System

Abstract

An object of the system according to the embodiment is to optimize the timeline based on the preference and emotion of the user.SOLUTION: A system includes an analysis part, a filtering part, a noise removal part, a feedback reflection part, and a customization part. The analysis unit analyzes the user's preference and emotion. The filtering unit filters the timeline on the basis of the result analyzed by the analysis unit. The noise removing unit removes noise from the timeline filtered by the filtering unit. The feedback reflection unit reflects the user's feedback on the timeline removed by the noise removal unit. The customization unit customizes the timeline reflected by the feedback reflection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, topics and noise that users do not want to see can be mixed into SNS timelines, which can reduce user satisfaction.

[0005] The system according to the embodiment aims to optimize the timeline based on the user's preferences and emotions. [Means for solving the problem]

[0006] A system according to an embodiment includes an analysis unit, a filtering unit, a noise removal unit, a feedback reflection unit, and a customization unit. The analysis unit analyzes user preferences and emotions. The filtering unit filters a timeline based on the results of the analysis by the analysis unit. The noise removal unit removes noise from the timeline filtered by the filtering unit. The feedback reflection unit reflects user feedback in the timeline removed by the noise removal unit. The customization unit customizes the timeline reflected by the feedback reflection unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the timeline based on the user's preferences and emotions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A timeline filtering system according to an embodiment of the present invention utilizes generative AI to filter a user's timeline and build a timeline that the user can use comfortably without feeling offended. As a result, the timeline filtering system optimizes the timeline based on the user's preferences and emotions, removes noise, and reflects feedback, allowing the user to use SNS comfortably.

[0029] A timeline filtering system according to an embodiment includes an analysis unit, a filtering unit, a noise removal unit, a feedback reflection unit, and a customization unit. The analysis unit analyzes a user's preferences and emotions. For example, the analysis unit analyzes the user's past posts, reactions, and browsing history to understand the user's preferences and emotions. The analysis unit can also use a generation AI to collect the user's biometric data (e.g., heart rate, electrodermal activity, etc.) in real time and analyze the data to estimate the user's emotional state. The analysis unit can also analyze the user's voice data and estimate emotions from the tone and speed of the voice. The filtering unit filters the timeline based on the results of the analysis by the analysis unit. For example, the filtering unit automatically removes topics that the user wants to avoid or content that the user finds unpleasant, and prioritizes displaying topics that interest the user or positive content. The filtering unit can also use a generation AI to analyze the user's real-time emotional state and prioritize displaying posts that best match the user's emotions at that time. The noise removal unit removes noise from the timeline filtered by the filtering unit. For example, the noise removal unit uses the generation AI to analyze the content of posts and the poster's behavioral patterns, and removes posts determined to be noise from the timeline. The noise removal unit can also analyze a user's past behavioral patterns and predict and remove posts determined to be noise in advance. The feedback reflection unit reflects user feedback on the timeline removed by the noise removal unit. For example, the feedback reflection unit receives user feedback and improves the filtering accuracy of the timeline based on that feedback. The feedback reflection unit can also collect user feedback in real time and build a system in which the generation AI immediately reflects that feedback. The customization unit customizes the timeline reflected by the feedback reflection unit. For example, the customization unit customizes the timeline based on the user's preferences and emotions. The customization unit can also use the generation AI to analyze the user's real-time emotional state and automatically generate a timeline that best suits their emotions at that time.As a result, the timeline filtering system according to the embodiment optimizes the timeline based on the user's preferences and emotions, removes noise, and reflects feedback, allowing the user to use the SNS comfortably. For example, since only topics that interest the user are displayed, browsing the timeline becomes more enjoyable and the user can spend more time using the SNS. Furthermore, removing noise allows the user to receive important information without missing anything.

[0030] The analysis unit collects data on external websites visited by the user, and the generation AI can analyze the user's preferences based on that data. The analysis unit, for example, collects data on external websites visited by the user, and the generation AI analyzes that data. For example, the analysis unit analyzes the content of news sites and blogs frequently visited by the user to identify the user's preferences. The analysis unit can also collect data on external websites visited by the user over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data on external websites visited by the user in real time and adjust the content of the timeline based on the user's preferences at that time. This makes it possible to analyze preferences based on data on external websites visited by the user.

[0031] The analysis unit collects data from online communities and forums in which the user participates, and the generation AI can analyze the user's preferences based on that data. For example, the analysis unit collects data from online communities and forums in which the user participates, and the generation AI analyzes that data. For example, the analysis unit analyzes topics and comment content frequently posted by the user to identify the user's preferences. The analysis unit can also collect data from online communities and forums in which the user participates over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data from online communities and forums in which the user participates in real time and adjust the content of the timeline based on the user's preferences at that time. This allows the analysis of preferences based on data from online communities and forums in which the user participates.

[0032] The filtering unit can develop an algorithm that learns from users' past feedback and continuously improves the accuracy of filtering. For example, the filtering unit collects users' past feedback and the generation AI learns from that data. For example, if a user provides feedback such as "I don't want to see this post," the filtering accuracy is improved based on that feedback. The filtering unit can also collect users' past feedback over a long period of time and the generation AI can learn from that data to improve the accuracy of filtering. Furthermore, the filtering unit can collect users' past feedback in real time and improve the accuracy of filtering based on that data. This makes it possible to improve the accuracy of filtering based on users' past feedback.

