and presenting the results of the bias determination to the user; and means for presenting alternative information sources based on the user's bias and promoting information balance. This enables the user to review their own information acquisition tendencies and obtain information from diverse perspectives.

[0093] A "terminal" is an electronic device operated by a user that has the function of collecting specific information and transmitting it to a data processing device.

[0094] "Content access history information" refers to records of information viewed or consumed by users, specifically data indicating the web pages visited and their content.

[0095] A "data processing device" refers to a computing device that has the function of analyzing received data and calculating results, and that performs specific processing using a generative AI model.

[0096] A "generative AI model" is a form of artificial intelligence technology that analyzes input data to identify specific patterns or trends, and in particular utilizes machine learning algorithms.

[0097] "Information bias" is a concept that describes a state in which users obtain information that is biased towards a particular source or opinion.

[0098] "Visual display" refers to presenting the analyzed results in a format that users can intuitively understand, and includes representations using graphs and charts.

[0099] "Alternative information sources" refer to new, reliable sources of information that are different from the user's current sources of information.

[0100] "Promoting information balance" means helping users obtain information from diverse perspectives and sources, thereby creating a state that enables objective and broad understanding.

[0101] The system implementing this invention mainly consists of a terminal, a data processing device, and a generative AI model. The following describes each component and its respective role.

[0102] First, the device is operated by the user to collect content access history information. This information includes the URLs of the web pages the user accessed, the time spent on each page, and even the article categories. This information is then transmitted to the data processing unit using a secure and efficient data transfer protocol, such as HTTPS.

[0103] Next, the data processing unit analyzes the received access history information using a generative AI model written in Python. This AI model implements machine learning algorithms and is specifically trained to determine user bias in information acquisition. The analysis results show the user's bias, and graph plotting libraries such as Matplotlib are used to visually display these results.

[0104] Subsequently, the data processing unit suggests alternative information sources to the user based on its bias assessment. This is to help the user obtain information from multiple perspectives. The user can visually confirm these suggestions through the terminal and access new information sources.

[0105] For example, if analysis reveals that a user is only browsing the "Gossip News" category, the system will recommend reliable alternative sources of "International News" or "Science News."

[0106] An example of a prompt statement is a process that suggests information sources to correct a user's bias, based on a question such as, "If a user is overly reliant on a particular information source, how would you recommend other, competing, and reliable information sources?"

[0107] In this way, the system of the present invention helps users avoid information bias and obtain information from a more holistic perspective.

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

[0109] Step 1:

[0110] The device collects information about the web pages accessed by the user. Specifically, it records data such as URLs, time spent on each page, and the categories of pages accessed. The input to this process is the user's browsing activity, and the output is structured access history information. The information is temporarily stored within the device and then prepared to be sent to a data processing unit using a secure data transfer protocol.

[0111] Step 2:

[0112] The server receives and stores access history information from terminals. The received data is analyzed using a generative AI model. The input here is access history information transferred from terminals, and the output is the analysis results showing the bias in the user's information acquisition. The generative AI model detects specific patterns and trends, determines whether there is a bias, and records the results.

[0113] Step 3:

[0114] The server visualizes the analysis results. This process uses libraries such as Matplotlib to display bias information as pie charts and bar graphs. The input is the bias analysis results derived in step 2, and the output is visualized graph data. This allows users to intuitively understand their own information acquisition tendencies.

[0115] Step 4:

[0116] Based on the bias assessment, the server presents the user with alternative information sources. This process involves listing reliable sources that differ from those previously relied upon. The input is the biased information data obtained in step 2, and the output is a list of recommended information sources.

[0117] Step 5:

[0118] The terminal displays the analysis results sent from the server and suggestions for alternative information sources to the user. This allows the user to connect to new information sources and obtain a wider variety of information. The input is the visualization data and list of information sources sent from the server, and the output is a link to new information sources that the user can access.

[0119] These steps allow users to improve their biased information intake and gain a holistic perspective on information. For example, if a user only reads "gossip news," the system will suggest new information sources such as "science news." Through this series of actions, the user's information scope is expanded.

[0120] 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.

[0121] This invention provides a system that detects biases in user information acquisition and, by utilizing an emotion engine, provides appropriate information based on the user's emotional state. As a result, users can more easily accept multiple perspectives and deepen their understanding and acceptance of information.

[0122] The system consists primarily of a terminal, a server, and an emotion engine. First, the terminal records the user's web browsing and application usage history. This reveals which sites the user visits and what information they frequently access. The collected data is encrypted to prevent leakage before being sent to the server.

[0123] The server inputs the received data into a generating AI model for statistical analysis. This analysis clarifies biases towards specific information sources or categories, and determines which information is unbalanced.

[0124] Next, the server uses an emotion engine to infer the emotional state based on additional information obtained from the user's device, such as real-time facial recognition, voice analysis, and entered text. Based on this emotion analysis, it considers the user's psychological state when receiving the information and determines an appropriate alternative source of information.

[0125] The server-generated report includes the user's current information-gathering biases and the underlying emotional state. This allows users to re-evaluate their information-gathering behavior and become aware of new perspectives. For example, if a user is feeling anxious about a news topic, the emotion engine will recommend articles with neutral or comforting content that alleviate those feelings.

[0126] Ultimately, the terminal displays suggestions from the server to the user, who can then search for and access the information sources accordingly. This process allows users to acquire information that takes their emotional state into account, providing a more balanced information environment. For example, if a user is stressed by news about a natural disaster, the emotion engine might recommend positive news or helpful guidelines to complement that information.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The device collects the user's web browsing history and application usage data. Specifically, it records the URLs of visited web pages, the time spent browsing, and the categories of information accessed. Through this, it is possible to understand which information sources and content the user primarily uses.

[0130] Step 2:

[0131] The device encrypts the data it collects and sends it to the server. This transmission is performed using secure protocols that protect user privacy.

[0132] Step 3:

[0133] The server analyzes the received data by running it through a generating AI model. The model uses statistical methods to calculate biases towards specific information sources or categories based on the user's information acquisition patterns. This analysis reveals the types of information users tend to favor.

[0134] Step 4:

[0135] The device acquires user emotional data. This typically includes real-time facial recognition and voice tone analysis obtained through the device's camera and microphone, or emotional analysis of entered text. Emotional data is used to understand the user's psychological state as they process information.

[0136] Step 5:

[0137] The server uses an emotion engine to analyze the emotions a user experiences when retrieving information. The results of the emotion analysis help to deepen our understanding of biases and to suggest alternative information sources that take into account the user's emotions, such as anxiety and stress.

[0138] Step 6:

[0139] The server determines suitable alternative information sources for the user based on bias analysis and sentiment analysis results, and generates a report. This report includes insights related to information-gathering biases and psychological states.

[0140] Step 7:

[0141] The terminal displays reports received from the server and suggested information sources to the user. By reviewing the reports and reassessing their own information-gathering behavior, users can more easily access information from a broader perspective.

[0142] Step 8:

[0143] Users access suggested information sources and acquire new perspectives and information. This allows users to gain richer knowledge and understanding in a balanced information environment that also takes their emotional state into account.

[0144] (Example 2)

[0145] 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".

[0146] In today's information society, users can obtain vast amounts of information through the internet, but a problem arises when this information acquisition is often biased towards specific sources or categories. This bias can narrow users' perspectives and lead to biased judgments based on the information. Another challenge is that the influence of users' emotional states on information reception is not being considered.

[0147] 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.

[0148] In this invention, the server includes means for analyzing the user's information acquisition history, means for evaluating the bias in the user's information acquisition, and means for inferring the user's emotional state using an emotion analysis engine. This enables the user to improve their own information acquisition bias and receive balanced information that is appropriate to their emotions.

[0149] A "terminal" refers to a device used by users to acquire information and to collect the user's information acquisition history.

[0150] A "server" refers to a computing device that receives the history of information acquisition transmitted from terminals and performs analysis and evaluation.

[0151] "Information acquisition history" refers to data that includes records of websites accessed and applications used by the user.

[0152] "Generative AI technology" refers to artificial intelligence technology that statistically analyzes collected information and evaluates the bias in users' information acquisition.

[0153] An "emotion analysis engine" refers to technology that analyzes a user's emotional state and predicts that state.

[0154] "Bias assessment results" refer to the analysis results that show which categories or information sources users' information acquisition behavior is biased towards.

[0155] "Alternative information sources" refer to information with different perspectives that are suggested to compensate for user biases.

[0156] This invention uses a system comprising a terminal, a server, and an emotion analysis engine to detect biases in users' information acquisition and provide information based on their emotions. The terminal is an information processing device such as a computer or smartphone that the user uses on a daily basis. The terminal collects the user's web browsing and application usage history, encrypts this history, and transmits it to the server.

[0157] The server uses the received data to perform statistical analysis based on a generative AI model. This AI model discovers patterns from a large amount of historical information and evaluates the characteristics and biases of users' information acquisition. The generative AI model used by the server can utilize, for example, machine learning algorithms. The AI ​​model uses prompt statements to instruct it to evaluate biases, for example, in the form of "Detect biases in each category from the user's browsing history."

[0158] Furthermore, the server actively utilizes an emotion analysis engine to analyze real-time information from users (e.g., webcam footage and audio data) and infer their emotional state. This emotion analysis employs natural language processing and computer vision technologies. Based on the emotional state, appropriate alternative information sources are selected and provided to the user through the terminal. Through this process, users can recognize their own biases in information acquisition and understand the underlying psychological state.

[0159] For example, if a user is seeing a lot of negative news about environmental issues, the sentiment analysis engine can recommend news about successful environmental protection activities and positive initiatives to alleviate that stress. Through this kind of information provision, users are expected to obtain more balanced information.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device records the user's web browser and application usage history. This includes URLs of visited sites, time spent on each site, and search keywords. The input is user activity data, and the output is this data compiled into a single historical dataset. This dataset is encrypted using encryption technology to protect privacy.

[0163] Step 2:

[0164] The terminal sends encrypted information retrieval history to the server. The input is encrypted history data, and the output is stored by the server as received data. During this process, data is securely transferred using Secure Sockets Layer (SSL) technology.

[0165] Step 3:

[0166] The server inputs the received historical data into a generating AI model. This model uses statistical analysis algorithms to analyze the user's browsing trends. The input is decoded historical data, and the output is statistical results showing the frequency of access to each category and bias towards specific information sources. For example, a prompt such as "Detect bias towards each category from the user's browsing history" might be used.

[0167] Step 4:

[0168] The server uses an emotion analysis engine to analyze the user's real-time data. For example, it reads facial expressions from webcam footage and analyzes audio data from the microphone. The input is the user's real-time video and audio data, and the output is the analysis results indicating the user's emotional state. Based on these results, the server determines the user's psychological state.

[0169] Step 5:

[0170] The server generates alternative information suggestions for the user based on bias assessment and sentiment analysis results. Here, bias is corrected and information that takes sentiment into account is selected. The input is the bias assessment and sentiment analysis results, and the output is a list of information sources suitable for the user.

