system

Abstract

The system according to this embodiment aims to learn user behavior and provide optimal feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a feedback unit, and a learning unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The learning unit learns the user's behavior.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to provide optimal feedback based on user actions, and there is room for improvement.

[0005] The system according to an embodiment aims to learn user actions and provide optimal feedback.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, a feedback unit, and a learning unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The feedback unit provides feedback based on the analysis result obtained by the analysis unit. The learning unit learns user actions. [Effects of the Invention]

[0007] The system according to this embodiment can learn user behavior and provide optimal feedback. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The SenseConnect system according to an embodiment of the present invention is an innovative wearable device that transmits information by utilizing sensory organs other than hearing. This system can select the optimal information transmission method according to the individual's disability and preferences. Furthermore, an autonomous AI agent learns the user's behavior and provides optimal feedback to continuously improve the user experience. This device primarily targets people with hearing impairments and addresses the various challenges they face. For example, the degree of impairment varies greatly, including people who are completely deaf, people who can hear but communicate through writing, and people who can communicate through oral communication. The SenseConnect system goes beyond being a mere hearing assistance device to provide a comprehensive communication support system that utilizes the diverse senses that humans possess. This aims to realize a society where all people can communicate comfortably, regardless of the type or degree of hearing impairment. For example, the SenseConnect system is equipped with a haptic feedback function, a visual information processing system, AI-based environmental recognition, and personalization functions. The haptic feedback function transmits the strength and direction of sound through vibration patterns and provides spatial information through changes in vibration intensity in different parts of the body. It also expresses the speaker's emotions and intonation through changes in vibration patterns. The visual information processing system provides real-time subtitle display through AR glasses, visually displays the location and number of speakers, and performs emotion analysis and visual representation through facial recognition. AI-powered environmental recognition analyzes the surrounding environment, prioritizes notification of important information, and automatically detects and warns of emergencies. Personalization features allow for the selection of the optimal information delivery method according to the individual's disability, automatic adjustment through learning of usage patterns, and customization according to user preferences. Potential application scenarios include educational settings, workplaces, and daily life. For example, in educational settings, lecture content can be converted into real-time visual and tactile information, allowing for intuitive understanding of the location and order of speakers in group discussions. In workplaces, the content of meetings can be understood through multiple senses, enabling reliable transmission of emergency contacts and important notices. In daily life, it can support conversations with family, receive guidance information in public facilities, and provide evacuation guidance in emergencies.Thus, the SenseConnect system brings about social impacts for the hearing impaired, including comprehensive information access, improved safety, enhanced social participation, and expanded educational and employment opportunities. It provides an environment where everyone, regardless of the type or degree of hearing impairment, can receive information, enabling reliable danger detection and rapid situational awareness in emergencies through information transmission via multiple senses. Furthermore, by reducing communication barriers and facilitating more natural interpersonal interaction, it promotes barrier-free learning environments and smoother communication in the workplace. In summary, the SenseConnect system can bring about social impacts for the hearing impaired, including comprehensive information access, improved safety, enhanced social participation, and expanded educational and employment opportunities.

[0029] The SenseConnect system according to this embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a learning unit. The data collection unit collects information. For example, the data collection unit can collect environmental information using sensors. The data collection unit can also collect information through user input. Furthermore, the data collection unit can collect information from the internet. For example, the data collection unit can collect ambient temperature information using a temperature sensor. The data collection unit can also collect text information entered by a user. The data collection unit can also collect the latest news information from the internet. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze the information using data mining techniques. Furthermore, the analysis unit can analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can extract useful information from a large amount of data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also recognize data patterns using machine learning algorithms. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback, for example, by sending notifications. Furthermore, the feedback unit can provide feedback by issuing alerts. Additionally, the feedback unit can provide feedback by generating reports. For example, the feedback unit can convey important information by sending notifications to users. The feedback unit can also notify users of emergencies by issuing alerts. The feedback unit can also provide detailed information by generating reports. The learning unit learns user behavior. For example, the learning unit can track user behavior using machine learning algorithms. The learning unit can also analyze user behavior patterns. Furthermore, the learning unit can accumulate user behavior history. For example, the learning unit can track user behavior in real time using machine learning algorithms.The learning unit can also understand user preferences and tendencies by analyzing user behavior patterns. The learning unit can also make predictions based on past data by accumulating user behavior history. As a result, the SenseConnect system according to this embodiment can improve the user experience through information collection, analysis, feedback, and learning.

[0030] The data collection unit collects information. For example, it can collect environmental information using sensors. Specifically, it can collect detailed information about the surrounding environment using a wide variety of sensors, such as temperature sensors, humidity sensors, light sensors, and sound sensors. These sensors acquire data in real time and transmit it to a central database. For example, a temperature sensor can continuously monitor ambient temperature and detect abnormal temperature changes. A humidity sensor monitors humidity fluctuations and provides data to maintain a comfortable environment. A light sensor measures ambient brightness and can be used for automatic lighting adjustment. A sound sensor detects ambient noise levels and provides data for noise reduction measures. The data collection unit can also collect information through user input. For example, it collects text and audio information entered by users via smartphones or tablets and uses this for analysis. Furthermore, the data collection unit can collect information from the internet. For example, it collects the latest news and trend information from news sites and social media on the internet and incorporates it into the system. This allows the data collection unit to collect a wide range of data from diverse sources, strengthening the information infrastructure of the entire system. The collected data is updated in real time and can be linked with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and feedback departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the information collected by the data collection department. For example, the analysis department can analyze information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, thereby enabling the discovery of hidden trends and outliers. For example, it can analyze environmental data collected from sensors to identify temperature and humidity fluctuation patterns under specific conditions. The analysis department can also analyze information using statistical analysis techniques. Statistical analysis techniques are methods for understanding the distribution and trends of data, thereby enabling the calculation of the mean, standard deviation, and correlation of data. For example, it can statistically analyze user input data to understand user behavior patterns at specific times and locations. Furthermore, the analysis department can also analyze information using machine learning algorithms. Machine learning algorithms are methods for learning from data and making predictions and classifications, thereby recognizing data patterns and predicting future trends. For example, it can predict what actions a user will take next based on their behavior history. This allows the analysis department to analyze the collected data from multiple perspectives and gain a deep understanding of the information across the entire system. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback by, for example, sending notifications. Specifically, it can send push notifications to the user's smartphone or tablet to convey important information and alerts in real time. For example, if an environmental sensor detects an abnormal temperature rise, it can immediately send a notification to the user to prompt appropriate action. The feedback unit can also provide feedback by issuing alerts. Alerts are a means of informing the user of an emergency through methods such as sound, vibration, or screen display, allowing the user to respond quickly. For example, if a fire sensor detects smoke, it can issue an audio alert to the user to prompt evacuation. Furthermore, the feedback unit can also provide feedback by generating reports. Reports are documents that summarize detailed information and analysis results, allowing the user to understand the situation in detail and make appropriate decisions. For example, through regularly generated environmental monitoring reports, the user can review past data and trends and take necessary measures. In this way, the feedback unit can provide feedback to the user through various means, maximizing the effectiveness of the entire system. Furthermore, the feedback unit can collect user feedback and use it to improve the system. For example, it can analyze user feedback to improve the accuracy of notifications and alerts. The feedback unit can also reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the feedback unit to provide users with quick and reliable instructions, improving the overall reliability and effectiveness of the system.