[0033] The filtering unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the filtering unit collects the user's geographical location information, and the generation AI filters the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The filtering unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the filtering unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0034] The filtering unit can display optimal content taking into account the user's device usage. For example, the filtering unit collects the user's device usage status, and the generation AI filters the timeline based on that data. For example, when using a mobile device, short videos and articles are preferentially displayed. The filtering unit can also collect the user's device usage status over a long period of time and display optimal content based on that data. Furthermore, the filtering unit can collect the user's device usage status in real time and display optimal content based on that data. This makes it possible to display optimal content based on the user's device usage status.

[0035] The noise removal unit can use the generation AI to develop an algorithm that evaluates the reliability of posts and automatically removes low-reliability posts. For example, the noise removal unit analyzes the source and citation of the post content so that the generation AI can evaluate the reliability of the post. Posts from low-reliability sources are automatically removed. The noise removal unit can also evaluate the reliability of posts over a long period of time and remove low-reliability posts based on that data. Furthermore, the noise removal unit can evaluate the reliability of posts in real time and remove low-reliability posts based on that data. This makes it possible to evaluate the reliability of posts and automatically remove low-reliability posts.

[0036] The noise removal unit can analyze a user's past behavioral patterns and predict and remove posts that are determined to be noise in advance. For example, the noise removal unit analyzes a user's past behavioral patterns and predicts posts that are determined to be noise in advance by having the generation AI learn the user's browsing history and reaction data. For example, the noise removal unit can learn the characteristics of posts that the user has ignored in the past and remove similar posts. The noise removal unit can also analyze a user's past behavioral patterns over a long period of time and remove posts that are determined to be noise based on that data. Furthermore, the noise removal unit can analyze a user's past behavioral patterns in real time and remove posts that are determined to be noise based on that data. This makes it possible to predict and remove posts that are determined to be noise in advance based on a user's past behavioral patterns.

[0037] The noise removal unit can reflect feedback from trusted friends and followers in the user's social network. For example, the noise removal unit collects feedback from trusted friends and followers in the user's social network, and the generation AI removes noise based on that data. For example, the noise removal unit removes posts that trusted friends find offensive. The noise removal unit can also collect feedback from trusted friends and followers in the user's social network over a long period of time and remove noise based on that data. Furthermore, the noise removal unit can collect feedback from trusted friends and followers in the user's social network in real time and remove noise based on that data. This makes it possible to remove noise based on feedback from trusted friends and followers in the user's social network.

[0038] The noise removal unit can automatically remove posts from accounts that the user has previously blocked. For example, the noise removal unit collects data on accounts that the user has previously blocked, and the generation AI removes noise based on that data. For example, posts from blocked accounts are automatically removed. The noise removal unit can also collect data on accounts that the user has previously blocked over a long period of time, and remove noise based on that data. Furthermore, the noise removal unit can collect data on accounts that the user has previously blocked in real time, and remove noise based on that data. This makes it possible to automatically remove posts from accounts that the user has previously blocked.

[0039] The feedback reflection unit can build a system in which user feedback is collected in real time and the generation AI instantly reflects that feedback. For example, the feedback reflection unit uses an online survey or comment function to collect user feedback in real time. The generation AI instantly adjusts the content of the timeline based on the collected feedback. The feedback reflection unit can also collect user feedback over a long period of time and adjust the content of the timeline based on that data. Furthermore, the feedback reflection unit can build a system in which user feedback is collected in real time and the content of the timeline is instantly adjusted based on that data. This makes it possible to collect user feedback in real time and reflect it instantly.

[0040] The feedback reflection unit can analyze user feedback and automatically adjust the filtering algorithm based on the content of the feedback. For example, the feedback reflection unit collects user feedback and the generation AI analyzes the data. For example, if a user provides feedback such as "I don't want to see this post," the filtering algorithm is adjusted based on that feedback. The feedback reflection unit can also collect user feedback over a long period of time and adjust the filtering algorithm based on that data. Furthermore, the feedback reflection unit can collect user feedback in real time and adjust the filtering algorithm based on that data. This makes it possible to automatically adjust the filtering algorithm based on user feedback.

[0041] The feedback reflection unit can compare the user's feedback with the feedback of other users, identify common problems, and improve them. The feedback reflection unit, for example, collects user feedback and the generation AI analyzes the data. For example, if multiple users point out the same problem, the problem is identified and improved. The feedback reflection unit can also collect user feedback over a long period of time and identify and improve common problems based on that data. Furthermore, the feedback reflection unit can collect user feedback in real time and identify and improve common problems based on that data. This makes it possible to compare the user's feedback with the feedback of other users, identify and improve common problems.

[0042] The feedback reflection unit adds a function that allows users to provide feedback by voice, and the generation AI can analyze the voice data. The feedback reflection unit adds a function that allows users to provide feedback by voice, and the generation AI analyzes the voice data. For example, if a user says by voice, "I don't want to see this post," the filtering algorithm is adjusted based on that feedback. The feedback reflection unit can also collect feedback provided by users by voice over a long period of time and adjust the filtering algorithm based on that data. Furthermore, the feedback reflection unit can collect feedback provided by users by voice in real time and adjust the filtering algorithm based on that data. This allows users to provide feedback by voice, and the voice data can be analyzed.

[0043] The customization unit can learn timeline customization patterns based on the user's past behavioral data and propose optimal customization. For example, the customization unit collects the user's past behavioral data, and the generation AI learns from that data. For example, the customization unit analyzes the user's past "likes" and comments to propose optimal customization. The customization unit can also collect the user's past behavioral data over a long period of time and learn customization patterns based on that data. Furthermore, the customization unit can collect the user's past behavioral data in real time and learn customization patterns based on that data. This makes it possible to propose optimal timeline customization based on the user's past behavioral data.