[0171] Step 6:

[0172] The terminal displays alternative information sources sent from the server to the user. The user can refer to this information and receive it from a new perspective. The input is a list of information from the server, and the output is information options displayed on the user interface. This makes it easier for the user to access new information sources.

[0173] (Application Example 2)

[0174] 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".

[0175] Many users suffer from bias in the information they obtain online, often developing a perspective that is biased towards specific sources or categories. Furthermore, this can lead to emotionally driven information acquisition, making a balanced understanding of information even more difficult. In this context, there is a need for technologies that support users in developing diverse perspectives and acquiring information while considering their emotions.

[0176] 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.

[0177] In this invention, the server includes means for collecting user access history data and emotional state data; means for statistically analyzing the data using a generative AI model and evaluating bias in information acquisition and emotional state; and means for determining and presenting alternative information sources appropriate to the emotional state based on the evaluation results. This enables users to obtain balanced information that reflects their own emotional state.

[0178] A "terminal" is an electronic device used by a user to acquire information, and is a device that collects and displays data.

[0179] "Access history data" refers to records of websites visited and applications used by users on the internet.

[0180] "Emotional state data" refers to data that shows the user's emotional response, and includes information obtained from real-time facial expressions and voice analysis.

[0181] A "server" is a computer system that receives data sent from a terminal, analyzes it using a generated AI model, and returns the processing results.

[0182] A "generative AI model" refers to artificial intelligence technology that uses machine learning algorithms to analyze patterns from input data and provide insights.

[0183] "Statistical analysis" means analyzing data using mathematical methods and objectively evaluating biases and patterns.

[0184] "Alternative information sources" refer to alternative sources or content presented to correct biases in users' information acquisition.

[0185] This invention is a system that detects information bias when a user acquires information using a terminal and provides alternative information according to their emotional state. The terminal collects the user's access history and emotional state data, encrypts it, and transmits it to the server. In this process, the emotional state data used is obtained from real-time facial expressions and voice.

[0186] The server performs statistical analysis on the received data using a generative AI model. This generative AI model is based on machine learning algorithms, analyzing data patterns and evaluating the user's information acquisition biases and emotional state. Based on this evaluation, it determines alternative information sources appropriate to the emotional state and sends them to the terminal. In this way, the user can obtain balanced information tailored to their emotional state.

[0187] For example, if a user frequently visits a particular political news site and analysis reveals that they are experiencing emotional distress, it is possible to present neutral news and positive topics offering diverse perspectives to help them harmonize their emotions. This allows the user to acquire new perspectives and information, thereby achieving emotional stability.

[0188] An example of a prompt message is: "The user tends to have a strong interest in the latest sports match results, and the sentiment engine has analyzed that the user is excited about this news. Please select a news article to recommend to provide the user with a new perspective."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The device collects user access history data and emotional state data. This data includes real-time user behavior (e.g., websites visited and apps used) and information about emotions gleaned from facial expressions and voice. This collected data is temporarily stored within the device.

[0192] Step 2:

[0193] The device encrypts the collected access history data and emotional state data and sends it to the server using a security protocol to prevent data leakage. This encrypted data is sent in a format that cannot be understood by third parties without the decryption key.

[0194] Step 3:

[0195] The server decrypts the received encrypted data and inputs it into a generative AI model. Here, the generative AI model is used to statistically analyze access history data and evaluate biases in user information acquisition. Based on past data patterns, the model specifically identifies which information sources or categories are being overused.

[0196] Step 4:

[0197] The server uses an emotion analysis algorithm to evaluate the user's emotional state based on the decoded emotional state data. This algorithm analyzes facial expressions and vocal characteristics to specifically determine the user's emotional state. The output of the emotion analysis is quantitative data indicating, for example, whether the user is anxious, excited, or calm.

[0198] Step 5:

[0199] The server integrates the results of statistical analysis and sentiment analysis to determine alternative information sources that suit the user's emotional state. Specifically, the process involves selecting alternative content that provides a complementary perspective to information that the user may be overly biased towards, and that soothes their emotions.

[0200] Step 6:

[0201] The server sends the selected alternative information source to the terminal. Here, the prompt generation unit is used to send information to the terminal that includes a summary of the selection reasons and recommended points. This information is intended to help the user make informed choices and to present new perspectives.

[0202] Step 7:

[0203] The device displays and suggests alternative information sources on the screen. This allows users to re-evaluate their biased information intake and access new information sources based on fresh perspectives. This process enables users to acquire more balanced information.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention includes a system for identifying information bias and providing diverse information sources. The system uses terminals, servers, and generative AI models to enable users to obtain unbiased information.

[0221] First, the device collects the user's web browsing and application usage history. This includes the URLs of visited websites, the time spent on each site, and the categories of content accessed. This data is securely transferred to the server with the user's permission.

[0222] The server uses a generative AI model to analyze the received access history data. Specifically, it identifies the type of information source (news, blogs, social media, etc.) and access patterns that are biased towards specific opinions or stances. This analysis helps determine whether users are dependent on specific categories or information sources.

[0223] The analysis results are visualized by the server and presented to the user. Users can intuitively understand their own information acquisition trends. Specifically, pie charts and bar graphs are used to show which information sources are used most frequently.

[0224] Next, the server suggests alternative information sources based on the user's information-gathering biases. For example, if it determines that the user is biased towards a particular news site, the server will present a list of other reliable news sites as recommendations. This suggestion serves as a guide for the user to obtain information from different perspectives.

[0225] Ultimately, by accepting this proposal and utilizing new sources of information, users can improve their information-gathering balance. This will prevent the formation of misunderstandings and biases, and provide an environment that promotes a broader and more objective understanding of information.

[0226] As described above, by implementing this invention, users can autonomously and proactively obtain diverse information, thereby reducing the risks associated with information bias. A specific example would be a case where, if a user only obtains political news from a particular site, the system recommends other neutral news sites, resulting in the user obtaining more balanced information.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The device collects the user's browsing history and app usage data. This includes the URLs of websites visited, the time spent browsing, and the categories of content accessed. This provides information about the sources the user is accessing.

[0230] Step 2:

[0231] The device encrypts the data it collects and sends it to the server. Secure communication protocols are used for transmission to protect user privacy. Data is transmitted regularly (e.g., daily or weekly) to ensure the server receives the most up-to-date information.

[0232] Step 3:

[0233] The server passes the received data to a generating AI model to begin analysis. The AI ​​model statistically processes the data to calculate the access frequency for specific categories and information sources. This analysis reveals users' information acquisition patterns.

[0234] Step 4:

[0235] The server visualizes the bias in users' information acquisition based on the analysis results. The analysis results are visualized as graphs and charts, allowing users to intuitively understand their own information acquisition tendencies.

[0236] Step 5:

[0237] The server suggests specific alternative information sources. If biased information acquisition is detected, it creates and presents a list of other reliable sources for that category. At this stage, recommendations are made to provide diverse perspectives.

[0238] Step 6:

[0239] The user accepts the suggestion from the server and accesses new information sources. The user opens the suggested information sources in a browser or app and obtains information from different perspectives. As a result, the user can improve the balance of information and gain a multifaceted perspective.

[0240] (Example 1)

[0241] 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."

[0242] In modern society, the information sources that users access via the internet are extremely diverse, but this diversity can conversely lead to information bias. By frequently accessing specific information sources, users may unknowingly receive only biased information, resulting in the formation of misunderstandings and prejudices. It is necessary to improve this situation and enable users to obtain more multifaceted and balanced information.

[0243] 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.

[0244] In this invention, the server includes means for statistically analyzing access history information using a generative AI model and evaluating the user's information acquisition bias, means for visualizing and presenting the evaluation results to the user, and means for presenting alternative information sources to biased information sources. This enables the user to recognize their own information acquisition bias and obtain information from diverse information sources.

[0245] A "terminal" is an electronic device used by a user to collect access history information.

[0246] "Access history information" refers to data that includes information about the websites a user has visited and the content they have viewed.

[0247] A "server" is a computer system that receives and analyzes access history information sent from terminals.

[0248] A "generative AI model" is an artificial intelligence algorithm used to analyze access history information, designed to identify bias in the information.

[0249] "Information bias" refers to the bias in information that arises when users access information from a particular source.

[0250] "Visualization" is a method of making information intuitively understandable to users by visually representing the results of the analysis.

[0251] An "alternative information source" is another information source that offers a different perspective or content from the information source that the user is currently referring to.

[0252] A "report" is a document that summarizes the results of an analysis regarding the bias in the information retrieved by the server.

[0253] This invention is a system that evaluates bias in information acquisition and provides users with diverse information. This system mainly consists of a terminal, a server, and a generative AI model.

[0254] First, the device collects the user's website browsing and application usage history. Hardware used for this includes electronic devices such as smartphones, tablets, and computers. The device temporarily stores the access history information obtained through this collection process and securely transmits it to a server.

[0255] Next, the server processes the received access history information and analyzes the data using a generative AI model. This generative AI model utilizes machine learning algorithms to classify information sources and identify biases in user information acquisition. Specifically, it uses natural language processing techniques to analyze the data and determine whether it is biased towards particular news sites or social media.

[0256] The analysis results are presented to the user in an intuitively understandable format by the server. Visualization methods such as pie charts and bar graphs allow users to easily grasp which information sources they rely on most.

[0257] Furthermore, the server has a function that suggests alternative information sources based on the user's information acquisition biases. This allows users to gain different perspectives and broaden the scope of their information acquisition. For example, if a user is biased towards a particular site regarding political news, the server will recommend other neutral news sites. In this case, an example of a prompt message to the generating AI model would be, "Based on this access history, identify the bias in the information sources the user relies on and suggest alternative information sources."

[0258] Ultimately, the system of the present invention allows users to access diverse information with less bias, thereby improving the quality of information acquisition.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The device collects the user's website browsing history and application usage history. Specifically, it records the URLs of websites the user accessed, the time spent on each site, and the categories of content viewed. Input is obtained from web browsers and applications, and output is a dataset of collected access history information.

[0262] Step 2:

[0263] The terminal transfers the collected access history information to the server using a secure communication protocol (e.g., HTTPS). The input is the dataset obtained in step 1, and the output is encrypted transmitted data. This protects the user's personal information.

[0264] Step 3:

[0265] The server inputs the received access history information into a generating AI model, which then statistically analyzes the data. Specifically, it uses natural language processing techniques to identify the type and frequency of use of information sources and determine whether they are biased towards a particular opinion. The input is access data sent from the terminal, and the output is the analysis results regarding information bias.

[0266] Step 4:

[0267] The server visualizes the analysis results using a visualization tool and presents them to the user in graph format. The input is the analysis results from step 3, and the output is a pie chart or bar graph displayed in the user interface. This allows the user to intuitively understand their own information acquisition trends.