[0033] The learning unit learns user behavior. For example, it can track user behavior using machine learning algorithms. Specifically, it tracks user movement patterns and activity history in real time based on data collected from the user's smartphone or wearable device. This allows for a detailed understanding of user behavior patterns and the provision of services best suited to each individual user. The learning unit can also analyze user behavior patterns. For example, if a user tends to visit a specific place at a specific time of day, the learning unit can analyze this pattern to understand the user's preferences and habits. This allows the system to provide personalized services tailored to the user's needs. Furthermore, the learning unit can accumulate user behavior history. This accumulated behavior history is used for predictions and analyses based on past data, providing important information for predicting future user behavior. For example, based on past behavior history, it can predict what action a user will take next and provide services at the appropriate time. In this way, the learning unit can continuously learn user behavior and improve the overall system performance. Furthermore, the learning unit can incorporate user feedback to improve the accuracy of its learning algorithms. For example, it can adjust algorithm parameters based on user feedback to make more accurate predictions. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual behavioral patterns and issue warnings early. This allows the learning unit to gain a detailed understanding of user behavior and improve the overall reliability and security of the system.

[0034] The haptic unit provides haptic feedback. The haptic unit can provide haptic feedback using, for example, a vibration motor. The haptic unit can transmit different information by changing the intensity and pattern of vibration. For example, the haptic unit can transmit the intensity of sound by changing the intensity of vibration. The haptic unit can also transmit the direction of sound by changing the pattern of vibration. The haptic unit can also provide spatial information by combining the intensity and pattern of vibration. For example, the haptic unit can transmit the intensity of sound by changing the intensity of vibration. The haptic unit can also transmit the direction of sound by changing the pattern of vibration. The haptic unit can also provide spatial information by combining the intensity and pattern of vibration. This allows information to be transmitted to the user through haptic feedback. Haptic feedback is provided by methods such as vibration, pressure, and temperature changes. Some or all of the above processing in the haptic unit may be performed using, for example, AI, or not using AI. For example, the tactile unit can input the control of the vibration motor into a generating AI, which can then execute the generation of vibration patterns.

[0035] The visual unit processes visual information. The visual unit can acquire visual information, for example, using a camera. The visual unit can process the acquired visual information in real time and provide it to the user. For example, the visual unit can display subtitles in real time using AR glasses. The visual unit can also visually display the location and number of speakers. The visual unit can also perform emotion analysis and visual representation through facial recognition. For example, the visual unit can identify the location of speakers using a camera and display it on AR glasses. The visual unit can also count the number of speakers and display it visually. The visual unit can analyze the emotions of speakers using facial recognition technology and visually display the results. In this way, by processing visual information, it is possible to provide visual information to the user. Visual information includes, for example, images, videos, graphics, etc. Some or all of the processing described above in the visual unit may be performed using, for example, AI, or not using AI. For example, the visual unit can input image data acquired by a camera into a generating AI and have the generating AI perform image processing.

[0036] The environmental recognition unit recognizes the environment. The environmental recognition unit can recognize the surrounding situation using sensors, for example. The environmental recognition unit can analyze the recognized environmental information and provide it to the user. For example, the environmental recognition unit can recognize the ambient temperature using a temperature sensor and notify the user. The environmental recognition unit can also recognize ambient sounds using a sound sensor and prioritize the notification of important information. The environmental recognition unit can also automatically detect emergencies and issue warnings. For example, the environmental recognition unit can recognize the ambient temperature using a temperature sensor and notify the user if it detects an abnormal temperature change. The environmental recognition unit can also recognize ambient sounds using a sound sensor and prioritize the notification of important information. The environmental recognition unit can also automatically detect emergencies and issue warnings. In this way, the surrounding situation can be communicated to the user through environmental recognition. The environment includes, for example, the physical environment, the social environment, and the digital environment. Some or all of the above-described processing in the environmental recognition unit may be performed using AI, for example, or without AI. For example, the environmental recognition unit can input environmental data acquired by sensors into a generating AI and have the generating AI perform the environmental recognition processing.

[0037] The personalization unit selects an information delivery method according to the individual's disability. For example, the personalization unit can analyze the user's disability and select the optimal information delivery method. The personalization unit can also learn the user's usage patterns and automatically adjust the information delivery method. The personalization unit can also customize the information delivery method according to the user's preferences. For example, the personalization unit can analyze the user's disability and select the optimal information delivery method from among visual information, tactile information, audio information, etc. The personalization unit can also learn the user's usage patterns and automatically adjust the information delivery method according to the frequency and circumstances of use. The personalization unit can also customize the information delivery method according to the user's preferences. This makes it possible to provide an optimal information delivery method according to the individual's disability. Disability conditions include, for example, visual impairment, hearing impairment, physical disability, etc. Some or all of the above processing in the personalization unit may be performed using, for example, AI, or not using AI. For example, the personalization unit can input the user's disability condition into a generating AI and have the generating AI select the optimal information delivery method.

[0038] The data collection unit can collect information by utilizing sensory organs other than hearing. For example, the data collection unit can collect visual information using a visual sensor. The data collection unit can also collect tactile information using a tactile sensor. The data collection unit can also collect olfactory information using an olfactory sensor. For example, the data collection unit can collect visual information using a camera. The data collection unit can also collect tactile information using a tactile sensor. The data collection unit can also collect olfactory information using an olfactory sensor. This broadens the scope of information collection by utilizing sensory organs other than hearing. Sensory organs include, for example, vision, touch, and smell. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input visual information acquired by a visual sensor into a generating AI and have the generating AI process the visual information.

[0039] The feedback unit can provide customized feedback tailored to the user's preferences. For example, the feedback unit can adjust the content and format of feedback based on user settings. The feedback unit can also dynamically adjust the content of feedback according to the user's usage. The feedback unit can also customize the format of feedback according to the user's preferences. For example, the feedback unit can adjust the content and format of notifications based on user settings. The feedback unit can also dynamically adjust the content of notifications according to the user's usage. The feedback unit can also customize the format of notifications according to the user's preferences. This improves the user experience by providing feedback tailored to the user's preferences. Customization can be performed, for example, through user settings or dynamic adjustments. Some or all of the above-described processes in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input user setting information into a generating AI and have the generating AI perform the customization of the feedback content.