[0044] The customization unit can display optimal content taking into account the user's device usage. For example, the customization unit collects the user's device usage status, and the generation AI customizes the timeline based on that data. For example, when using a mobile device, short videos and articles are preferentially displayed. The customization unit can also collect the user's device usage status over a long period of time and display optimal content based on that data. Furthermore, the customization unit can collect the user's device usage status in real time and display optimal content based on that data. This makes it possible to display optimal content based on the user's device usage status.

[0045] The customization unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the customization unit collects the user's geographical location information, and the generation AI customizes the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The customization unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the customization unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit collects data on external websites visited by the user, and the generation AI can analyze the user's preferences based on that data. For example, data on external websites visited by the user is collected and the generation AI analyzes that data. For example, the content of news sites and blogs frequently visited by the user may be analyzed to identify the user's preferences. The analysis unit can also collect data on external websites visited by the user over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data on external websites visited by the user in real time and adjust the content of the timeline based on the user's preferences at that time. This makes it possible to analyze preferences based on data on external websites visited by the user.

[0048] The analysis unit collects data on online communities and forums in which the user participates, and the generation AI can analyze the user's preferences based on that data. For example, data on online communities and forums in which the user participates is collected, and the generation AI analyzes that data. For example, the analysis unit may analyze topics and comments frequently posted by the user to identify the user's preferences. The analysis unit may also collect data on online communities and forums in which the user participates over a long period of time, and the generation AI may analyze that data to track changes in the user's preferences. Furthermore, the analysis unit may collect data on online communities and forums in which the user participates in real time, and adjust the content of the timeline based on the user's preferences at that time. This allows the analysis of preferences based on data on online communities and forums in which the user participates.

[0049] The filtering unit can develop an algorithm that learns from users' past feedback and continuously improves the accuracy of filtering. For example, users' past feedback is collected and the generation AI learns from that data. For example, if a user provides feedback such as "I don't want to see this post," the accuracy of filtering is improved based on that feedback. The filtering unit can also collect users' past feedback over a long period of time and the generation AI can learn from that data to improve the accuracy of filtering. Furthermore, the filtering unit can collect users' past feedback in real time and improve the accuracy of filtering based on that data. This makes it possible to improve the accuracy of filtering based on users' past feedback.

[0050] The filtering unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the generation AI collects the user's geographical location information and filters the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The filtering unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the filtering unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The analysis unit analyzes the user's preferences and emotions. For example, the analysis unit analyzes the user's past posts, reactions, and browsing history to understand the user's preferences and emotions. The analysis unit can also use generative AI to collect the user's biometric data (heart rate, electrodermal activity, etc.) in real time and analyze that data to infer the user's emotional state. Furthermore, the analysis unit can analyze the user's voice data and infer emotions from the tone and speed of the voice. Step 2: The filtering unit filters the timeline based on the results of the analysis by the analysis unit. For example, the filtering unit automatically removes topics that the user wants to avoid or content that the user finds unpleasant, and prioritizes displaying topics that interest the user or positive content. The filtering unit can also use generative AI to analyze the user's real-time emotional state and prioritize displaying posts that best fit the user's emotions at that time. Step 3: The noise removal unit removes noise from the timeline filtered by the filtering unit. For example, the noise removal unit analyzes the content of posts and the poster's behavioral patterns using the generation AI, and removes posts that are determined to be noise from the timeline. The noise removal unit can also analyze a user's past behavioral patterns and predict and remove posts that are determined to be noise in advance. Step 4: The feedback reflection unit reflects the user's feedback on the timeline removed by the noise removal unit. For example, the feedback reflection unit receives feedback from the user and improves the filtering accuracy of the timeline based on that feedback. The feedback reflection unit can also build a system in which the generation AI collects user feedback in real time and immediately reflects that feedback. Step 5: The customization unit customizes the timeline reflected by the feedback reflection unit. For example, the customization unit customizes the timeline based on the user's preferences and emotions. The customization unit can also use generation AI to analyze the user's real-time emotional state and automatically generate a timeline that best suits their emotions at that time.

[0053] (Example 2) A timeline filtering system according to an embodiment of the present invention utilizes generative AI to filter a user's timeline and build a timeline that the user can use comfortably without feeling offended. As a result, the timeline filtering system optimizes the timeline based on the user's preferences and emotions, removes noise, and reflects feedback, allowing the user to use SNS comfortably.

[0054] A timeline filtering system according to an embodiment includes an analysis unit, a filtering unit, a noise removal unit, a feedback reflection unit, and a customization unit. The analysis unit analyzes a user's preferences and emotions. For example, the analysis unit analyzes the user's past posts, reactions, and browsing history to understand the user's preferences and emotions. The analysis unit can also use a generation AI to collect the user's biometric data (e.g., heart rate, electrodermal activity, etc.) in real time and analyze the data to estimate the user's emotional state. The analysis unit can also analyze the user's voice data and estimate emotions from the tone and speed of the voice. The filtering unit filters the timeline based on the results of the analysis by the analysis unit. For example, the filtering unit automatically removes topics that the user wants to avoid or content that the user finds unpleasant, and prioritizes displaying topics that interest the user or positive content. The filtering unit can also use a generation AI to analyze the user's real-time emotional state and prioritize displaying posts that best match the user's emotions at that time. The noise removal unit removes noise from the timeline filtered by the filtering unit. For example, the noise removal unit uses the generation AI to analyze the content of posts and the poster's behavioral patterns, and removes posts determined to be noise from the timeline. The noise removal unit can also analyze a user's past behavioral patterns and predict and remove posts determined to be noise in advance. The feedback reflection unit reflects user feedback on the timeline removed by the noise removal unit. For example, the feedback reflection unit receives user feedback and improves the filtering accuracy of the timeline based on that feedback. The feedback reflection unit can also collect user feedback in real time and build a system in which the generation AI immediately reflects that feedback. The customization unit customizes the timeline reflected by the feedback reflection unit. For example, the customization unit customizes the timeline based on the user's preferences and emotions. The customization unit can also use the generation AI to analyze the user's real-time emotional state and automatically generate a timeline that best suits their emotions at that time.As a result, the timeline filtering system according to the embodiment optimizes the timeline based on the user's preferences and emotions, removes noise, and reflects feedback, allowing the user to use the SNS comfortably. For example, since only topics that interest the user are displayed, browsing the timeline becomes more enjoyable and the user can spend more time using the SNS. Furthermore, removing noise allows the user to receive important information without missing anything.