[0268] Step 5:

[0269] The server performs a function that suggests alternative information sources to the user based on the analysis results. Specifically, it uses a generative AI model to generate prompt sentences and list various information sources. The input is the analysis results from step 3, and the output is the list of alternative information sources presented to the user.

[0270] Step 6:

[0271] The user accesses new information sources based on the alternative information sources presented by the server. Thus, the input is the list of information sources provided in step 5, and the output is the newly obtained information. This increases the diversity of information retrieval for the user.

[0272] (Application Example 1)

[0273] 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."

[0274] In today's information-driven society, users tend to rely heavily on specific information sources when browsing the web or consuming content. This bias can narrow users' perspectives and hinder the broad and objective understanding that diverse information can provide. Therefore, it is crucial to enable users to obtain information from a variety of sources without bias and to make more balanced information judgments.

[0275] 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.

[0276] In this invention, the server includes means for analyzing the user's content access history information using a generative AI model and determining information acquisition bias; means for visually displaying and presenting the results of the bias determination to the user; and means for presenting alternative information sources based on the user's bias and promoting information balance. This enables the user to review their own information acquisition tendencies and obtain information from diverse perspectives.

[0277] A "terminal" is an electronic device operated by a user that has the function of collecting specific information and transmitting it to a data processing device.

[0278] "Content access history information" refers to records of information viewed or consumed by users, specifically data indicating the web pages visited and their content.

[0279] A "data processing device" refers to a computing device that has the function of analyzing received data and calculating results, and that performs specific processing using a generative AI model.

[0280] A "generative AI model" is a form of artificial intelligence technology that analyzes input data to identify specific patterns or trends, and in particular utilizes machine learning algorithms.

[0281] "Information bias" is a concept that describes a state in which users obtain information that is biased towards a particular source or opinion.

[0282] "Visually display" means presenting the analyzed results in a form that can be intuitively understood by the user, including expressions using graphs and charts.

[0283] "Alternative information source" refers to a new and reliable information provider that is different from the user's current information acquisition source.

[0284] "Promote information balance" means creating a state that helps the user obtain information from diverse perspectives and information sources and enables an objective and broad understanding.

[0285] The system for implementing this invention mainly consists of a terminal, a data processing device, and a generative AI model. Each component and its respective role will be described below.

[0286] First, the terminal is operated by the user and collects content access history information. This information includes the URL of the web page accessed by the user, the stay time, and even the article category. This information is transmitted to the data processing device using a secure and efficient data transfer protocol, such as HTTPS.

[0287] Next, the data processing device analyzes the received access history information using a generative AI model using Python. This AI model implements a machine learning algorithm and is trained particularly to determine the bias in the user's information acquisition. The result of the analysis indicates the user's bias state, and in order to visually display the result, a graph drawing library such as Matplotlib is utilized.

[0288] After that, based on the result of the bias determination, the data processing device proposes alternative information sources to the user. This is for the purpose of assisting the user to obtain information from a multi-faceted perspective. The user can visually confirm these proposals through the terminal and access new information sources.

[0289] For example, if analysis reveals that a user is only browsing the "Gossip News" category, the system will recommend reliable alternative sources of "International News" or "Science News."

[0290] An example of a prompt statement is a process that suggests information sources to correct a user's bias, based on a question such as, "If a user is overly reliant on a particular information source, how would you recommend other, competing, and reliable information sources?"

[0291] In this way, the system of the present invention helps users avoid information bias and obtain information from a more holistic perspective.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The device collects information about the web pages accessed by the user. Specifically, it records data such as URLs, time spent on each page, and the categories of pages accessed. The input to this process is the user's browsing activity, and the output is structured access history information. The information is temporarily stored within the device and then prepared to be sent to a data processing unit using a secure data transfer protocol.

[0295] Step 2:

[0296] The server receives and stores access history information from terminals. The received data is analyzed using a generative AI model. The input here is access history information transferred from terminals, and the output is the analysis results showing the bias in the user's information acquisition. The generative AI model detects specific patterns and trends, determines whether there is a bias, and records the results.

[0297] Step 3:

[0298] The server visualizes the analysis results. This process uses libraries such as Matplotlib to display bias information as pie charts and bar graphs. The input is the bias analysis results derived in step 2, and the output is visualized graph data. This allows users to intuitively understand their own information acquisition tendencies.

[0299] Step 4:

[0300] Based on the bias assessment, the server presents the user with alternative information sources. This process involves listing reliable sources that differ from those previously relied upon. The input is the biased information data obtained in step 2, and the output is a list of recommended information sources.

[0301] Step 5:

[0302] The terminal displays the analysis results sent from the server and suggestions for alternative information sources to the user. This allows the user to connect to new information sources and obtain a wider variety of information. The input is the visualization data and list of information sources sent from the server, and the output is a link to new information sources that the user can access.

[0303] These steps allow users to improve their biased information intake and gain a holistic perspective on information. For example, if a user only reads "gossip news," the system will suggest new information sources such as "science news." Through this series of actions, the user's information scope is expanded.

[0304] 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.

[0305] The present invention provides a system that detects biases in user information acquisition and further uses an emotion engine to provide appropriate information based on the user's emotional state. This enables the user to more easily accept a multi-faceted perspective and deepen their understanding and acceptance of information.

[0306] The system has a terminal, a server, and an emotion engine as its main components. First, the terminal records the user's web browsing and application usage history. This reveals what websites the user visits and what information they frequently acquire. The collected data is encrypted to prevent leakage and then sent to the server.

[0307] The server inputs the received data into a generative AI model for statistical analysis. This analysis clarifies biases towards specific information sources or categories and determines which information lacks balance.

[0308] Next, the server uses the emotion engine to infer the emotional state based on additional information obtained from the user's terminal, such as real-time facial recognition, voice analysis, and input strings. Based on this emotional analysis, it determines appropriate alternative information sources considering the user's psychological state when receiving information.

[0309] The report generated by the server includes the user's current information acquisition bias and the emotional state behind it. This allows the user to review their information acquisition behavior and become aware of new perspectives. For example, if the user feels anxious about a certain news topic, the emotion engine recommends articles with neutral or comforting content to soothe that emotion.

[0310] Ultimately, the terminal displays suggestions from the server to the user, who can then search for and access the information sources accordingly. This process allows users to acquire information that takes their emotional state into account, providing a more balanced information environment. For example, if a user is stressed by news about a natural disaster, the emotion engine might recommend positive news or helpful guidelines to complement that information.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The device collects the user's web browsing history and application usage data. Specifically, it records the URLs of visited web pages, the time spent browsing, and the categories of information accessed. Through this, it is possible to understand which information sources and content the user primarily uses.

[0314] Step 2:

[0315] The device encrypts the data it collects and sends it to the server. This transmission is performed using secure protocols that protect user privacy.

[0316] Step 3:

[0317] The server analyzes the received data by running it through a generating AI model. The model uses statistical methods to calculate biases towards specific information sources or categories based on the user's information acquisition patterns. This analysis reveals the types of information users tend to favor.

[0318] Step 4:

[0319] The device acquires user emotional data. This typically includes real-time facial recognition and voice tone analysis obtained through the device's camera and microphone, or emotional analysis of entered text. Emotional data is used to understand the user's psychological state as they process information.

[0320] Step 5:

[0321] The server uses an emotion engine to analyze the emotions a user experiences when retrieving information. The results of the emotion analysis help to deepen our understanding of biases and to suggest alternative information sources that take into account the user's emotions, such as anxiety and stress.

[0322] Step 6:

[0323] The server determines suitable alternative information sources for the user based on bias analysis and sentiment analysis results, and generates a report. This report includes insights related to information-gathering biases and psychological states.

[0324] Step 7:

[0325] The terminal displays reports received from the server and suggested information sources to the user. By reviewing the reports and reassessing their own information-gathering behavior, users can more easily access information from a broader perspective.

[0326] Step 8:

[0327] Users access suggested information sources and acquire new perspectives and information. This allows users to gain richer knowledge and understanding in a balanced information environment that also takes their emotional state into account.

[0328] (Example 2)

[0329] 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".

[0330] In today's information society, users can obtain vast amounts of information through the internet, but a problem arises when this information acquisition is often biased towards specific sources or categories. This bias can narrow users' perspectives and lead to biased judgments based on the information. Another challenge is that the influence of users' emotional states on information reception is not being considered.

[0331] 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.

[0332] In this invention, the server includes means for analyzing the user's information acquisition history, means for evaluating the bias in the user's information acquisition, and means for inferring the user's emotional state using an emotion analysis engine. This enables the user to improve their own information acquisition bias and receive balanced information that is appropriate to their emotions.

[0333] A "terminal" refers to a device used by users to acquire information and to collect the user's information acquisition history.

[0334] A "server" refers to a computing device that receives the history of information acquisition transmitted from terminals and performs analysis and evaluation.

[0335] "Information acquisition history" refers to data that includes records of websites accessed and applications used by the user.

[0336] "Generative AI technology" refers to artificial intelligence technology that statistically analyzes collected information and evaluates the bias in users' information acquisition.

[0337] An "emotion analysis engine" refers to technology that analyzes a user's emotional state and predicts that state.

[0338] "Bias assessment results" refer to the analysis results that show which categories or information sources users' information acquisition behavior is biased towards.

[0339] "Alternative information sources" refer to information with different perspectives that are suggested to compensate for user biases.

[0340] This invention uses a system comprising a terminal, a server, and an emotion analysis engine to detect biases in users' information acquisition and provide information based on their emotions. The terminal is an information processing device such as a computer or smartphone that the user uses on a daily basis. The terminal collects the user's web browsing and application usage history, encrypts this history, and transmits it to the server.

[0341] The server uses the received data to perform statistical analysis based on a generative AI model. This AI model discovers patterns from a large amount of historical information and evaluates the characteristics and biases of users' information acquisition. The generative AI model used by the server can utilize, for example, machine learning algorithms. The AI ​​model uses prompt statements to instruct it to evaluate biases, for example, in the form of "Detect biases in each category from the user's browsing history."

[0342] Furthermore, the server actively utilizes an emotion analysis engine to analyze real-time information from users (e.g., webcam footage and audio data) and infer their emotional state. This emotion analysis employs natural language processing and computer vision technologies. Based on the emotional state, appropriate alternative information sources are selected and provided to the user through the terminal. Through this process, users can recognize their own biases in information acquisition and understand the underlying psychological state.

[0343] For example, if a user is seeing a lot of negative news about environmental issues, the sentiment analysis engine can recommend news about successful environmental protection activities and positive initiatives to alleviate that stress. Through this kind of information provision, users are expected to obtain more balanced information.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The device records the user's web browser and application usage history. This includes URLs of visited sites, time spent on each site, and search keywords. The input is user activity data, and the output is this data compiled into a single historical dataset. This dataset is encrypted using encryption technology to protect privacy.

[0347] Step 2:

[0348] The terminal sends encrypted information retrieval history to the server. The input is encrypted history data, and the output is stored by the server as received data. During this process, data is securely transferred using Secure Sockets Layer (SSL) technology.