[0040] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect the user's behavior history using log data. The data collection unit can also collect the user's behavior history using sensor data. The data collection unit can analyze the collected behavior history and select the optimal information collection method. For example, the data collection unit can prioritize information collection methods that the user has frequently used in the past. The data collection unit can also analyze the user's behavior patterns and propose the most efficient information collection method. The data collection unit can also select the optimal information collection method for a specific time period from the user's past behavior history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Behavior history includes, for example, specific collection methods and analysis methods such as log data and sensor data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input log data into a generating AI and have the generating AI perform the analysis of the behavior history.

[0041] The data collection unit can filter information based on the user's current environment and circumstances during data collection. For example, the data collection unit can filter information considering the physical environment. It can also filter information considering the time of day. It can also filter information considering the user's location. For example, if the user is in a quiet environment, the data collection unit can prioritize collecting audio information. If the user is on the move, the data collection unit can prioritize collecting visual information. If the user is in a meeting, the data collection unit can filter and collect only important information. This allows for more appropriate information collection by filtering information based on the user's environment and circumstances. The environment and circumstances include specific scopes and filtering methods such as physical environment, time of day, and location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's environment data into a generating AI and have the generating AI perform information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can obtain the user's geographical location information using GPS data. The data collection unit can also obtain the user's geographical location information using location-based services. Based on the acquired geographical location information, the data collection unit can prioritize the collection of highly relevant information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. If the user is traveling, the data collection unit can prioritize the collection of information related to the travel destination. If the user is at home, the data collection unit can prioritize the collection of information around the user's home. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Geographical location information includes, for example, specific collection methods and considerations such as GPS data and location-based services. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input GPS data into a generating AI and have the generating AI select highly relevant information.

[0043] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of social media posts. The data collection unit can also analyze a user's likes and comments. The data collection unit can also analyze the content of posts from accounts the user follows. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also analyze the content of posts from accounts the user follows and collect relevant information. The data collection unit can also analyze the activities of groups and communities the user participates in and collect relevant information. In this way, relevant information can be collected by analyzing a user's social media activity. Social media activity includes specific analysis and collection methods such as content of posts, likes, and comments. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the collection of relevant information.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can evaluate the importance of information by considering the user's level of interest. The analysis unit can also evaluate the importance of information by considering its urgency. The analysis unit can also evaluate the importance of information by considering its relevance. For example, the analysis unit can perform a detailed analysis on important information. The analysis unit can perform a concise analysis on general information. The analysis unit can also perform a rapid analysis on information that is highly urgent. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. The importance of information includes specific evaluation methods and criteria such as the user's level of interest, urgency, and relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a speech recognition algorithm to audio information. For example, the analysis unit can apply a natural language processing algorithm to text information to analyze the information. The analysis unit can also apply an image recognition algorithm to image information to analyze the information. The analysis unit can also apply a speech recognition algorithm to audio information to analyze the information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. The category of information includes, for example, specific classification methods such as text, images, and audio, and the algorithms to be applied. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also prioritize the analysis of information collected during a specific period. For example, the analysis unit can prioritize the analysis of the most recent information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also prioritize the analysis of information collected during a specific period. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on when the information was collected. The timing of information collection includes specific considerations and methods for determining priority, such as the latest information and historical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can evaluate relevance by considering the context of the information. The analysis unit can also evaluate relevance by considering the user's level of interest. The analysis unit can also evaluate relevance by considering the importance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the information. The relevance of information includes specific evaluation methods and criteria such as context, user level of interest, and importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The feedback unit can adjust the level of detail in the feedback based on the importance of the information. For example, the feedback unit can evaluate the importance of information by considering the user's level of interest. The feedback unit can also evaluate the importance of information by considering its urgency. The feedback unit can also evaluate the importance of information by considering its relevance. For example, the feedback unit can provide detailed feedback for important information. The feedback unit can provide concise feedback for general information. The feedback unit can also provide rapid feedback for highly urgent information. By adjusting the level of detail in the feedback based on the importance of the information, more appropriate feedback can be provided. The importance of information includes specific evaluation methods and criteria such as the user's level of interest, urgency, and relevance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information importance data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0049] The feedback unit can apply different feedback algorithms depending on the category of information during the feedback process. For example, the feedback unit can apply a natural language processing algorithm to text information. The feedback unit can also apply an image recognition algorithm to image information. The feedback unit can also apply a speech recognition algorithm to audio information. For example, the feedback unit can apply a natural language processing algorithm to text information to analyze the information. The feedback unit can also apply an image recognition algorithm to image information to analyze the information. The feedback unit can also apply a speech recognition algorithm to audio information to analyze the information. This allows for more appropriate feedback to be provided by applying different feedback algorithms depending on the category of information. Feedback algorithms include specific types and application methods such as rule-based and machine learning-based algorithms. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information category data into a generating AI and cause the generating AI to apply an appropriate feedback algorithm.

[0050] The feedback unit can determine the priority of feedback based on when the information was collected. For example, the feedback unit can prioritize the most recent information. The feedback unit can also provide feedback on current information while referring to past information. The feedback unit can also prioritize information collected during a specific period. For example, the feedback unit can prioritize the most recent information. The feedback unit can also provide feedback on current information while referring to past information. The feedback unit can also prioritize information collected during a specific period. This allows for more appropriate feedback to be provided by determining the priority of feedback based on when the information was collected. The priority of feedback includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the feedback priority.

[0051] The feedback unit can adjust the order of feedback based on the relevance of the information during the feedback process. For example, the feedback unit can evaluate relevance by considering the context of the information. The feedback unit can also evaluate relevance by considering the user's level of interest. The feedback unit can also evaluate relevance by considering the importance of the information. For example, the feedback unit can prioritize feedback on highly relevant information. The feedback unit can also postpone feedback on less relevant information. The feedback unit can also dynamically adjust the order of feedback based on the relevance of the information. This allows for the provision of more appropriate feedback by adjusting the order of feedback based on the relevance of the information. The order of feedback includes specific adjustment methods and criteria, such as relevance and importance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0052] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. The learning unit can also analyze past learning data and dynamically adjust the learning algorithm. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. The learning unit can also analyze past learning data and dynamically adjust the learning algorithm. This improves learning efficiency by optimizing the learning algorithm by referring to past learning data. Optimization of the learning algorithm includes specific methods and criteria such as parameter tuning and model selection. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0053] The learning unit can weight the training data based on when the information was collected during training. For example, the learning unit can give higher weight to the most recent information. The learning unit can also give lower weight to past information. The learning unit can also adjust the weighting for information collected during a specific period. For example, the learning unit can give higher weight to the most recent information. The learning unit can also give lower weight to past information. The learning unit can also adjust the weighting for information collected during a specific period. This allows for more appropriate training by weighting the training data based on when the information was collected. The weighting of the training data includes specific methods and criteria such as data importance and reliability. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on when the information was collected into a generating AI and have the generating AI perform the weighting of the training data.