[0055] The analysis unit collects the user's biometric data in real time, and the generation AI analyzes the data to estimate the user's emotional state. The analysis unit, for example, monitors the user's heart rate and electrodermal activity in real time and inputs the data into the generation AI. The generation AI analyzes this biometric data to estimate the user's emotional state. For example, if the heart rate is elevated, it determines that the user is excited. The analysis unit can also measure the user's electrodermal activity and estimate the user's stress level based on that data. Furthermore, the analysis unit can collect the user's biometric data over a long period of time, and the generation AI can analyze that data to track changes in the user's emotional state. This allows the user's emotional state to be estimated in real time based on the user's biometric data.

[0056] The analysis unit can analyze the user's voice data, infer the user's emotional state from the tone and speed of the voice, and reflect the content of the timeline based on the emotional state. For example, the analysis unit collects the user's voice data, and the generation AI analyzes the voice data. The user's emotional state is inferred based on characteristics such as the tone, speed, and volume of the voice. For example, if the voice tone is high and the speed is fast, it is determined that the user is excited. The analysis unit can also collect the user's voice data over a long period of time, and the generation AI can analyze the data to track changes in the user's emotional state. Furthermore, the analysis unit can analyze the user's voice data in real time and adjust the content of the timeline based on the user's emotional state at that time. This allows the user's emotional state to be inferred based on the user's voice data and reflected in the content of the timeline.

[0057] The analysis unit uses the emotion estimation function to analyze the emotional tone of posts that a user has previously liked and prioritizes the display of posts that evoke positive emotions. For example, the analysis unit uses natural language processing technology to analyze the emotional tone of posts that a user has previously liked. It identifies posts that evoke positive emotions and prioritizes their display on the timeline. For example, it extracts keywords and phrases that evoke positive emotions from posts that a user has previously liked and selects posts based on these keywords and phrases. The analysis unit can also analyze the emotional tone of posts that a user has previously liked over a long period of time to understand the user's emotional trends. Furthermore, the analysis unit can analyze the emotional tone of posts that a user has previously liked in real time and select posts based on the user's emotional state at the time. This allows the analysis of the emotional tone of posts that a user has previously liked to prioritize the display of posts that evoke positive emotions.

[0058] The analysis unit collects data on external websites visited by the user, and the generation AI can analyze the user's preferences based on that data. The analysis unit, for example, collects data on external websites visited by the user, and the generation AI analyzes that data. For example, the analysis unit analyzes the content of news sites and blogs frequently visited by the user to identify the user's preferences. The analysis unit can also collect data on external websites visited by the user over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data on external websites visited by the user in real time and adjust the content of the timeline based on the user's preferences at that time. This makes it possible to analyze preferences based on data on external websites visited by the user.

[0059] The analysis unit collects data from online communities and forums in which the user participates, and the generation AI can analyze the user's preferences based on that data. For example, the analysis unit collects data from online communities and forums in which the user participates, and the generation AI analyzes that data. For example, the analysis unit analyzes topics and comment content frequently posted by the user to identify the user's preferences. The analysis unit can also collect data from online communities and forums in which the user participates over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data from online communities and forums in which the user participates in real time and adjust the content of the timeline based on the user's preferences at that time. This allows the analysis of preferences based on data from online communities and forums in which the user participates.

[0060] The filtering unit can use the generation AI to analyze the user's real-time emotional state and prioritize displaying posts that are most appropriate for the user's emotions at that time. For example, the filtering unit uses the generation AI to analyze the user's real-time emotional state and prioritize displaying posts that are most appropriate for the user's emotions at that time. For example, if the user is feeling stressed, the filtering unit can display relaxing content. The filtering unit can also analyze the user's real-time emotional state over a long period of time and select posts based on the user's emotions at that time. The filtering unit can also analyze the user's real-time emotional state in real time and select posts based on the user's emotions at that time. This makes it possible to display optimal posts based on the user's real-time emotional state.

[0061] The filtering unit can develop an algorithm that learns from users' past feedback and continuously improves the accuracy of filtering. For example, the filtering unit collects users' past feedback and the generation AI learns from that data. For example, if a user provides feedback such as "I don't want to see this post," the filtering accuracy is improved based on that feedback. The filtering unit can also collect users' past feedback over a long period of time and the generation AI can learn from that data to improve the accuracy of filtering. Furthermore, the filtering unit can collect users' past feedback in real time and improve the accuracy of filtering based on that data. This makes it possible to improve the accuracy of filtering based on users' past feedback.