[0349] Step 3:

[0350] The server inputs the received historical data into a generating AI model. This model uses statistical analysis algorithms to analyze the user's browsing trends. The input is decoded historical data, and the output is statistical results showing the frequency of access to each category and bias towards specific information sources. For example, a prompt such as "Detect bias towards each category from the user's browsing history" might be used.

[0351] Step 4:

[0352] The server uses an emotion analysis engine to analyze the user's real-time data. For example, it reads facial expressions from webcam footage and analyzes audio data from the microphone. The input is the user's real-time video and audio data, and the output is the analysis results indicating the user's emotional state. Based on these results, the server determines the user's psychological state.

[0353] Step 5:

[0354] The server generates alternative information suggestions for the user based on bias assessment and sentiment analysis results. Here, bias is corrected and information that takes sentiment into account is selected. The input is the bias assessment and sentiment analysis results, and the output is a list of information sources suitable for the user.

[0355] Step 6:

[0356] The terminal displays alternative information sources sent from the server to the user. The user can refer to this information and receive it from a new perspective. The input is a list of information from the server, and the output is information options displayed on the user interface. This makes it easier for the user to access new information sources.

[0357] (Application Example 2)

[0358] 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."

[0359] Many users suffer from bias in the information they obtain online, often developing a perspective that is biased towards specific sources or categories. Furthermore, this can lead to emotionally driven information acquisition, making a balanced understanding of information even more difficult. In this context, there is a need for technologies that support users in developing diverse perspectives and acquiring information while considering their emotions.

[0360] 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.

[0361] In this invention, the server includes means for collecting user access history data and emotional state data; means for statistically analyzing the data using a generative AI model and evaluating bias in information acquisition and emotional state; and means for determining and presenting alternative information sources appropriate to the emotional state based on the evaluation results. This enables users to obtain balanced information that reflects their own emotional state.

[0362] A "terminal" is an electronic device used by a user to acquire information, and is a device that collects and displays data.

[0363] "Access history data" refers to records of websites visited and applications used by users on the internet.

[0364] "Emotional state data" refers to data that shows the user's emotional response, and includes information obtained from real-time facial expressions and voice analysis.

[0365] A "server" is a computer system that receives data sent from a terminal, analyzes it using a generated AI model, and returns the processing results.

[0366] A "generative AI model" refers to artificial intelligence technology that uses machine learning algorithms to analyze patterns from input data and provide insights.

[0367] "Statistical analysis" means analyzing data using mathematical methods and objectively evaluating biases and patterns.

[0368] "Alternative information sources" refer to alternative sources or content presented to correct biases in users' information acquisition.

[0369] This invention is a system that detects information bias when a user acquires information using a terminal and provides alternative information according to their emotional state. The terminal collects the user's access history and emotional state data, encrypts it, and transmits it to the server. In this process, the emotional state data used is obtained from real-time facial expressions and voice.

[0370] The server performs statistical analysis on the received data using a generative AI model. This generative AI model is based on machine learning algorithms, analyzing data patterns and evaluating the user's information acquisition biases and emotional state. Based on this evaluation, it determines alternative information sources appropriate to the emotional state and sends them to the terminal. In this way, the user can obtain balanced information tailored to their emotional state.

[0371] For example, if a user frequently visits a particular political news site and analysis reveals that they are experiencing emotional distress, it is possible to present neutral news and positive topics offering diverse perspectives to help them harmonize their emotions. This allows the user to acquire new perspectives and information, thereby achieving emotional stability.

[0372] An example of a prompt message is: "The user tends to have a strong interest in the latest sports match results, and the sentiment engine has analyzed that the user is excited about this news. Please select a news article to recommend to provide the user with a new perspective."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The device collects user access history data and emotional state data. This data includes real-time user behavior (e.g., websites visited and apps used) and information about emotions gleaned from facial expressions and voice. This collected data is temporarily stored within the device.

[0376] Step 2:

[0377] The device encrypts the collected access history data and emotional state data and sends it to the server using a security protocol to prevent data leakage. This encrypted data is sent in a format that cannot be understood by third parties without the decryption key.

[0378] Step 3:

[0379] The server decrypts the received encrypted data and inputs it into a generative AI model. Here, the generative AI model is used to statistically analyze access history data and evaluate biases in user information acquisition. Based on past data patterns, the model specifically identifies which information sources or categories are being overused.

[0380] Step 4:

[0381] The server uses an emotion analysis algorithm to evaluate the user's emotional state based on the decoded emotional state data. This algorithm analyzes facial expressions and vocal characteristics to specifically determine the user's emotional state. The output of the emotion analysis is quantitative data indicating, for example, whether the user is anxious, excited, or calm.

[0382] Step 5:

[0383] The server integrates the results of statistical analysis and sentiment analysis to determine alternative information sources that suit the user's emotional state. Specifically, the process involves selecting alternative content that provides a complementary perspective to information that the user may be overly biased towards, and that soothes their emotions.

[0384] Step 6:

[0385] The server sends the selected alternative information source to the terminal. Here, the prompt generation unit is used to send information to the terminal that includes a summary of the selection reasons and recommended points. This information is intended to help the user make informed choices and to present new perspectives.

[0386] Step 7:

[0387] The device displays and suggests alternative information sources on the screen. This allows users to re-evaluate their biased information intake and access new information sources based on fresh perspectives. This process enables users to acquire more balanced information.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] This invention includes a system for identifying information bias and providing diverse information sources. The system uses terminals, servers, and generative AI models to enable users to obtain unbiased information.

[0405] First, the device collects the user's web browsing and application usage history. This includes the URLs of visited websites, the time spent on each site, and the categories of content accessed. This data is securely transferred to the server with the user's permission.

[0406] The server uses a generative AI model to analyze the received access history data. Specifically, it identifies the type of information source (news, blogs, social media, etc.) and access patterns that are biased towards specific opinions or stances. This analysis helps determine whether users are dependent on specific categories or information sources.

[0407] The analysis results are visualized by the server and presented to the user. Users can intuitively understand their own information acquisition trends. Specifically, pie charts and bar graphs are used to show which information sources are used most frequently.

[0408] Next, the server suggests alternative information sources based on the user's information-gathering biases. For example, if it determines that the user is biased towards a particular news site, the server will present a list of other reliable news sites as recommendations. This suggestion serves as a guide for the user to obtain information from different perspectives.

[0409] Ultimately, by accepting this proposal and utilizing new sources of information, users can improve their information-gathering balance. This will prevent the formation of misunderstandings and biases, and provide an environment that promotes a broader and more objective understanding of information.

[0410] As described above, by implementing this invention, users can autonomously and proactively obtain diverse information, thereby reducing the risks associated with information bias. A specific example would be a case where, if a user only obtains political news from a particular site, the system recommends other neutral news sites, resulting in the user obtaining more balanced information.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The device collects the user's browsing history and app usage data. This includes the URLs of websites visited, the time spent browsing, and the categories of content accessed. This provides information about the sources the user is accessing.

[0414] Step 2:

[0415] The device encrypts the data it collects and sends it to the server. Secure communication protocols are used for transmission to protect user privacy. Data is transmitted regularly (e.g., daily or weekly) to ensure the server receives the most up-to-date information.

[0416] Step 3:

[0417] The server passes the received data to a generating AI model to begin analysis. The AI ​​model statistically processes the data to calculate the access frequency for specific categories and information sources. This analysis reveals users' information acquisition patterns.

[0418] Step 4:

[0419] The server visualizes the bias in users' information acquisition based on the analysis results. The analysis results are visualized as graphs and charts, allowing users to intuitively understand their own information acquisition tendencies.

[0420] Step 5:

[0421] The server suggests specific alternative information sources. If biased information acquisition is detected, it creates and presents a list of other reliable sources for that category. At this stage, recommendations are made to provide diverse perspectives.

[0422] Step 6:

[0423] The user accepts the suggestion from the server and accesses new information sources. The user opens the suggested information sources in a browser or app and obtains information from different perspectives. As a result, the user can improve the balance of information and gain a multifaceted perspective.

[0424] (Example 1)

[0425] 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."

[0426] In modern society, the information sources that users access via the internet are extremely diverse, but this diversity can conversely lead to information bias. By frequently accessing specific information sources, users may unknowingly receive only biased information, resulting in the formation of misunderstandings and prejudices. It is necessary to improve this situation and enable users to obtain more multifaceted and balanced information.

[0427] 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.

[0428] In this invention, the server includes means for statistically analyzing access history information using a generative AI model and evaluating the user's information acquisition bias, means for visualizing and presenting the evaluation results to the user, and means for presenting alternative information sources to biased information sources. This enables the user to recognize their own information acquisition bias and obtain information from diverse information sources.

[0429] A "terminal" is an electronic device used by a user to collect access history information.

[0430] "Access history information" refers to data that includes information about the websites a user has visited and the content they have viewed.

[0431] A "server" is a computer system that receives and analyzes access history information sent from terminals.

[0432] A "generative AI model" is an artificial intelligence algorithm used to analyze access history information, designed to identify bias in the information.

[0433] "Information bias" refers to the bias in information that arises when users access information from a particular source.

[0434] "Visualization" is a method of making information intuitively understandable to users by visually representing the results of the analysis.

[0435] An "alternative information source" is another information source that offers a different perspective or content from the information source that the user is currently referring to.

[0436] A "report" is a document that summarizes the results of an analysis regarding the bias in the information retrieved by the server.

[0437] This invention is a system that evaluates bias in information acquisition and provides users with diverse information. This system mainly consists of a terminal, a server, and a generative AI model.

[0438] First, the device collects the user's website browsing and application usage history. Hardware used for this includes electronic devices such as smartphones, tablets, and computers. The device temporarily stores the access history information obtained through this collection process and securely transmits it to a server.

[0439] Next, the server processes the received access history information and analyzes the data using a generative AI model. This generative AI model utilizes machine learning algorithms to classify information sources and identify biases in user information acquisition. Specifically, it uses natural language processing techniques to analyze the data and determine whether it is biased towards particular news sites or social media.

[0440] The analysis results are presented to the user in an intuitively understandable format by the server. Visualization methods such as pie charts and bar graphs allow users to easily grasp which information sources they rely on most.

[0441] Furthermore, the server has a function that suggests alternative information sources based on the user's information acquisition biases. This allows users to gain different perspectives and broaden the scope of their information acquisition. For example, if a user is biased towards a particular site regarding political news, the server will recommend other neutral news sites. In this case, an example of a prompt message to the generating AI model would be, "Based on this access history, identify the bias in the information sources the user relies on and suggest alternative information sources."

[0442] Ultimately, the system of the present invention allows users to access diverse information with less bias, thereby improving the quality of information acquisition.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The device collects the user's website browsing history and application usage history. Specifically, it records the URLs of websites the user accessed, the time spent on each site, and the categories of content viewed. Input is obtained from web browsers and applications, and output is a dataset of collected access history information.