[0054] The haptic unit can adjust the level of detail of haptic feedback based on the importance of the information. For example, the haptic unit can assess the importance of information by considering the user's level of interest. The haptic unit can also assess the importance of information by considering its urgency. The haptic unit can also assess the importance of information by considering its relevance. For example, the haptic unit can provide detailed haptic feedback for important information. The haptic unit can also provide concise haptic feedback for general information. The haptic unit can also provide rapid haptic feedback for highly urgent information. This allows for more appropriate feedback to be provided by adjusting the level of detail of haptic feedback based on the importance of the information. The level of detail of haptic feedback includes specific adjustment methods and criteria such as the feedback pattern and duration. Some or all of the above processing in the haptic unit may be performed using AI, for example, or without AI. For example, the haptic unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the haptic feedback.

[0055] The haptic unit can determine the priority of haptic feedback based on the timing of information collection. For example, the haptic unit can prioritize haptic feedback for the most recent information. The haptic unit can also lower the priority of haptic feedback for older information. The haptic unit can also adjust the priority of haptic feedback for information collected during a specific period. For example, the haptic unit can prioritize haptic feedback for the most recent information. The haptic unit can also lower the priority of haptic feedback for older information. The haptic unit can also adjust the priority of haptic feedback for information collected during a specific period. This allows for more appropriate feedback to be provided by determining the priority of haptic feedback based on the timing of information collection. The priority of haptic feedback includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the haptic unit may be performed using AI, for example, or not using AI. For example, the haptic unit can input information collection timing data into a generating AI and have the generating AI perform the determination of haptic feedback priority.

[0056] The visual unit can adjust the level of detail of visual information based on its importance when displaying visual information. For example, the visual unit can evaluate the importance of information by considering the user's level of interest. The visual unit can also evaluate the importance of information by considering its urgency. The visual unit can also evaluate the importance of information by considering its relevance. For example, the visual unit can provide detailed visual information for important information. The visual unit can also provide concise visual information for general information. The visual unit can also provide visual information quickly for information that is highly urgent. This allows for more appropriate information to be provided by adjusting the level of detail of visual information based on its importance. The level of detail of visual information includes specific adjustment methods and criteria, such as detailed explanations and concise summaries. Some or all of the above processing in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the visual information.

[0057] The visual unit can determine the priority of visual information based on when the information was collected when displaying visual information. For example, the visual unit can prioritize displaying the latest information. The visual unit can also display current information while referring to past information. The visual unit can also prioritize displaying information collected during a specific period. For example, the visual unit can prioritize displaying the latest information. The visual unit can also display current information while referring to past information. The visual unit can also prioritize displaying information collected during a specific period. This makes it possible to provide more appropriate information by determining the priority of visual information based on when the information was collected. The priority of visual information includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input information collection time data into a generating AI and have the generating AI perform the determination of the priority of visual information.

[0058] The environment recognition unit can optimize its environment recognition algorithm by referring to past environmental data during environment recognition. For example, the environment recognition unit can select the optimal environment recognition algorithm based on past environmental data. The environment recognition unit can also apply algorithms that improve the efficiency of environment recognition from past environmental data. The environment recognition unit can also analyze past environmental data and dynamically adjust the environment recognition algorithm. For example, the environment recognition unit can select the optimal environment recognition algorithm based on past environmental data. The environment recognition unit can also apply algorithms that improve the efficiency of environment recognition from past environmental data. The environment recognition unit can also analyze past environmental data and dynamically adjust the environment recognition algorithm. This improves the efficiency of environment recognition by optimizing the environment recognition algorithm by referring to past environmental data. Optimization of the environment recognition algorithm includes specific methods and criteria such as parameter adjustment and model selection. Some or all of the above-described processes in the environment recognition unit may be performed using AI, for example, or without AI. For example, the environment recognition unit can input past environmental data into a generating AI and have the generating AI perform the optimization of the environment recognition algorithm.

[0059] The environmental recognition unit can weight environmental recognition data based on the timing of information collection during environmental recognition. For example, the environmental recognition unit can give higher weight to the most recent information. The environmental recognition unit can also give lower weight to past information. The environmental recognition unit can also adjust the weighting for information collected during a specific period. For example, the environmental recognition unit can give higher weight to the most recent information. The environmental recognition unit can also give lower weight to past information. The environmental recognition unit can also adjust the weighting for information collected during a specific period. This enables more appropriate environmental recognition by weighting environmental recognition data based on the timing of information collection. The weighting of environmental recognition data includes specific methods and criteria such as data importance and reliability. Some or all of the above processing in the environmental recognition unit may be performed using AI, for example, or without AI. For example, the environmental recognition unit can input information collection timing data into a generating AI and have the generating AI perform the weighting of environmental recognition data.

[0060] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during personalization. For example, the personalization unit can select the optimal personalization method based on the user's past behavior history. The personalization unit can also analyze the user's behavior patterns and propose the most efficient personalization method. The personalization unit can also select the optimal personalization method for a specific time period based on the user's past behavior history. For example, the personalization unit can select the optimal personalization method based on the user's past behavior history. The personalization unit can also analyze the user's behavior patterns and propose the most efficient personalization method. The personalization unit can also select the optimal personalization method for a specific time period based on the user's past behavior history. This makes it possible to personalize more appropriately by selecting the optimal personalization method by referring to the user's past behavior history. Personalization methods include specific selection methods and criteria such as the user's past behavior history and current situation. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0061] The personalization unit can select the optimal personalization method when personalizing, taking into account the user's geographical location information. For example, the personalization unit can select the optimal personalization method based on the user's geographical location information. The personalization unit can also adjust the personalization method considering the user's geographical location information. The personalization unit can also prioritize providing highly relevant information based on the user's geographical location information. For example, if the user is in a specific region, the personalization unit can select a personalization method related to that region. If the user is traveling, the personalization unit can select a personalization method related to the travel destination. If the user is at home, the personalization unit can select a personalization method based on information around the user's home. This allows for more appropriate personalization by selecting the optimal personalization method considering the user's geographical location information. Geographical location information includes, for example, specific collection methods and consideration methods such as GPS data and location-based services. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal personalization method.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze what information users have collected in the past based on their behavioral history and reflect this in current data collection. For example, the data collection unit can prioritize collecting information from sources that users have frequently accessed in the past. Furthermore, the data collection unit can collect information appropriate for a given time period based on the information users accessed during that time. In addition, the data collection unit can analyze user behavior patterns and optimize data collection methods under specific circumstances. This enables more appropriate data collection by leveraging user behavioral history.