[0062] The filtering unit can use the emotion estimation function to detect posts that users find offensive in advance and issue a warning before they are displayed. The filtering unit, for example, uses the emotion estimation function to detect posts that users find offensive in advance. For example, it identifies posts with high negative emotion scores and issues a warning before they are displayed. The filtering unit can also analyze posts that users find offensive over a long period of time and issue a warning based on that data. Furthermore, the filtering unit can analyze posts that users find offensive in real time and issue a warning based on that data. This makes it possible to detect posts that users may find offensive in advance and issue a warning.

[0063] The filtering unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the filtering unit collects the user's geographical location information, and the generation AI filters the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The filtering unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the filtering unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0064] The filtering unit can display optimal content taking into account the user's device usage. For example, the filtering unit collects the user's device usage status, and the generation AI filters the timeline based on that data. For example, when using a mobile device, short videos and articles are preferentially displayed. The filtering unit can also collect the user's device usage status over a long period of time and display optimal content based on that data. Furthermore, the filtering unit can collect the user's device usage status in real time and display optimal content based on that data. This makes it possible to display optimal content based on the user's device usage status.

[0065] The filtering unit can use the emotion estimation function to analyze how a user feels about a specific event or occurrence, and perform filtering based on that emotion. The filtering unit, for example, uses the emotion estimation function to analyze how a user feels about a specific event or occurrence. For example, posts related to events for which the user has positive emotions are preferentially displayed. The filtering unit can also analyze how a user feels about a specific event or occurrence over a long period of time, and perform filtering based on that data. Furthermore, the filtering unit can analyze how a user feels about a specific event or occurrence in real time, and perform filtering based on that data. This makes it possible to perform filtering based on the emotions a user feels about a specific event or occurrence.

[0066] The noise removal unit can use the generation AI to develop an algorithm that evaluates the reliability of posts and automatically removes low-reliability posts. For example, the noise removal unit analyzes the source and citation of the post content so that the generation AI can evaluate the reliability of the post. Posts from low-reliability sources are automatically removed. The noise removal unit can also evaluate the reliability of posts over a long period of time and remove low-reliability posts based on that data. Furthermore, the noise removal unit can evaluate the reliability of posts in real time and remove low-reliability posts based on that data. This makes it possible to evaluate the reliability of posts and automatically remove low-reliability posts.

[0067] The noise removal unit can analyze a user's past behavioral patterns and predict and remove posts that are determined to be noise in advance. For example, the noise removal unit analyzes a user's past behavioral patterns and predicts posts that are determined to be noise in advance by having the generation AI learn the user's browsing history and reaction data. For example, the noise removal unit can learn the characteristics of posts that the user has ignored in the past and remove similar posts. The noise removal unit can also analyze a user's past behavioral patterns over a long period of time and remove posts that are determined to be noise based on that data. Furthermore, the noise removal unit can analyze a user's past behavioral patterns in real time and remove posts that are determined to be noise based on that data. This makes it possible to predict and remove posts that are determined to be noise in advance based on a user's past behavioral patterns.

[0068] The noise removal unit can use the emotion estimation function to detect noise that users find unpleasant and issue a warning before displaying the noise. The noise removal unit, for example, uses the emotion estimation function to detect noise that users find unpleasant. For example, it identifies posts with high negative emotion scores and issues a warning before displaying the posts. The noise removal unit can also analyze noise that users find unpleasant over a long period of time and issue a warning based on the data. Furthermore, the noise removal unit can analyze noise that users find unpleasant in real time and issue a warning based on the data. This makes it possible to detect noise that users may find unpleasant in advance and issue a warning.

[0069] The noise removal unit can reflect feedback from trusted friends and followers in the user's social network. For example, the noise removal unit collects feedback from trusted friends and followers in the user's social network, and the generation AI removes noise based on that data. For example, the noise removal unit removes posts that trusted friends find offensive. The noise removal unit can also collect feedback from trusted friends and followers in the user's social network over a long period of time and remove noise based on that data. Furthermore, the noise removal unit can collect feedback from trusted friends and followers in the user's social network in real time and remove noise based on that data. This makes it possible to remove noise based on feedback from trusted friends and followers in the user's social network.

[0070] The noise removal unit can automatically remove posts from accounts that the user has previously blocked. For example, the noise removal unit collects data on accounts that the user has previously blocked, and the generation AI removes noise based on that data. For example, posts from blocked accounts are automatically removed. The noise removal unit can also collect data on accounts that the user has previously blocked over a long period of time, and remove noise based on that data. Furthermore, the noise removal unit can collect data on accounts that the user has previously blocked in real time, and remove noise based on that data. This makes it possible to automatically remove posts from accounts that the user has previously blocked.

[0071] The noise removal unit can use the emotion estimation function to analyze what kind of noise a user finds annoying during a specific time period and perform filtering appropriate for that time period. The noise removal unit, for example, uses the emotion estimation function to analyze what kind of noise a user finds annoying during a specific time period. For example, if a user wants to relax at night, posts that may increase stress can be removed. The noise removal unit can also analyze noises that a user finds annoying during a specific time period over a long period of time and perform filtering based on that data. Furthermore, the noise removal unit can analyze noises that a user finds annoying during a specific time period in real time and perform filtering based on that data. This makes it possible to analyze noises that a user finds annoying during a specific time period and perform filtering appropriate for that time period.

[0072] The feedback reflection unit can build a system in which user feedback is collected in real time and the generation AI instantly reflects that feedback. For example, the feedback reflection unit uses an online survey or comment function to collect user feedback in real time. The generation AI instantly adjusts the content of the timeline based on the collected feedback. The feedback reflection unit can also collect user feedback over a long period of time and adjust the content of the timeline based on that data. Furthermore, the feedback reflection unit can build a system in which user feedback is collected in real time and the content of the timeline is instantly adjusted based on that data. This makes it possible to collect user feedback in real time and reflect it instantly.