[0446] Step 2:

[0447] The terminal transfers the collected access history information to the server using a secure communication protocol (e.g., HTTPS). The input is the dataset obtained in step 1, and the output is encrypted transmitted data. This protects the user's personal information.

[0448] Step 3:

[0449] The server inputs the received access history information into a generating AI model, which then statistically analyzes the data. Specifically, it uses natural language processing techniques to identify the type and frequency of use of information sources and determine whether they are biased towards a particular opinion. The input is access data sent from the terminal, and the output is the analysis results regarding information bias.

[0450] Step 4:

[0451] The server visualizes the analysis results using a visualization tool and presents them to the user in graph format. The input is the analysis results from step 3, and the output is a pie chart or bar graph displayed in the user interface. This allows the user to intuitively understand their own information acquisition trends.

[0452] Step 5:

[0453] The server performs a function that suggests alternative information sources to the user based on the analysis results. Specifically, it uses a generative AI model to generate prompt sentences and list various information sources. The input is the analysis results from step 3, and the output is the list of alternative information sources presented to the user.

[0454] Step 6:

[0455] The user accesses new information sources based on the alternative information sources presented by the server. Thus, the input is the list of information sources provided in step 5, and the output is the newly obtained information. This increases the diversity of information retrieval for the user.

[0456] (Application Example 1)

[0457] 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."

[0458] In today's information-driven society, users tend to rely heavily on specific information sources when browsing the web or consuming content. This bias can narrow users' perspectives and hinder the broad and objective understanding that diverse information can provide. Therefore, it is crucial to enable users to obtain information from a variety of sources without bias and to make more balanced information judgments.

[0459] 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.

[0460] In this invention, the server includes means for analyzing the user's content access history information using a generative AI model and determining information acquisition bias; means for visually displaying and presenting the results of the bias determination to the user; and means for presenting alternative information sources based on the user's bias and promoting information balance. This enables the user to review their own information acquisition tendencies and obtain information from diverse perspectives.

[0461] A "terminal" is an electronic device operated by a user that has the function of collecting specific information and transmitting it to a data processing device.

[0462] "Content access history information" refers to records of information viewed or consumed by users, specifically data indicating the web pages visited and their content.

[0463] A "data processing device" refers to a computing device that has the function of analyzing received data and calculating results, and that performs specific processing using a generative AI model.

[0464] A "generative AI model" is a form of artificial intelligence technology that analyzes input data to identify specific patterns or trends, and in particular utilizes machine learning algorithms.

[0465] "Information bias" is a concept that describes a state in which users obtain information that is biased towards a particular source or opinion.

[0466] "Visual display" refers to presenting the analyzed results in a format that users can intuitively understand, and includes representations using graphs and charts.

[0467] "Alternative information sources" refer to new, reliable sources of information that are different from the user's current sources of information.

[0468] "Promoting information balance" means helping users obtain information from diverse perspectives and sources, thereby creating a state that enables objective and broad understanding.

[0469] The system implementing this invention mainly consists of a terminal, a data processing device, and a generative AI model. The following describes each component and its respective role.

[0470] First, the device is operated by the user to collect content access history information. This information includes the URLs of the web pages the user accessed, the time spent on each page, and even the article categories. This information is then transmitted to the data processing unit using a secure and efficient data transfer protocol, such as HTTPS.

[0471] Next, the data processing unit analyzes the received access history information using a generative AI model written in Python. This AI model implements machine learning algorithms and is specifically trained to determine user bias in information acquisition. The analysis results show the user's bias, and graph plotting libraries such as Matplotlib are used to visually display these results.

[0472] Subsequently, the data processing unit suggests alternative information sources to the user based on its bias assessment. This is to help the user obtain information from multiple perspectives. The user can visually confirm these suggestions through the terminal and access new information sources.

[0473] For example, if analysis reveals that a user is only browsing the "Gossip News" category, the system will recommend reliable alternative sources of "International News" or "Science News."

[0474] An example of a prompt statement is a process that suggests information sources to correct a user's bias, based on a question such as, "If a user is overly reliant on a particular information source, how would you recommend other, competing, and reliable information sources?"

[0475] In this way, the system of the present invention helps users avoid information bias and obtain information from a more holistic perspective.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The device collects information about the web pages accessed by the user. Specifically, it records data such as URLs, time spent on each page, and the categories of pages accessed. The input to this process is the user's browsing activity, and the output is structured access history information. The information is temporarily stored within the device and then prepared to be sent to a data processing unit using a secure data transfer protocol.

[0479] Step 2:

[0480] The server receives and stores access history information from terminals. The received data is analyzed using a generative AI model. The input here is access history information transferred from terminals, and the output is the analysis results showing the bias in the user's information acquisition. The generative AI model detects specific patterns and trends, determines whether there is a bias, and records the results.

[0481] Step 3:

[0482] The server visualizes the analysis results. This process uses libraries such as Matplotlib to display bias information as pie charts and bar graphs. The input is the bias analysis results derived in step 2, and the output is visualized graph data. This allows users to intuitively understand their own information acquisition tendencies.

[0483] Step 4:

[0484] Based on the bias assessment, the server presents the user with alternative information sources. This process involves listing reliable sources that differ from those previously relied upon. The input is the biased information data obtained in step 2, and the output is a list of recommended information sources.

[0485] Step 5:

[0486] The terminal displays the analysis results sent from the server and suggestions for alternative information sources to the user. This allows the user to connect to new information sources and obtain a wider variety of information. The input is the visualization data and list of information sources sent from the server, and the output is a link to new information sources that the user can access.

[0487] These steps allow users to improve their biased information intake and gain a holistic perspective on information. For example, if a user only reads "gossip news," the system will suggest new information sources such as "science news." Through this series of actions, the user's information scope is expanded.

[0488] 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.

[0489] This invention provides a system that detects biases in user information acquisition and, by utilizing an emotion engine, provides appropriate information based on the user's emotional state. As a result, users can more easily accept multiple perspectives and deepen their understanding and acceptance of information.

[0490] The system consists primarily of a terminal, a server, and an emotion engine. First, the terminal records the user's web browsing and application usage history. This reveals which sites the user visits and what information they frequently access. The collected data is encrypted to prevent leakage before being sent to the server.

[0491] The server inputs the received data into a generating AI model for statistical analysis. This analysis clarifies biases towards specific information sources or categories, and determines which information is unbalanced.

[0492] Next, the server uses an emotion engine to infer the emotional state based on additional information obtained from the user's device, such as real-time facial recognition, voice analysis, and entered text. Based on this emotion analysis, it considers the user's psychological state when receiving the information and determines an appropriate alternative source of information.

[0493] The server-generated report includes the user's current information-gathering biases and the underlying emotional state. This allows users to re-evaluate their information-gathering behavior and become aware of new perspectives. For example, if a user is feeling anxious about a news topic, the emotion engine will recommend articles with neutral or comforting content that alleviate those feelings.

[0494] Ultimately, the terminal displays suggestions from the server to the user, who can then search for and access the information sources accordingly. This process allows users to acquire information that takes their emotional state into account, providing a more balanced information environment. For example, if a user is stressed by news about a natural disaster, the emotion engine might recommend positive news or helpful guidelines to complement that information.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device collects the user's web browsing history and application usage data. Specifically, it records the URLs of visited web pages, the time spent browsing, and the categories of information accessed. Through this, it is possible to understand which information sources and content the user primarily uses.

[0498] Step 2:

[0499] The device encrypts the data it collects and sends it to the server. This transmission is performed using secure protocols that protect user privacy.

[0500] Step 3:

[0501] The server analyzes the received data by running it through a generating AI model. The model uses statistical methods to calculate biases towards specific information sources or categories based on the user's information acquisition patterns. This analysis reveals the types of information users tend to favor.

[0502] Step 4:

[0503] The device acquires user emotional data. This typically includes real-time facial recognition and voice tone analysis obtained through the device's camera and microphone, or emotional analysis of entered text. Emotional data is used to understand the user's psychological state as they process information.

[0504] Step 5:

[0505] The server uses an emotion engine to analyze the emotions a user experiences when retrieving information. The results of the emotion analysis help to deepen our understanding of biases and to suggest alternative information sources that take into account the user's emotions, such as anxiety and stress.

[0506] Step 6:

[0507] The server determines suitable alternative information sources for the user based on bias analysis and sentiment analysis results, and generates a report. This report includes insights related to information-gathering biases and psychological states.

[0508] Step 7:

[0509] The terminal displays reports received from the server and suggested information sources to the user. By reviewing the reports and reassessing their own information-gathering behavior, users can more easily access information from a broader perspective.

[0510] Step 8:

[0511] Users access suggested information sources and acquire new perspectives and information. This allows users to gain richer knowledge and understanding in a balanced information environment that also takes their emotional state into account.

[0512] (Example 2)

[0513] 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."

[0514] In today's information society, users can obtain vast amounts of information through the internet, but a problem arises when this information acquisition is often biased towards specific sources or categories. This bias can narrow users' perspectives and lead to biased judgments based on the information. Another challenge is that the influence of users' emotional states on information reception is not being considered.

[0515] 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.

[0516] In this invention, the server includes means for analyzing the user's information acquisition history, means for evaluating the bias in the user's information acquisition, and means for inferring the user's emotional state using an emotion analysis engine. This enables the user to improve their own information acquisition bias and receive balanced information that is appropriate to their emotions.

[0517] A "terminal" refers to a device used by users to acquire information and to collect the user's information acquisition history.

[0518] A "server" refers to a computing device that receives the history of information acquisition transmitted from terminals and performs analysis and evaluation.

[0519] "Information acquisition history" refers to data that includes records of websites accessed and applications used by the user.

[0520] "Generative AI technology" refers to artificial intelligence technology that statistically analyzes collected information and evaluates the bias in users' information acquisition.

[0521] An "emotion analysis engine" refers to technology that analyzes a user's emotional state and predicts that state.

[0522] "Bias assessment results" refer to the analysis results that show which categories or information sources users' information acquisition behavior is biased towards.

[0523] "Alternative information sources" refer to information with different perspectives that are suggested to compensate for user biases.

[0524] This invention uses a system comprising a terminal, a server, and an emotion analysis engine to detect biases in users' information acquisition and provide information based on their emotions. The terminal is an information processing device such as a computer or smartphone that the user uses on a daily basis. The terminal collects the user's web browsing and application usage history, encrypts this history, and transmits it to the server.

[0525] The server uses the received data to perform statistical analysis based on a generative AI model. This AI model discovers patterns from a large amount of historical information and evaluates the characteristics and biases of users' information acquisition. The generative AI model used by the server can utilize, for example, machine learning algorithms. The AI ​​model uses prompt statements to instruct it to evaluate biases, for example, in the form of "Detect biases in each category from the user's browsing history."

[0526] Furthermore, the server actively utilizes an emotion analysis engine to analyze real-time information from users (e.g., webcam footage and audio data) and infer their emotional state. This emotion analysis employs natural language processing and computer vision technologies. Based on the emotional state, appropriate alternative information sources are selected and provided to the user through the terminal. Through this process, users can recognize their own biases in information acquisition and understand the underlying psychological state.