[0064] The visual unit can adjust how user visual information is displayed according to the user's current activity. For example, when the user is driving, the visual unit can display important information in a larger, more visually prominent size. When the user is in a meeting, the visual unit can prioritize displaying information relevant to the meeting's content. Furthermore, when the user is relaxed, the visual unit can display visual information in calming colors. This enables the provision of visual information tailored to the user's activity.

[0065] The personalization function can select the optimal method of information delivery based on the user's geographical location. For example, if the user is in a specific region, the personalization function can prioritize providing information related to that region. If the user is traveling, it can also provide information related to their travel destination. Furthermore, if the user is at home, it can select the method of information delivery based on information about the area around their home. This enables information delivery that takes the user's geographical location into consideration.

[0066] The feedback section can provide customized feedback tailored to the user's preferences. For example, the feedback section can adjust the content and format of notifications based on the user's settings. It can also dynamically adjust the content of notifications according to the user's usage. Furthermore, it can customize the format of notifications according to the user's preferences. This improves the user experience by providing feedback tailored to the user's preferences.

[0067] The learning unit can optimize its learning algorithm by referring to the user's past behavior history. For example, the learning unit can select the optimal learning algorithm based on past behavior history. It can also apply algorithms that improve learning efficiency based on past behavior history. Furthermore, it can analyze past behavior history and dynamically adjust the learning algorithm. As a result, learning efficiency is improved by optimizing the learning algorithm by referring to past behavior history.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection unit collects information. The collection unit can collect environmental information using sensors, for example. It can also collect information through user input. Furthermore, it can collect information from the internet. For example, the collection unit can collect ambient temperature information using a temperature sensor. It can also collect text information entered by the user. It can also collect the latest news information from the internet. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can extract useful information from large amounts of data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also recognize data patterns using machine learning algorithms. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback, for example, by sending notifications. It can also provide feedback by issuing alerts. Furthermore, the feedback unit can provide feedback by generating reports. For example, the feedback unit can convey important information to users by sending notifications. The feedback unit can also notify users of emergencies by issuing alerts. The feedback unit can also provide detailed information by generating reports. Step 4: The learning unit learns user behavior. The learning unit can, for example, track user behavior using machine learning algorithms. It can also analyze user behavior patterns. Furthermore, the learning unit can accumulate user behavior history. For example, the learning unit can track user behavior in real time using machine learning algorithms. By analyzing user behavior patterns, the learning unit can understand user preferences and tendencies. By accumulating user behavior history, the learning unit can also make predictions based on past data.

[0070] (Example of form 2) The SenseConnect system according to an embodiment of the present invention is an innovative wearable device that transmits information by utilizing sensory organs other than hearing. This system can select the optimal information transmission method according to the individual's disability and preferences. Furthermore, an autonomous AI agent learns the user's behavior and provides optimal feedback to continuously improve the user experience. This device primarily targets people with hearing impairments and addresses the various challenges they face. For example, the degree of impairment varies greatly, including people who are completely deaf, people who can hear but communicate through writing, and people who can communicate through oral communication. The SenseConnect system goes beyond being a mere hearing assistance device to provide a comprehensive communication support system that utilizes the diverse senses that humans possess. This aims to realize a society where all people can communicate comfortably, regardless of the type or degree of hearing impairment. For example, the SenseConnect system is equipped with a haptic feedback function, a visual information processing system, AI-based environmental recognition, and personalization functions. The haptic feedback function transmits the strength and direction of sound through vibration patterns and provides spatial information through changes in vibration intensity in different parts of the body. It also expresses the speaker's emotions and intonation through changes in vibration patterns. The visual information processing system provides real-time subtitle display through AR glasses, visually displays the location and number of speakers, and performs emotion analysis and visual representation through facial recognition. AI-powered environmental recognition analyzes the surrounding environment, prioritizes notification of important information, and automatically detects and warns of emergencies. Personalization features allow for the selection of the optimal information delivery method according to the individual's disability, automatic adjustment through learning of usage patterns, and customization according to user preferences. Potential application scenarios include educational settings, workplaces, and daily life. For example, in educational settings, lecture content can be converted into real-time visual and tactile information, allowing for intuitive understanding of the location and order of speakers in group discussions. In workplaces, the content of meetings can be understood through multiple senses, enabling reliable transmission of emergency contacts and important notices. In daily life, it can support conversations with family, receive guidance information in public facilities, and provide evacuation guidance in emergencies.Thus, the SenseConnect system brings about social impacts for the hearing impaired, including comprehensive information access, improved safety, enhanced social participation, and expanded educational and employment opportunities. It provides an environment where everyone, regardless of the type or degree of hearing impairment, can receive information, enabling reliable danger detection and rapid situational awareness in emergencies through information transmission via multiple senses. Furthermore, by reducing communication barriers and facilitating more natural interpersonal interaction, it promotes barrier-free learning environments and smoother communication in the workplace. In summary, the SenseConnect system can bring about social impacts for the hearing impaired, including comprehensive information access, improved safety, enhanced social participation, and expanded educational and employment opportunities.

[0071] The SenseConnect system according to this embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a learning unit. The data collection unit collects information. For example, the data collection unit can collect environmental information using sensors. The data collection unit can also collect information through user input. Furthermore, the data collection unit can collect information from the internet. For example, the data collection unit can collect ambient temperature information using a temperature sensor. The data collection unit can also collect text information entered by a user. The data collection unit can also collect the latest news information from the internet. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze the information using data mining techniques. Furthermore, the analysis unit can analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can extract useful information from a large amount of data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also recognize data patterns using machine learning algorithms. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback, for example, by sending notifications. Furthermore, the feedback unit can provide feedback by issuing alerts. Additionally, the feedback unit can provide feedback by generating reports. For example, the feedback unit can convey important information by sending notifications to users. The feedback unit can also notify users of emergencies by issuing alerts. The feedback unit can also provide detailed information by generating reports. The learning unit learns user behavior. For example, the learning unit can track user behavior using machine learning algorithms. The learning unit can also analyze user behavior patterns. Furthermore, the learning unit can accumulate user behavior history. For example, the learning unit can track user behavior in real time using machine learning algorithms.The learning unit can also understand user preferences and tendencies by analyzing user behavior patterns. The learning unit can also make predictions based on past data by accumulating user behavior history. As a result, the SenseConnect system according to this embodiment can improve the user experience through information collection, analysis, feedback, and learning.