[0073] The feedback reflection unit can analyze user feedback and automatically adjust the filtering algorithm based on the content of the feedback. For example, the feedback reflection unit collects user feedback and the generation AI analyzes the data. For example, if a user provides feedback such as "I don't want to see this post," the filtering algorithm is adjusted based on that feedback. The feedback reflection unit can also collect user feedback over a long period of time and adjust the filtering algorithm based on that data. Furthermore, the feedback reflection unit can collect user feedback in real time and adjust the filtering algorithm based on that data. This makes it possible to automatically adjust the filtering algorithm based on user feedback.

[0074] The feedback reflection unit can use the emotion estimation function to analyze the emotional state of the user when providing feedback and preferentially reflect feedback based on the emotion. The feedback reflection unit, for example, uses the emotion estimation function to analyze the emotional state of the user when providing feedback. For example, it preferentially reflects feedback provided in a positive emotional state. The feedback reflection unit can also analyze the emotional state of the user when providing feedback over a long period of time and preferentially reflect feedback based on the data. Furthermore, the feedback reflection unit can analyze the emotional state of the user when providing feedback in real time and preferentially reflect feedback based on the data. This makes it possible to preferentially reflect feedback based on the emotional state of the user when providing feedback.

[0075] The feedback reflection unit can compare the user's feedback with the feedback of other users, identify common problems, and improve them. The feedback reflection unit, for example, collects user feedback and the generation AI analyzes the data. For example, if multiple users point out the same problem, the problem is identified and improved. The feedback reflection unit can also collect user feedback over a long period of time and identify and improve common problems based on that data. Furthermore, the feedback reflection unit can collect user feedback in real time and identify and improve common problems based on that data. This makes it possible to compare the user's feedback with the feedback of other users, identify and improve common problems.

[0076] The feedback reflection unit adds a function that allows users to provide feedback by voice, and the generation AI can analyze the voice data. The feedback reflection unit adds a function that allows users to provide feedback by voice, and the generation AI analyzes the voice data. For example, if a user says by voice, "I don't want to see this post," the filtering algorithm is adjusted based on that feedback. The feedback reflection unit can also collect feedback provided by users by voice over a long period of time and adjust the filtering algorithm based on that data. Furthermore, the feedback reflection unit can collect feedback provided by users by voice in real time and adjust the filtering algorithm based on that data. This allows users to provide feedback by voice, and the voice data can be analyzed.

[0077] The feedback reflection unit can use the emotion estimation function to analyze what kind of feedback the user will provide during a specific time period and introduce a feedback collection method suitable for that time period. The feedback reflection unit, for example, uses the emotion estimation function to analyze what kind of feedback the user will provide during a specific time period. For example, if the user is relaxed at night, feedback provided in a relaxed state is preferentially collected. The feedback reflection unit can also analyze feedback provided by the user during a specific time period over a long period of time and adjust the feedback collection method based on the data. Furthermore, the feedback reflection unit can analyze feedback provided by the user during a specific time period in real time and adjust the feedback collection method based on the data. In this way, it is possible to analyze feedback provided by the user during a specific time period and introduce a feedback collection method suitable for that time period.

[0078] The customization unit can use the generation AI to analyze the user's real-time emotional state and automatically generate a timeline that is most suitable for the emotions at that time. For example, the customization unit uses the generation AI to analyze the user's real-time emotional state and automatically generate a timeline that is most suitable for the emotions at that time. For example, if the user is feeling stressed, content that helps them relax is displayed. The customization unit can also analyze the user's real-time emotional state over a long period of time and automatically generate a timeline based on the emotions at that time. Furthermore, the customization unit can analyze the user's real-time emotional state in real time and automatically generate a timeline based on the emotions at that time. This makes it possible to automatically generate an optimal timeline based on the user's real-time emotional state.

[0079] The customization unit can learn timeline customization patterns based on the user's past behavioral data and propose optimal customization. For example, the customization unit collects the user's past behavioral data, and the generation AI learns from that data. For example, the customization unit analyzes the user's past "likes" and comments to propose optimal customization. The customization unit can also collect the user's past behavioral data over a long period of time and learn customization patterns based on that data. Furthermore, the customization unit can collect the user's past behavioral data in real time and learn customization patterns based on that data. This makes it possible to propose optimal timeline customization based on the user's past behavioral data.

[0080] The customization unit can use the emotion estimation function to analyze how a user feels about a specific theme or genre, and customize the timeline based on that emotion. The customization unit, for example, uses the emotion estimation function to analyze how a user feels about a specific theme or genre. For example, posts related to themes about which the user has positive emotions can be preferentially displayed. The customization unit can also analyze how a user feels about a specific theme or genre over a long period of time, and customize the timeline based on that data. Furthermore, the customization unit can analyze how a user feels about a specific theme or genre in real time, and customize the timeline based on that data. This makes it possible to customize the timeline based on the user's emotions about a specific theme or genre.

[0081] The customization unit can display optimal content taking into account the user's device usage. For example, the customization unit collects the user's device usage status, and the generation AI customizes the timeline based on that data. For example, when using a mobile device, short videos and articles are preferentially displayed. The customization unit can also collect the user's device usage status over a long period of time and display optimal content based on that data. Furthermore, the customization unit can collect the user's device usage status in real time and display optimal content based on that data. This makes it possible to display optimal content based on the user's device usage status.