[0527] For example, if a user is seeing a lot of negative news about environmental issues, the sentiment analysis engine can recommend news about successful environmental protection activities and positive initiatives to alleviate that stress. Through this kind of information provision, users are expected to obtain more balanced information.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The device records the user's web browser and application usage history. This includes URLs of visited sites, time spent on each site, and search keywords. The input is user activity data, and the output is this data compiled into a single historical dataset. This dataset is encrypted using encryption technology to protect privacy.

[0531] Step 2:

[0532] The terminal sends encrypted information retrieval history to the server. The input is encrypted history data, and the output is stored by the server as received data. During this process, data is securely transferred using Secure Sockets Layer (SSL) technology.

[0533] Step 3:

[0534] The server inputs the received historical data into a generating AI model. This model uses statistical analysis algorithms to analyze the user's browsing trends. The input is decoded historical data, and the output is statistical results showing the frequency of access to each category and bias towards specific information sources. For example, a prompt such as "Detect bias towards each category from the user's browsing history" might be used.

[0535] Step 4:

[0536] The server uses an emotion analysis engine to analyze the user's real-time data. For example, it reads facial expressions from webcam footage and analyzes audio data from the microphone. The input is the user's real-time video and audio data, and the output is the analysis results indicating the user's emotional state. Based on these results, the server determines the user's psychological state.

[0537] Step 5:

[0538] The server generates alternative information suggestions for the user based on bias assessment and sentiment analysis results. Here, bias is corrected and information that takes sentiment into account is selected. The input is the bias assessment and sentiment analysis results, and the output is a list of information sources suitable for the user.

[0539] Step 6:

[0540] The terminal displays alternative information sources sent from the server to the user. The user can refer to this information and receive it from a new perspective. The input is a list of information from the server, and the output is information options displayed on the user interface. This makes it easier for the user to access new information sources.

[0541] (Application Example 2)

[0542] 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."

[0543] Many users suffer from bias in the information they obtain online, often developing a perspective that is biased towards specific sources or categories. Furthermore, this can lead to emotionally driven information acquisition, making a balanced understanding of information even more difficult. In this context, there is a need for technologies that support users in developing diverse perspectives and acquiring information while considering their emotions.

[0544] 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.

[0545] In this invention, the server includes means for collecting user access history data and emotional state data; means for statistically analyzing the data using a generative AI model and evaluating bias in information acquisition and emotional state; and means for determining and presenting alternative information sources appropriate to the emotional state based on the evaluation results. This enables users to obtain balanced information that reflects their own emotional state.

[0546] A "terminal" is an electronic device used by a user to acquire information, and is a device that collects and displays data.

[0547] "Access history data" refers to records of websites visited and applications used by users on the internet.

[0548] "Emotional state data" refers to data that shows the user's emotional response, and includes information obtained from real-time facial expressions and voice analysis.

[0549] A "server" is a computer system that receives data sent from a terminal, analyzes it using a generated AI model, and returns the processing results.

[0550] A "generative AI model" refers to artificial intelligence technology that uses machine learning algorithms to analyze patterns from input data and provide insights.

[0551] "Statistical analysis" means analyzing data using mathematical methods and objectively evaluating biases and patterns.

[0552] "Alternative information sources" refer to alternative sources or content presented to correct biases in users' information acquisition.

[0553] This invention is a system that detects information bias when a user acquires information using a terminal and provides alternative information according to their emotional state. The terminal collects the user's access history and emotional state data, encrypts it, and transmits it to the server. In this process, the emotional state data used is obtained from real-time facial expressions and voice.

[0554] The server performs statistical analysis on the received data using a generative AI model. This generative AI model is based on machine learning algorithms, analyzing data patterns and evaluating the user's information acquisition biases and emotional state. Based on this evaluation, it determines alternative information sources appropriate to the emotional state and sends them to the terminal. In this way, the user can obtain balanced information tailored to their emotional state.

[0555] For example, if a user frequently visits a particular political news site and analysis reveals that they are experiencing emotional distress, it is possible to present neutral news and positive topics offering diverse perspectives to help them harmonize their emotions. This allows the user to acquire new perspectives and information, thereby achieving emotional stability.

[0556] An example of a prompt message is: "The user tends to have a strong interest in the latest sports match results, and the sentiment engine has analyzed that the user is excited about this news. Please select a news article to recommend to provide the user with a new perspective."

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The device collects user access history data and emotional state data. This data includes real-time user behavior (e.g., websites visited and apps used) and information about emotions gleaned from facial expressions and voice. This collected data is temporarily stored within the device.

[0560] Step 2:

[0561] The device encrypts the collected access history data and emotional state data and sends it to the server using a security protocol to prevent data leakage. This encrypted data is sent in a format that cannot be understood by third parties without the decryption key.

[0562] Step 3:

[0563] The server decrypts the received encrypted data and inputs it into a generative AI model. Here, the generative AI model is used to statistically analyze access history data and evaluate biases in user information acquisition. Based on past data patterns, the model specifically identifies which information sources or categories are being overused.

[0564] Step 4:

[0565] The server uses an emotion analysis algorithm to evaluate the user's emotional state based on the decoded emotional state data. This algorithm analyzes facial expressions and vocal characteristics to specifically determine the user's emotional state. The output of the emotion analysis is quantitative data indicating, for example, whether the user is anxious, excited, or calm.

[0566] Step 5:

[0567] The server integrates the results of statistical analysis and sentiment analysis to determine alternative information sources that suit the user's emotional state. Specifically, the process involves selecting alternative content that provides a complementary perspective to information that the user may be overly biased towards, and that soothes their emotions.

[0568] Step 6:

[0569] The server sends the selected alternative information source to the terminal. Here, the prompt generation unit is used to send information to the terminal that includes a summary of the selection reasons and recommended points. This information is intended to help the user make informed choices and to present new perspectives.

[0570] Step 7:

[0571] The device displays and suggests alternative information sources on the screen. This allows users to re-evaluate their biased information intake and access new information sources based on fresh perspectives. This process enables users to acquire more balanced information.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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".

[0589] This invention includes a system for identifying information bias and providing diverse information sources. The system uses terminals, servers, and generative AI models to enable users to obtain unbiased information.

[0590] First, the device collects the user's web browsing and application usage history. This includes the URLs of visited websites, the time spent on each site, and the categories of content accessed. This data is securely transferred to the server with the user's permission.

[0591] The server uses a generative AI model to analyze the received access history data. Specifically, it identifies the type of information source (news, blogs, social media, etc.) and access patterns that are biased towards specific opinions or stances. This analysis helps determine whether users are dependent on specific categories or information sources.

[0592] The analysis results are visualized by the server and presented to the user. Users can intuitively understand their own information acquisition trends. Specifically, pie charts and bar graphs are used to show which information sources are used most frequently.

[0593] Next, the server suggests alternative information sources based on the user's information-gathering biases. For example, if it determines that the user is biased towards a particular news site, the server will present a list of other reliable news sites as recommendations. This suggestion serves as a guide for the user to obtain information from different perspectives.

[0594] Ultimately, by accepting this proposal and utilizing new sources of information, users can improve their information-gathering balance. This will prevent the formation of misunderstandings and biases, and provide an environment that promotes a broader and more objective understanding of information.

[0595] As described above, by implementing this invention, users can autonomously and proactively obtain diverse information, thereby reducing the risks associated with information bias. A specific example would be a case where, if a user only obtains political news from a particular site, the system recommends other neutral news sites, resulting in the user obtaining more balanced information.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The device collects the user's browsing history and app usage data. This includes the URLs of websites visited, the time spent browsing, and the categories of content accessed. This provides information about the sources the user is accessing.

[0599] Step 2:

[0600] The device encrypts the data it collects and sends it to the server. Secure communication protocols are used for transmission to protect user privacy. Data is transmitted regularly (e.g., daily or weekly) to ensure the server receives the most up-to-date information.

[0601] Step 3:

[0602] The server passes the received data to a generating AI model to begin analysis. The AI ​​model statistically processes the data to calculate the access frequency for specific categories and information sources. This analysis reveals users' information acquisition patterns.

[0603] Step 4:

[0604] The server visualizes the bias in users' information acquisition based on the analysis results. The analysis results are visualized as graphs and charts, allowing users to intuitively understand their own information acquisition tendencies.

[0605] Step 5:

[0606] The server suggests specific alternative information sources. If biased information acquisition is detected, it creates and presents a list of other reliable sources for that category. At this stage, recommendations are made to provide diverse perspectives.

[0607] Step 6:

[0608] The user accepts the suggestion from the server and accesses new information sources. The user opens the suggested information sources in a browser or app and obtains information from different perspectives. As a result, the user can improve the balance of information and gain a multifaceted perspective.

[0609] (Example 1)

[0610] 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".

[0611] In modern society, the information sources that users access via the internet are extremely diverse, but this diversity can conversely lead to information bias. By frequently accessing specific information sources, users may unknowingly receive only biased information, resulting in the formation of misunderstandings and prejudices. It is necessary to improve this situation and enable users to obtain more multifaceted and balanced information.

[0612] 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.

[0613] In this invention, the server includes means for statistically analyzing access history information using a generative AI model and evaluating the user's information acquisition bias, means for visualizing and presenting the evaluation results to the user, and means for presenting alternative information sources to biased information sources. This enables the user to recognize their own information acquisition bias and obtain information from diverse information sources.

[0614] A "terminal" is an electronic device used by a user to collect access history information.

[0615] "Access history information" refers to data that includes information about the websites a user has visited and the content they have viewed.

[0616] A "server" is a computer system that receives and analyzes access history information sent from terminals.

[0617] A "generative AI model" is an artificial intelligence algorithm used to analyze access history information, designed to identify bias in the information.

[0618] "Information bias" refers to the bias in information that arises when users access information from a particular source.

[0619] "Visualization" is a method of making information intuitively understandable to users by visually representing the results of the analysis.

[0620] An "alternative information source" is another information source that offers a different perspective or content from the information source that the user is currently referring to.

[0621] A "report" is a document that summarizes the results of an analysis regarding the bias in the information retrieved by the server.

[0622] This invention is a system that evaluates bias in information acquisition and provides users with diverse information. This system mainly consists of a terminal, a server, and a generative AI model.

[0623] First, the device collects the user's website browsing and application usage history. Hardware used for this includes electronic devices such as smartphones, tablets, and computers. The device temporarily stores the access history information obtained through this collection process and securely transmits it to a server.

[0624] Next, the server processes the received access history information and analyzes the data using a generative AI model. This generative AI model utilizes machine learning algorithms to classify information sources and identify biases in user information acquisition. Specifically, it uses natural language processing techniques to analyze the data and determine whether it is biased towards particular news sites or social media.

[0625] The analysis results are presented to the user in an intuitively understandable format by the server. Visualization methods such as pie charts and bar graphs allow users to easily grasp which information sources they rely on most.