[0072] The data collection unit collects information. For example, it can collect environmental information using sensors. Specifically, it can collect detailed information about the surrounding environment using a wide variety of sensors, such as temperature sensors, humidity sensors, light sensors, and sound sensors. These sensors acquire data in real time and transmit it to a central database. For example, a temperature sensor can continuously monitor ambient temperature and detect abnormal temperature changes. A humidity sensor monitors humidity fluctuations and provides data to maintain a comfortable environment. A light sensor measures ambient brightness and can be used for automatic lighting adjustment. A sound sensor detects ambient noise levels and provides data for noise reduction measures. The data collection unit can also collect information through user input. For example, it collects text and audio information entered by users via smartphones or tablets and uses this for analysis. Furthermore, the data collection unit can collect information from the internet. For example, it collects the latest news and trend information from news sites and social media on the internet and incorporates it into the system. This allows the data collection unit to collect a wide range of data from diverse sources, strengthening the information infrastructure of the entire system. The collected data is updated in real time and can be linked with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and feedback departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis department analyzes the information collected by the data collection department. For example, the analysis department can analyze information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, thereby enabling the discovery of hidden trends and outliers. For example, it can analyze environmental data collected from sensors to identify temperature and humidity fluctuation patterns under specific conditions. The analysis department can also analyze information using statistical analysis techniques. Statistical analysis techniques are methods for understanding the distribution and trends of data, thereby enabling the calculation of the mean, standard deviation, and correlation of data. For example, it can statistically analyze user input data to understand user behavior patterns at specific times and locations. Furthermore, the analysis department can also analyze information using machine learning algorithms. Machine learning algorithms are methods for learning from data and making predictions and classifications, thereby recognizing data patterns and predicting future trends. For example, it can predict what actions a user will take next based on their behavior history. This allows the analysis department to analyze the collected data from multiple perspectives and gain a deep understanding of the information across the entire system. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0074] The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback by, for example, sending notifications. Specifically, it can send push notifications to the user's smartphone or tablet to convey important information and alerts in real time. For example, if an environmental sensor detects an abnormal temperature rise, it can immediately send a notification to the user to prompt appropriate action. The feedback unit can also provide feedback by issuing alerts. Alerts are a means of informing the user of an emergency through methods such as sound, vibration, or screen display, allowing the user to respond quickly. For example, if a fire sensor detects smoke, it can issue an audio alert to the user to prompt evacuation. Furthermore, the feedback unit can also provide feedback by generating reports. Reports are documents that summarize detailed information and analysis results, allowing the user to understand the situation in detail and make appropriate decisions. For example, through regularly generated environmental monitoring reports, the user can review past data and trends and take necessary measures. In this way, the feedback unit can provide feedback to the user through various means, maximizing the effectiveness of the entire system. Furthermore, the feedback unit can collect user feedback and use it to improve the system. For example, it can analyze user feedback to improve the accuracy of notifications and alerts. The feedback unit can also reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the feedback unit to provide users with quick and reliable instructions, improving the overall reliability and effectiveness of the system.

[0075] The learning unit learns user behavior. For example, it can track user behavior using machine learning algorithms. Specifically, it tracks user movement patterns and activity history in real time based on data collected from the user's smartphone or wearable device. This allows for a detailed understanding of user behavior patterns and the provision of services best suited to each individual user. The learning unit can also analyze user behavior patterns. For example, if a user tends to visit a specific place at a specific time of day, the learning unit can analyze this pattern to understand the user's preferences and habits. This allows the system to provide personalized services tailored to the user's needs. Furthermore, the learning unit can accumulate user behavior history. This accumulated behavior history is used for predictions and analyses based on past data, providing important information for predicting future user behavior. For example, based on past behavior history, it can predict what action a user will take next and provide services at the appropriate time. In this way, the learning unit can continuously learn user behavior and improve the overall system performance. Furthermore, the learning unit can incorporate user feedback to improve the accuracy of its learning algorithms. For example, it can adjust algorithm parameters based on user feedback to make more accurate predictions. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual behavioral patterns and issue warnings early. This allows the learning unit to gain a detailed understanding of user behavior and improve the overall reliability and security of the system.

[0076] The haptic unit provides haptic feedback. The haptic unit can provide haptic feedback using, for example, a vibration motor. The haptic unit can transmit different information by changing the intensity and pattern of vibration. For example, the haptic unit can transmit the intensity of sound by changing the intensity of vibration. The haptic unit can also transmit the direction of sound by changing the pattern of vibration. The haptic unit can also provide spatial information by combining the intensity and pattern of vibration. For example, the haptic unit can transmit the intensity of sound by changing the intensity of vibration. The haptic unit can also transmit the direction of sound by changing the pattern of vibration. The haptic unit can also provide spatial information by combining the intensity and pattern of vibration. This allows information to be transmitted to the user through haptic feedback. Haptic feedback is provided by methods such as vibration, pressure, and temperature changes. Some or all of the above processing in the haptic unit may be performed using, for example, AI, or not using AI. For example, the tactile unit can input the control of the vibration motor into a generating AI, which can then execute the generation of vibration patterns.

[0077] The visual unit processes visual information. The visual unit can acquire visual information, for example, using a camera. The visual unit can process the acquired visual information in real time and provide it to the user. For example, the visual unit can display subtitles in real time using AR glasses. The visual unit can also visually display the location and number of speakers. The visual unit can also perform emotion analysis and visual representation through facial recognition. For example, the visual unit can identify the location of speakers using a camera and display it on AR glasses. The visual unit can also count the number of speakers and display it visually. The visual unit can analyze the emotions of speakers using facial recognition technology and visually display the results. In this way, by processing visual information, it is possible to provide visual information to the user. Visual information includes, for example, images, videos, graphics, etc. Some or all of the processing described above in the visual unit may be performed using, for example, AI, or not using AI. For example, the visual unit can input image data acquired by a camera into a generating AI and have the generating AI perform image processing.

[0078] The environmental recognition unit recognizes the environment. The environmental recognition unit can recognize the surrounding situation using sensors, for example. The environmental recognition unit can analyze the recognized environmental information and provide it to the user. For example, the environmental recognition unit can recognize the ambient temperature using a temperature sensor and notify the user. The environmental recognition unit can also recognize ambient sounds using a sound sensor and prioritize the notification of important information. The environmental recognition unit can also automatically detect emergencies and issue warnings. For example, the environmental recognition unit can recognize the ambient temperature using a temperature sensor and notify the user if it detects an abnormal temperature change. The environmental recognition unit can also recognize ambient sounds using a sound sensor and prioritize the notification of important information. The environmental recognition unit can also automatically detect emergencies and issue warnings. In this way, the surrounding situation can be communicated to the user through environmental recognition. The environment includes, for example, the physical environment, the social environment, and the digital environment. Some or all of the above-described processing in the environmental recognition unit may be performed using AI, for example, or without AI. For example, the environmental recognition unit can input environmental data acquired by sensors into a generating AI and have the generating AI perform the environmental recognition processing.