[0082] The customization unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the customization unit collects the user's geographical location information, and the generation AI customizes the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The customization unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the customization unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0083] The customization unit can use the emotion estimation function to analyze what kind of content a user prefers during a specific time period and automatically generate a timeline suitable for that time period. The customization unit, for example, uses the emotion estimation function to analyze what kind of content a user prefers during a specific time period. For example, if a user wants to relax at night, the customization unit displays relaxing content. The customization unit can also analyze the content a user prefers during a specific time period over a long period of time and automatically generate a timeline based on that data. Furthermore, the customization unit can analyze the content a user prefers during a specific time period in real time and automatically generate a timeline based on that data. This makes it possible to analyze the content a user prefers during a specific time period and automatically generate a timeline suitable for that time period.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The analysis unit analyzes the user's preferences and emotions. For example, the analysis unit analyzes the user's past posts, reactions, and browsing history to understand the user's preferences and emotions. The analysis unit can also use the generation AI to collect the user's biometric data (heart rate, electrodermal activity, etc.) in real time and analyze that data to estimate the user's emotional state. The analysis unit can also analyze the user's voice data and estimate emotions from the tone and speed of the voice. The filtering unit filters the timeline based on the results of the analysis by the analysis unit. For example, the filtering unit automatically removes topics that the user wants to avoid or content that the user finds unpleasant, and prioritizes displaying topics that interest the user or positive content. The filtering unit can also use the generation AI to analyze the user's real-time emotional state and prioritize displaying posts that best match the user's emotions at that time. The noise removal unit removes noise from the timeline filtered by the filtering unit. For example, the noise removal unit uses the generation AI to analyze the content of posts and the poster's behavioral patterns and removes posts that are determined to be noise from the timeline. The noise removal unit can also analyze a user's past behavioral patterns and predict and remove posts that are deemed to be noise in advance. The feedback reflection unit reflects user feedback on the timeline removed by the noise removal unit. For example, the feedback reflection unit receives feedback from users and improves the filtering accuracy of the timeline based on that feedback. The feedback reflection unit can also collect user feedback in real time and build a system in which the generation AI immediately reflects that feedback. The customization unit customizes the timeline reflected by the feedback reflection unit. For example, the customization unit customizes the timeline based on the user's preferences and emotions. The customization unit can also use the generation AI to analyze the user's real-time emotional state and automatically generate a timeline that best suits their emotions at that time.As a result, the timeline filtering system according to the embodiment optimizes the timeline based on the user's preferences and emotions, removes noise, and reflects feedback, allowing the user to use the SNS comfortably. For example, since only topics that interest the user are displayed, browsing the timeline becomes more enjoyable and the user can spend more time using the SNS. Furthermore, removing noise allows the user to receive important information without missing anything.

[0086] The analysis unit collects the user's biometric data in real time, and the generation AI analyzes that data to estimate the user's emotional state. For example, the analysis unit monitors the user's heart rate and electrodermal activity in real time and inputs that data into the generation AI. The generation AI analyzes this biometric data to estimate the user's emotional state. For example, if the heart rate is elevated, it determines that the user is excited. The analysis unit can also measure the user's electrodermal activity and estimate the user's stress level based on that data. Furthermore, the analysis unit can collect the user's biometric data over a long period of time, and the generation AI can analyze that data to track changes in the user's emotional state. This allows the user's emotional state to be estimated in real time based on the user's biometric data.

[0087] The analysis unit can analyze the user's voice data, infer the user's emotional state from the tone and speed of the voice, and reflect the content of the timeline based on the emotional state. For example, the user's voice data is collected and the generation AI analyzes the voice data. The user's emotional state is inferred based on characteristics such as the tone, speed, and volume of the voice. For example, if the voice tone is high and the speed is fast, it is determined that the user is excited. The analysis unit can also collect the user's voice data over a long period of time and the generation AI can analyze the data to track changes in the user's emotional state. Furthermore, the analysis unit can analyze the user's voice data in real time and adjust the content of the timeline based on the user's emotional state at that time. This allows the user's emotional state to be inferred based on the user's voice data and reflected in the content of the timeline.

[0088] The analysis unit uses the emotion estimation function to analyze the emotional tone of posts that a user has previously liked and prioritizes the display of posts that evoke positive emotions. For example, the generative AI uses natural language processing technology to analyze the emotional tone of posts that a user has previously liked. It identifies posts that evoke positive emotions and prioritizes their display on the timeline. For example, it extracts keywords and phrases that evoke positive emotions from posts that a user has previously liked and selects posts based on these. The analysis unit can also analyze the emotional tone of posts that a user has previously liked over a long period of time to understand the user's emotional trends. Furthermore, the analysis unit can analyze the emotional tone of posts that a user has previously liked in real time and select posts based on the user's emotional state at the time. This allows the analysis of the emotional tone of posts that a user has previously liked to prioritize the display of posts that evoke positive emotions.

[0089] The analysis unit collects data on external websites visited by the user, and the generation AI can analyze the user's preferences based on that data. For example, data on external websites visited by the user is collected and the generation AI analyzes that data. For example, the content of news sites and blogs frequently visited by the user may be analyzed to identify the user's preferences. The analysis unit can also collect data on external websites visited by the user over a long period of time, and the generation AI can analyze that data to track changes in the user's preferences. Furthermore, the analysis unit can collect data on external websites visited by the user in real time and adjust the content of the timeline based on the user's preferences at that time. This makes it possible to analyze preferences based on data on external websites visited by the user.