[0626] Furthermore, the server has a function that suggests alternative information sources based on the user's information acquisition biases. This allows users to gain different perspectives and broaden the scope of their information acquisition. For example, if a user is biased towards a particular site regarding political news, the server will recommend other neutral news sites. In this case, an example of a prompt message to the generating AI model would be, "Based on this access history, identify the bias in the information sources the user relies on and suggest alternative information sources."

[0627] Ultimately, the system of the present invention allows users to access diverse information with less bias, thereby improving the quality of information acquisition.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The device collects the user's website browsing history and application usage history. Specifically, it records the URLs of websites the user accessed, the time spent on each site, and the categories of content viewed. Input is obtained from web browsers and applications, and output is a dataset of collected access history information.

[0631] Step 2:

[0632] The terminal transfers the collected access history information to the server using a secure communication protocol (e.g., HTTPS). The input is the dataset obtained in step 1, and the output is encrypted transmitted data. This protects the user's personal information.

[0633] Step 3:

[0634] The server inputs the received access history information into a generating AI model, which then statistically analyzes the data. Specifically, it uses natural language processing techniques to identify the type and frequency of use of information sources and determine whether they are biased towards a particular opinion. The input is access data sent from the terminal, and the output is the analysis results regarding information bias.

[0635] Step 4:

[0636] The server visualizes the analysis results using a visualization tool and presents them to the user in graph format. The input is the analysis results from step 3, and the output is a pie chart or bar graph displayed in the user interface. This allows the user to intuitively understand their own information acquisition trends.

[0637] Step 5:

[0638] The server performs a function that suggests alternative information sources to the user based on the analysis results. Specifically, it uses a generative AI model to generate prompt sentences and list various information sources. The input is the analysis results from step 3, and the output is the list of alternative information sources presented to the user.

[0639] Step 6:

[0640] The user accesses new information sources based on the alternative information sources presented by the server. Thus, the input is the list of information sources provided in step 5, and the output is the newly obtained information. This increases the diversity of information retrieval for the user.

[0641] (Application Example 1)

[0642] 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".

[0643] In today's information-driven society, users tend to rely heavily on specific information sources when browsing the web or consuming content. This bias can narrow users' perspectives and hinder the broad and objective understanding that diverse information can provide. Therefore, it is crucial to enable users to obtain information from a variety of sources without bias and to make more balanced information judgments.

[0644] 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.

[0645] In this invention, the server includes means for analyzing the user's content access history information using a generative AI model and determining information acquisition bias; means for visually displaying and presenting the results of the bias determination to the user; and means for presenting alternative information sources based on the user's bias and promoting information balance. This enables the user to review their own information acquisition tendencies and obtain information from diverse perspectives.

[0646] A "terminal" is an electronic device operated by a user that has the function of collecting specific information and transmitting it to a data processing device.

[0647] "Content access history information" refers to records of information viewed or consumed by users, specifically data indicating the web pages visited and their content.

[0648] A "data processing device" refers to a computing device that has the function of analyzing received data and calculating results, and that performs specific processing using a generative AI model.

[0649] A "generative AI model" is a form of artificial intelligence technology that analyzes input data to identify specific patterns or trends, and in particular utilizes machine learning algorithms.

[0650] "Information bias" is a concept that describes a state in which users obtain information that is biased towards a particular source or opinion.

[0651] "Visual display" refers to presenting the analyzed results in a format that users can intuitively understand, and includes representations using graphs and charts.

[0652] "Alternative information sources" refer to new, reliable sources of information that are different from the user's current sources of information.

[0653] "Promoting information balance" means helping users obtain information from diverse perspectives and sources, thereby creating a state that enables objective and broad understanding.

[0654] The system implementing this invention mainly consists of a terminal, a data processing device, and a generative AI model. The following describes each component and its respective role.

[0655] First, the device is operated by the user to collect content access history information. This information includes the URLs of the web pages the user accessed, the time spent on each page, and even the article categories. This information is then transmitted to the data processing unit using a secure and efficient data transfer protocol, such as HTTPS.

[0656] Next, the data processing unit analyzes the received access history information using a generative AI model written in Python. This AI model implements machine learning algorithms and is specifically trained to determine user bias in information acquisition. The analysis results show the user's bias, and graph plotting libraries such as Matplotlib are used to visually display these results.

[0657] Subsequently, the data processing unit suggests alternative information sources to the user based on its bias assessment. This is to help the user obtain information from multiple perspectives. The user can visually confirm these suggestions through the terminal and access new information sources.

[0658] For example, if analysis reveals that a user is only browsing the "Gossip News" category, the system will recommend reliable alternative sources of "International News" or "Science News."

[0659] An example of a prompt statement is a process that suggests information sources to correct a user's bias, based on a question such as, "If a user is overly reliant on a particular information source, how would you recommend other, competing, and reliable information sources?"

[0660] In this way, the system of the present invention helps users avoid information bias and obtain information from a more holistic perspective.

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] The device collects information about the web pages accessed by the user. Specifically, it records data such as URLs, time spent on each page, and the categories of pages accessed. The input to this process is the user's browsing activity, and the output is structured access history information. The information is temporarily stored within the device and then prepared to be sent to a data processing unit using a secure data transfer protocol.

[0664] Step 2:

[0665] The server receives and stores access history information from terminals. The received data is analyzed using a generative AI model. The input here is access history information transferred from terminals, and the output is the analysis results showing the bias in the user's information acquisition. The generative AI model detects specific patterns and trends, determines whether there is a bias, and records the results.

[0666] Step 3:

[0667] The server visualizes the analysis results. This process uses libraries such as Matplotlib to display bias information as pie charts and bar graphs. The input is the bias analysis results derived in step 2, and the output is visualized graph data. This allows users to intuitively understand their own information acquisition tendencies.

[0668] Step 4:

[0669] Based on the bias assessment, the server presents the user with alternative information sources. This process involves listing reliable sources that differ from those previously relied upon. The input is the biased information data obtained in step 2, and the output is a list of recommended information sources.

[0670] Step 5:

[0671] The terminal displays the analysis results sent from the server and suggestions for alternative information sources to the user. This allows the user to connect to new information sources and obtain a wider variety of information. The input is the visualization data and list of information sources sent from the server, and the output is a link to new information sources that the user can access.

[0672] These steps allow users to improve their biased information intake and gain a holistic perspective on information. For example, if a user only reads "gossip news," the system will suggest new information sources such as "science news." Through this series of actions, the user's information scope is expanded.

[0673] 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.

[0674] This invention provides a system that detects biases in user information acquisition and, by utilizing an emotion engine, provides appropriate information based on the user's emotional state. As a result, users can more easily accept multiple perspectives and deepen their understanding and acceptance of information.

[0675] The system consists primarily of a terminal, a server, and an emotion engine. First, the terminal records the user's web browsing and application usage history. This reveals which sites the user visits and what information they frequently access. The collected data is encrypted to prevent leakage before being sent to the server.

[0676] The server inputs the received data into a generating AI model for statistical analysis. This analysis clarifies biases towards specific information sources or categories, and determines which information is unbalanced.

[0677] Next, the server uses an emotion engine to infer the emotional state based on additional information obtained from the user's device, such as real-time facial recognition, voice analysis, and entered text. Based on this emotion analysis, it considers the user's psychological state when receiving the information and determines an appropriate alternative source of information.

[0678] The server-generated report includes the user's current information-gathering biases and the underlying emotional state. This allows users to re-evaluate their information-gathering behavior and become aware of new perspectives. For example, if a user is feeling anxious about a news topic, the emotion engine will recommend articles with neutral or comforting content that alleviate those feelings.

[0679] Ultimately, the terminal displays suggestions from the server to the user, who can then search for and access the information sources accordingly. This process allows users to acquire information that takes their emotional state into account, providing a more balanced information environment. For example, if a user is stressed by news about a natural disaster, the emotion engine might recommend positive news or helpful guidelines to complement that information.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The device collects the user's web browsing history and application usage data. Specifically, it records the URLs of visited web pages, the time spent browsing, and the categories of information accessed. Through this, it is possible to understand which information sources and content the user primarily uses.

[0683] Step 2:

[0684] The device encrypts the data it collects and sends it to the server. This transmission is performed using secure protocols that protect user privacy.

[0685] Step 3:

[0686] The server analyzes the received data by running it through a generating AI model. The model uses statistical methods to calculate biases towards specific information sources or categories based on the user's information acquisition patterns. This analysis reveals the types of information users tend to favor.

[0687] Step 4:

[0688] The device acquires user emotional data. This typically includes real-time facial recognition and voice tone analysis obtained through the device's camera and microphone, or emotional analysis of entered text. Emotional data is used to understand the user's psychological state as they process information.

[0689] Step 5:

[0690] The server uses an emotion engine to analyze the emotions a user experiences when retrieving information. The results of the emotion analysis help to deepen our understanding of biases and to suggest alternative information sources that take into account the user's emotions, such as anxiety and stress.

[0691] Step 6:

[0692] The server determines suitable alternative information sources for the user based on bias analysis and sentiment analysis results, and generates a report. This report includes insights related to information-gathering biases and psychological states.

[0693] Step 7:

[0694] The terminal displays reports received from the server and suggested information sources to the user. By reviewing the reports and reassessing their own information-gathering behavior, users can more easily access information from a broader perspective.

[0695] Step 8:

[0696] Users access suggested information sources and acquire new perspectives and information. This allows users to gain richer knowledge and understanding in a balanced information environment that also takes their emotional state into account.

[0697] (Example 2)

[0698] 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".

[0699] In today's information society, users can obtain vast amounts of information through the internet, but a problem arises when this information acquisition is often biased towards specific sources or categories. This bias can narrow users' perspectives and lead to biased judgments based on the information. Another challenge is that the influence of users' emotional states on information reception is not being considered.

[0700] 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.

[0701] In this invention, the server includes means for analyzing the user's information acquisition history, means for evaluating the bias in the user's information acquisition, and means for inferring the user's emotional state using an emotion analysis engine. This enables the user to improve their own information acquisition bias and receive balanced information that is appropriate to their emotions.

[0702] A "terminal" refers to a device used by users to acquire information and to collect the user's information acquisition history.

[0703] A "server" refers to a computing device that receives the history of information acquisition transmitted from terminals and performs analysis and evaluation.

[0704] "Information acquisition history" refers to data that includes records of websites accessed and applications used by the user.

[0705] "Generative AI technology" refers to artificial intelligence technology that statistically analyzes collected information and evaluates the bias in users' information acquisition.

[0706] An "emotion analysis engine" refers to technology that analyzes a user's emotional state and predicts that state.

[0707] "Bias assessment results" refer to the analysis results that show which categories or information sources users' information acquisition behavior is biased towards.

[0708] "Alternative information sources" refer to information with different perspectives that are suggested to compensate for user biases.