[0079] The personalization unit selects an information delivery method according to the individual's disability. For example, the personalization unit can analyze the user's disability and select the optimal information delivery method. The personalization unit can also learn the user's usage patterns and automatically adjust the information delivery method. The personalization unit can also customize the information delivery method according to the user's preferences. For example, the personalization unit can analyze the user's disability and select the optimal information delivery method from among visual information, tactile information, audio information, etc. The personalization unit can also learn the user's usage patterns and automatically adjust the information delivery method according to the frequency and circumstances of use. The personalization unit can also customize the information delivery method according to the user's preferences. This makes it possible to provide an optimal information delivery method according to the individual's disability. Disability conditions include, for example, visual impairment, hearing impairment, physical disability, etc. Some or all of the above processing in the personalization unit may be performed using, for example, AI, or not using AI. For example, the personalization unit can input the user's disability condition into a generating AI and have the generating AI select the optimal information delivery method.

[0080] The data collection unit can collect information by utilizing sensory organs other than hearing. For example, the data collection unit can collect visual information using a visual sensor. The data collection unit can also collect tactile information using a tactile sensor. The data collection unit can also collect olfactory information using an olfactory sensor. For example, the data collection unit can collect visual information using a camera. The data collection unit can also collect tactile information using a tactile sensor. The data collection unit can also collect olfactory information using an olfactory sensor. This broadens the scope of information collection by utilizing sensory organs other than hearing. Sensory organs include, for example, vision, touch, and smell. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input visual information acquired by a visual sensor into a generating AI and have the generating AI process the visual information.

[0081] The feedback unit can provide customized feedback tailored to the user's preferences. For example, the feedback unit can adjust the content and format of feedback based on user settings. The feedback unit can also dynamically adjust the content of feedback according to the user's usage. The feedback unit can also customize the format of feedback according to the user's preferences. For example, the feedback unit can adjust the content and format of notifications based on user settings. The feedback unit can also dynamically adjust the content of notifications according to the user's usage. The feedback unit can also customize the format of notifications according to the user's preferences. This improves the user experience by providing feedback tailored to the user's preferences. Customization can be performed, for example, through user settings or dynamic adjustments. Some or all of the above-described processes in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input user setting information into a generating AI and have the generating AI perform the customization of the feedback content.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the data collection unit can estimate the user's emotions using facial recognition technology and reduce the frequency of information collection if the user is stressed. It can also estimate the user's emotions using voice analysis technology and temporarily stop information collection if the user is concentrating. Finally, it can estimate the user's emotions using biosignals and refrain from information collection if the user is tired. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0083] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect the user's behavior history using log data. The data collection unit can also collect the user's behavior history using sensor data. The data collection unit can analyze the collected behavior history and select the optimal information collection method. For example, the data collection unit can prioritize information collection methods that the user has frequently used in the past. The data collection unit can also analyze the user's behavior patterns and propose the most efficient information collection method. The data collection unit can also select the optimal information collection method for a specific time period from the user's past behavior history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Behavior history includes, for example, specific collection methods and analysis methods such as log data and sensor data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input log data into a generating AI and have the generating AI perform the analysis of the behavior history.

[0084] The data collection unit can filter information based on the user's current environment and circumstances during data collection. For example, the data collection unit can filter information considering the physical environment. It can also filter information considering the time of day. It can also filter information considering the user's location. For example, if the user is in a quiet environment, the data collection unit can prioritize collecting audio information. If the user is on the move, the data collection unit can prioritize collecting visual information. If the user is in a meeting, the data collection unit can filter and collect only important information. This allows for more appropriate information collection by filtering information based on the user's environment and circumstances. The environment and circumstances include specific scopes and filtering methods such as physical environment, time of day, and location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's environment data into a generating AI and have the generating AI perform information filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the data collection unit can estimate the user's emotions using facial recognition technology and prioritize collecting information that promotes relaxation if the user is tense. It can also estimate the user's emotions using voice analysis technology and prioritize collecting information that attracts interest if the user is excited. Finally, it can estimate the user's emotions using biosignals and prioritize collecting information that promotes refreshment if the user is tired. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0086] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can obtain the user's geographical location information using GPS data. The data collection unit can also obtain the user's geographical location information using location-based services. Based on the acquired geographical location information, the data collection unit can prioritize the collection of highly relevant information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. If the user is traveling, the data collection unit can prioritize the collection of information related to the travel destination. If the user is at home, the data collection unit can prioritize the collection of information around the user's home. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Geographical location information includes, for example, specific collection methods and considerations such as GPS data and location-based services. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input GPS data into a generating AI and have the generating AI select highly relevant information.

[0087] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of social media posts. The data collection unit can also analyze a user's likes and comments. The data collection unit can also analyze the content of posts from accounts the user follows. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also analyze the content of posts from accounts the user follows and collect relevant information. The data collection unit can also analyze the activities of groups and communities the user participates in and collect relevant information. In this way, relevant information can be collected by analyzing a user's social media activity. Social media activity includes specific analysis and collection methods such as content of posts, likes, and comments. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the collection of relevant information.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the analysis unit can estimate the user's emotions using facial recognition technology and provide detailed analysis results if the user is relaxed. It can also estimate the user's emotions using voice analysis technology and provide concise analysis results that get straight to the point if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and provide analysis results using visually stimulating expressions if the user is excited. This allows for more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0089] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can evaluate the importance of information by considering the user's level of interest. The analysis unit can also evaluate the importance of information by considering its urgency. The analysis unit can also evaluate the importance of information by considering its relevance. For example, the analysis unit can perform a detailed analysis on important information. The analysis unit can perform a concise analysis on general information. The analysis unit can also perform a rapid analysis on information that is highly urgent. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. The importance of information includes specific evaluation methods and criteria such as the user's level of interest, urgency, and relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a speech recognition algorithm to audio information. For example, the analysis unit can apply a natural language processing algorithm to text information to analyze the information. The analysis unit can also apply an image recognition algorithm to image information to analyze the information. The analysis unit can also apply a speech recognition algorithm to audio information to analyze the information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. The category of information includes, for example, specific classification methods such as text, images, and audio, and the algorithms to be applied. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the analysis unit can estimate the user's emotions using facial recognition technology and provide a short, concise analysis if the user is in a hurry. It can also estimate the user's emotions using voice analysis technology and provide a detailed analysis if the user is relaxed. Finally, it can estimate the user's emotions using biosignals and provide an analysis using visually stimulating expressions if the user is excited. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0092] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also prioritize the analysis of information collected during a specific period. For example, the analysis unit can prioritize the analysis of the most recent information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also prioritize the analysis of information collected during a specific period. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on when the information was collected. The timing of information collection includes specific considerations and methods for determining priority, such as the latest information and historical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can evaluate relevance by considering the context of the information. The analysis unit can also evaluate relevance by considering the user's level of interest. The analysis unit can also evaluate relevance by considering the importance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the information. The relevance of information includes specific evaluation methods and criteria such as context, user level of interest, and importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the feedback unit can estimate the user's emotions using facial recognition technology and provide detailed feedback if the user is relaxed. It can also estimate the user's emotions using voice analysis technology and provide concise, to the point if the user is in a hurry. It can also estimate the user's emotions using biosignals and provide visually stimulating feedback if the user is excited. This allows for more appropriate feedback by adjusting the way it expresses the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data acquired by facial recognition technology into a generating AI and cause the generating AI to perform emotion estimation.