[0090] The analysis unit collects data on online communities and forums in which the user participates, and the generation AI can analyze the user's preferences based on that data. For example, data on online communities and forums in which the user participates is collected, and the generation AI analyzes that data. For example, the analysis unit may analyze topics and comments frequently posted by the user to identify the user's preferences. The analysis unit may also collect data on online communities and forums in which the user participates over a long period of time, and the generation AI may analyze that data to track changes in the user's preferences. Furthermore, the analysis unit may collect data on online communities and forums in which the user participates in real time, and adjust the content of the timeline based on the user's preferences at that time. This allows the analysis of preferences based on data on online communities and forums in which the user participates.

[0091] The filtering unit can use the generation AI to analyze the user's real-time emotional state and prioritize displaying posts that are most appropriate for the user's emotions at that time. For example, the generation AI can analyze the user's real-time emotional state and prioritize displaying posts that are most appropriate for the user's emotions at that time. For example, if the user is feeling stressed, it can display relaxing content. The filtering unit can also analyze the user's real-time emotional state over a long period of time and select posts based on the user's emotions at that time. The filtering unit can also analyze the user's real-time emotional state in real time and select posts based on the user's emotions at that time. This makes it possible to display optimal posts based on the user's real-time emotional state.

[0092] The filtering unit can develop an algorithm that learns from users' past feedback and continuously improves the accuracy of filtering. For example, users' past feedback is collected and the generation AI learns from that data. For example, if a user provides feedback such as "I don't want to see this post," the accuracy of filtering is improved based on that feedback. The filtering unit can also collect users' past feedback over a long period of time and the generation AI can learn from that data to improve the accuracy of filtering. Furthermore, the filtering unit can collect users' past feedback in real time and improve the accuracy of filtering based on that data. This makes it possible to improve the accuracy of filtering based on users' past feedback.

[0093] The filtering unit can use the emotion estimation function to detect posts that users find offensive in advance and issue a warning before they are displayed. For example, the emotion estimation function is used to detect posts that users find offensive in advance. For example, posts with high negative emotion scores are identified and a warning is issued before they are displayed. The filtering unit can also analyze posts that users find offensive over a long period of time and issue a warning based on that data. Furthermore, the filtering unit can analyze posts that users find offensive in real time and issue a warning based on that data. This makes it possible to detect posts that users may find offensive in advance and issue a warning.

[0094] The filtering unit can take into account the user's geographical location information and prioritize displaying region-specific topics. For example, the generation AI collects the user's geographical location information and filters the timeline based on that data. For example, news and event information from the region where the user lives can be prioritized. The filtering unit can also collect the user's geographical location information over a long period of time and prioritize displaying region-specific topics based on that data. Furthermore, the filtering unit can collect the user's geographical location information in real time and prioritize displaying region-specific topics based on that data. This makes it possible to prioritize displaying region-specific topics based on the user's geographical location information.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The analysis unit analyzes the user's preferences and emotions. For example, the analysis unit analyzes the user's past posts, reactions, and browsing history to understand the user's preferences and emotions. The analysis unit can also use generative AI to collect the user's biometric data (heart rate, electrodermal activity, etc.) in real time and analyze that data to infer the user's emotional state. Furthermore, the analysis unit can analyze the user's voice data and infer emotions from the tone and speed of the voice. Step 2: The filtering unit filters the timeline based on the results of the analysis by the analysis unit. For example, the filtering unit automatically removes topics that the user wants to avoid or content that the user finds unpleasant, and prioritizes displaying topics that interest the user or positive content. The filtering unit can also use generative AI to analyze the user's real-time emotional state and prioritize displaying posts that best fit the user's emotions at that time. Step 3: The noise removal unit removes noise from the timeline filtered by the filtering unit. For example, the noise removal unit analyzes the content of posts and the poster's behavioral patterns using the generation AI, and removes posts that are determined to be noise from the timeline. The noise removal unit can also analyze a user's past behavioral patterns and predict and remove posts that are determined to be noise in advance. Step 4: The feedback reflection unit reflects the user's feedback on the timeline removed by the noise removal unit. For example, the feedback reflection unit receives feedback from the user and improves the filtering accuracy of the timeline based on that feedback. The feedback reflection unit can also build a system in which the generation AI collects user feedback in real time and immediately reflects that feedback. Step 5: The customization unit customizes the timeline reflected by the feedback reflection unit. For example, the customization unit customizes the timeline based on the user's preferences and emotions. The customization unit can also use generation AI to analyze the user's real-time emotional state and automatically generate a timeline that best suits their emotions at that time.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes user preferences and emotions; a filtering unit that filters a timeline based on the result of the analysis by the analysis unit; a noise removal unit that removes noise from the timeline filtered by the filtering unit; a feedback reflecting unit that reflects user feedback on the timeline removed by the noise removing unit; a customization unit that customizes the timeline reflected by the feedback reflection unit. A system characterized by:

2. The analysis unit collect data on external websites visited by said user; The generating AI analyzes the user's preferences based on that data. The system of claim 1 .

3. The filtering unit Taking into account the geographical location information of the user, Prioritize region-specific topics The system of claim 1 .

4. The noise removal unit Using generative AI, we evaluate the authenticity of posts, Developing an algorithm to automatically filter out unreliable posts The system of claim 1 .

5. The feedback reflection unit collecting feedback from said users in real time; Build a system where generative AI instantly reflects that feedback The system of claim 1 .

6. The customization unit using a generative AI to analyze the user's real-time emotional state; Automatically generate a timeline that best suits your emotions at that time The system of claim 1 .

7. The analysis unit collecting biometric data of the user in real time; A generative AI analyzes the data and estimates the user's emotional state. The system of claim 1 .

8. The filtering unit Using an emotion estimation function, detect posts that the user finds unpleasant in advance; Give a warning before displaying The system of claim 1 .

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