[0709] This invention uses a system comprising a terminal, a server, and an emotion analysis engine to detect biases in users' information acquisition and provide information based on their emotions. The terminal is an information processing device such as a computer or smartphone that the user uses on a daily basis. The terminal collects the user's web browsing and application usage history, encrypts this history, and transmits it to the server.

[0710] The server uses the received data to perform statistical analysis based on a generative AI model. This AI model discovers patterns from a large amount of historical information and evaluates the characteristics and biases of users' information acquisition. The generative AI model used by the server can utilize, for example, machine learning algorithms. The AI ​​model uses prompt statements to instruct it to evaluate biases, for example, in the form of "Detect biases in each category from the user's browsing history."

[0711] Furthermore, the server actively utilizes an emotion analysis engine to analyze real-time information from users (e.g., webcam footage and audio data) and infer their emotional state. This emotion analysis employs natural language processing and computer vision technologies. Based on the emotional state, appropriate alternative information sources are selected and provided to the user through the terminal. Through this process, users can recognize their own biases in information acquisition and understand the underlying psychological state.

[0712] For example, if a user is seeing a lot of negative news about environmental issues, the sentiment analysis engine can recommend news about successful environmental protection activities and positive initiatives to alleviate that stress. Through this kind of information provision, users are expected to obtain more balanced information.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The device records the user's web browser and application usage history. This includes URLs of visited sites, time spent on each site, and search keywords. The input is user activity data, and the output is this data compiled into a single historical dataset. This dataset is encrypted using encryption technology to protect privacy.

[0716] Step 2:

[0717] The terminal sends encrypted information retrieval history to the server. The input is encrypted history data, and the output is stored by the server as received data. During this process, data is securely transferred using Secure Sockets Layer (SSL) technology.

[0718] Step 3:

[0719] The server inputs the received historical data into a generating AI model. This model uses statistical analysis algorithms to analyze the user's browsing trends. The input is decoded historical data, and the output is statistical results showing the frequency of access to each category and bias towards specific information sources. For example, a prompt such as "Detect bias towards each category from the user's browsing history" might be used.

[0720] Step 4:

[0721] The server uses an emotion analysis engine to analyze the user's real-time data. For example, it reads facial expressions from webcam footage and analyzes audio data from the microphone. The input is the user's real-time video and audio data, and the output is the analysis results indicating the user's emotional state. Based on these results, the server determines the user's psychological state.

[0722] Step 5:

[0723] The server generates alternative information suggestions for the user based on bias assessment and sentiment analysis results. Here, bias is corrected and information that takes sentiment into account is selected. The input is the bias assessment and sentiment analysis results, and the output is a list of information sources suitable for the user.

[0724] Step 6:

[0725] The terminal displays alternative information sources sent from the server to the user. The user can refer to this information and receive it from a new perspective. The input is a list of information from the server, and the output is information options displayed on the user interface. This makes it easier for the user to access new information sources.

[0726] (Application Example 2)

[0727] 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".

[0728] Many users suffer from bias in the information they obtain online, often developing a perspective that is biased towards specific sources or categories. Furthermore, this can lead to emotionally driven information acquisition, making a balanced understanding of information even more difficult. In this context, there is a need for technologies that support users in developing diverse perspectives and acquiring information while considering their emotions.

[0729] 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.

[0730] In this invention, the server includes means for collecting user access history data and emotional state data; means for statistically analyzing the data using a generative AI model and evaluating bias in information acquisition and emotional state; and means for determining and presenting alternative information sources appropriate to the emotional state based on the evaluation results. This enables users to obtain balanced information that reflects their own emotional state.

[0731] A "terminal" is an electronic device used by a user to acquire information, and is a device that collects and displays data.

[0732] "Access history data" refers to records of websites visited and applications used by users on the internet.

[0733] "Emotional state data" refers to data that shows the user's emotional response, and includes information obtained from real-time facial expressions and voice analysis.

[0734] A "server" is a computer system that receives data sent from a terminal, analyzes it using a generated AI model, and returns the processing results.

[0735] A "generative AI model" refers to artificial intelligence technology that uses machine learning algorithms to analyze patterns from input data and provide insights.

[0736] "Statistical analysis" means analyzing data using mathematical methods and objectively evaluating biases and patterns.

[0737] "Alternative information sources" refer to alternative sources or content presented to correct biases in users' information acquisition.

[0738] This invention is a system that detects information bias when a user acquires information using a terminal and provides alternative information according to their emotional state. The terminal collects the user's access history and emotional state data, encrypts it, and transmits it to the server. In this process, the emotional state data used is obtained from real-time facial expressions and voice.

[0739] The server performs statistical analysis on the received data using a generative AI model. This generative AI model is based on machine learning algorithms, analyzing data patterns and evaluating the user's information acquisition biases and emotional state. Based on this evaluation, it determines alternative information sources appropriate to the emotional state and sends them to the terminal. In this way, the user can obtain balanced information tailored to their emotional state.

[0740] For example, if a user frequently visits a particular political news site and analysis reveals that they are experiencing emotional distress, it is possible to present neutral news and positive topics offering diverse perspectives to help them harmonize their emotions. This allows the user to acquire new perspectives and information, thereby achieving emotional stability.

[0741] An example of a prompt message is: "The user tends to have a strong interest in the latest sports match results, and the sentiment engine has analyzed that the user is excited about this news. Please select a news article to recommend to provide the user with a new perspective."

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The device collects user access history data and emotional state data. This data includes real-time user behavior (e.g., websites visited and apps used) and information about emotions gleaned from facial expressions and voice. This collected data is temporarily stored within the device.

[0745] Step 2:

[0746] The device encrypts the collected access history data and emotional state data and sends it to the server using a security protocol to prevent data leakage. This encrypted data is sent in a format that cannot be understood by third parties without the decryption key.

[0747] Step 3:

[0748] The server decrypts the received encrypted data and inputs it into a generative AI model. Here, the generative AI model is used to statistically analyze access history data and evaluate biases in user information acquisition. Based on past data patterns, the model specifically identifies which information sources or categories are being overused.

[0749] Step 4:

[0750] The server uses an emotion analysis algorithm to evaluate the user's emotional state based on the decoded emotional state data. This algorithm analyzes facial expressions and vocal characteristics to specifically determine the user's emotional state. The output of the emotion analysis is quantitative data indicating, for example, whether the user is anxious, excited, or calm.

[0751] Step 5:

[0752] The server integrates the results of statistical analysis and sentiment analysis to determine alternative information sources that suit the user's emotional state. Specifically, the process involves selecting alternative content that provides a complementary perspective to information that the user may be overly biased towards, and that soothes their emotions.

[0753] Step 6:

[0754] The server sends the selected alternative information source to the terminal. Here, the prompt generation unit is used to send information to the terminal that includes a summary of the selection reasons and recommended points. This information is intended to help the user make informed choices and to present new perspectives.

[0755] Step 7:

[0756] The device displays and suggests alternative information sources on the screen. This allows users to re-evaluate their biased information intake and access new information sources based on fresh perspectives. This process enables users to acquire more balanced information.

[0757] 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.

[0758] 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.

[0759] 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 robot 414.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] The following is further disclosed regarding the embodiments described above.

[0779] (Claim 1)

[0780] A means by which a terminal collects user access history data,

[0781] Means for sending the aforementioned access history data to the server,

[0782] A means for statistically analyzing the access history data using a server-generated AI model and evaluating the bias in user information acquisition,

[0783] A means of visualizing the evaluation results of the aforementioned bias and presenting them to the user,

[0784] A means of presenting alternative sources of information for information that users may be biased towards,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, further comprising means for the server to create a report as information to be provided to the user based on the bias evaluation results.

[0788] (Claim 3)

[0789] The system according to claim 1, further comprising means for the terminal to display reports and suggestions received from the server, and enabling the user to access new information sources.

[0790] "Example 1"

[0791] (Claim 1)

[0792] A means by which a terminal collects user access history information,

[0793] Means for sending the aforementioned access history information to the server,

[0794] A server uses an AI model to statistically analyze the access history information and evaluates the bias in users' information acquisition,

[0795] A means for visualizing the evaluation results of the aforementioned bias and presenting them to the user as visual information,

[0796] A means of presenting alternative information sources when users are biased towards specific information,

[0797] The presentation of the aforementioned alternative information sources provides a means for users to explore new information sources and improve the diversity of information acquisition,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for the server to create a report including analysis results as information to be provided to users based on the bias evaluation results.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for the terminal to display reports received from the server and proposed information sources, enabling the user to access new information sources and broaden the scope of information acquisition.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] A means by which a device collects information about the user's content access history,

[0806] means for transferring the aforementioned access history information to a data processing device,

[0807] The data processing device includes means for analyzing the access history information using a generated AI model and determining the bias in the user's information acquisition,

[0808] A means for visually displaying and presenting the results of the bias assessment to the user,

[0809] A means of promoting information balance by presenting alternative information sources based on user biases,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for the data processing device to create an analysis document as information to be provided to the user based on the bias judgment result.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for the terminal to display analysis documents and proposals received from the data processing device, and enabling the user to connect to new information sources.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] A means by which a terminal collects the user's information acquisition history,

[0818] A means for encrypting the aforementioned information acquisition history and sending it to the server,

[0819] A means for statistically analyzing the information acquisition history using server-generated AI technology and evaluating the bias in the user's information acquisition,

[0820] A means for visualizing the evaluation results of the aforementioned bias and generating alternative information based on the user's emotional state,

[0821] A means of inferring the emotional state of a user using an emotion analysis engine,

[0822] A means of presenting alternative information sources to help users improve their information acquisition bias,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising means for the server to create a report as information to be provided to the user based on the bias evaluation results and emotional state.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for the terminal to display reports and suggestions received from the server, and enabling the user to access new information sources that correspond to their emotional state.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] A means by which the terminal collects user access history data and emotional state data,

[0831] Means for encrypting the aforementioned data and sending it to the server,

[0832] A server uses a generated AI model to statistically analyze the data and evaluate the user's bias in information acquisition and emotional state,

[0833] Based on the aforementioned evaluation results, a means for determining and presenting alternative information sources that correspond to the user's emotional state,

[0834] ...

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for the server to create a report that includes content adapted to the emotional state, as information to be provided to the user, based on the results of the evaluation of bias and emotional state.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for the terminal to display reports and suggestions received from the server, and enabling the user to access new information sources to obtain balanced information and diverse perspectives. [Explanation of symbols]

[0840] 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 by which a terminal collects user access history data, Means for sending the aforementioned access history data to the server, A means for statistically analyzing the access history data using a server-generated AI model and evaluating the bias in user information acquisition, A means of visualizing the evaluation results of the aforementioned bias and presenting them to the user, A means of presenting alternative sources of information for information that users may be biased towards, A system that includes this.

2. The system according to claim 1, further comprising means for the server to create a report as information to be provided to the user based on the bias evaluation results.

3. The system according to claim 1, further comprising means for the terminal to display reports and suggestions received from the server, and enabling the user to access new information sources.

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