[0095] The feedback unit can adjust the level of detail in the feedback based on the importance of the information. For example, the feedback unit can evaluate the importance of information by considering the user's level of interest. The feedback unit can also evaluate the importance of information by considering its urgency. The feedback unit can also evaluate the importance of information by considering its relevance. For example, the feedback unit can provide detailed feedback for important information. The feedback unit can provide concise feedback for general information. The feedback unit can also provide rapid feedback for highly urgent information. By adjusting the level of detail in the feedback based on the importance of the information, more appropriate feedback can be provided. The importance of information includes specific evaluation methods and criteria such as the user's level of interest, urgency, and relevance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information importance data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0096] The feedback unit can apply different feedback algorithms depending on the category of information during the feedback process. For example, the feedback unit can apply a natural language processing algorithm to text information. The feedback unit can also apply an image recognition algorithm to image information. The feedback unit can also apply a speech recognition algorithm to audio information. For example, the feedback unit can apply a natural language processing algorithm to text information to analyze the information. The feedback unit can also apply an image recognition algorithm to image information to analyze the information. The feedback unit can also apply a speech recognition algorithm to audio information to analyze the information. This allows for more appropriate feedback to be provided by applying different feedback algorithms depending on the category of information. Feedback algorithms include specific types and application methods such as rule-based and machine learning-based algorithms. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information category data into a generating AI and cause the generating AI to apply an appropriate feedback algorithm.

[0097] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the feedback unit can estimate the user's emotions using facial recognition technology and provide short, concise feedback if the user is in a hurry. It can also estimate the user's emotions using voice analysis technology and provide detailed feedback if the user is relaxed. Finally, it can estimate the user's emotions using biosignals and provide feedback using visually stimulating expressions if the user is excited. This allows for more appropriate feedback by adjusting the length of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data acquired by facial recognition technology into a generating AI and cause the generating AI to perform emotion estimation.

[0098] The feedback unit can determine the priority of feedback based on when the information was collected. For example, the feedback unit can prioritize the most recent information. The feedback unit can also provide feedback on current information while referring to past information. The feedback unit can also prioritize information collected during a specific period. For example, the feedback unit can prioritize the most recent information. The feedback unit can also provide feedback on current information while referring to past information. The feedback unit can also prioritize information collected during a specific period. This allows for more appropriate feedback to be provided by determining the priority of feedback based on when the information was collected. The priority of feedback includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the feedback priority.

[0099] The feedback unit can adjust the order of feedback based on the relevance of the information during the feedback process. For example, the feedback unit can evaluate relevance by considering the context of the information. The feedback unit can also evaluate relevance by considering the user's level of interest. The feedback unit can also evaluate relevance by considering the importance of the information. For example, the feedback unit can prioritize feedback on highly relevant information. The feedback unit can also postpone feedback on less relevant information. The feedback unit can also dynamically adjust the order of feedback based on the relevance of the information. This allows for the provision of more appropriate feedback by adjusting the order of feedback based on the relevance of the information. The order of feedback includes specific adjustment methods and criteria, such as relevance and importance. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0100] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using speech analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the learning unit can estimate the user's emotions using facial recognition technology and select detailed training data if the user is relaxed. It can also estimate the user's emotions using speech analysis technology and select concise training data that gets straight to the point if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and select visually stimulating training data if the user is excited. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0101] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. The learning unit can also analyze past learning data and dynamically adjust the learning algorithm. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. The learning unit can also analyze past learning data and dynamically adjust the learning algorithm. This improves learning efficiency by optimizing the learning algorithm by referring to past learning data. Optimization of the learning algorithm includes specific methods and criteria such as parameter tuning and model selection. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0102] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. The learning unit can estimate the user's emotions using, for example, facial recognition technology. The learning unit can also estimate the user's emotions using voice analysis technology. The learning unit can also estimate the user's emotions using biosignals. For example, the learning unit can estimate the user's emotions using facial recognition technology and increase the learning frequency if the user is relaxed. The learning unit can estimate the user's emotions using voice analysis technology and decrease the learning frequency if the user is in a hurry. The learning unit can also estimate the user's emotions using biosignals and adjust the learning frequency if the user is excited. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input data acquired using facial recognition technology into a generating AI, which can then perform emotion estimation.

[0103] The learning unit can weight the training data based on when the information was collected during training. For example, the learning unit can give higher weight to the most recent information. The learning unit can also give lower weight to past information. The learning unit can also adjust the weighting for information collected during a specific period. For example, the learning unit can give higher weight to the most recent information. The learning unit can also give lower weight to past information. The learning unit can also adjust the weighting for information collected during a specific period. This allows for more appropriate training by weighting the training data based on when the information was collected. The weighting of the training data includes specific methods and criteria such as data importance and reliability. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on when the information was collected into a generating AI and have the generating AI perform the weighting of the training data.

[0104] The haptic unit can estimate the user's emotions and adjust the intensity of haptic feedback based on the estimated emotions. For example, the haptic unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the haptic unit can estimate the user's emotions using facial recognition technology and provide gentle haptic feedback when the user is relaxed. It can also estimate the user's emotions using voice analysis technology and provide strong haptic feedback when the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and provide varied haptic feedback when the user is excited. This allows for more appropriate feedback by adjusting the intensity of haptic feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the haptic unit may be performed using AI, for example, or without AI. For example, the haptic unit can input data acquired by facial recognition technology into a generating AI, and have the generating AI perform emotion estimation.

[0105] The haptic unit can adjust the level of detail of haptic feedback based on the importance of the information. For example, the haptic unit can assess the importance of information by considering the user's level of interest. The haptic unit can also assess the importance of information by considering its urgency. The haptic unit can also assess the importance of information by considering its relevance. For example, the haptic unit can provide detailed haptic feedback for important information. The haptic unit can also provide concise haptic feedback for general information. The haptic unit can also provide rapid haptic feedback for highly urgent information. This allows for more appropriate feedback to be provided by adjusting the level of detail of haptic feedback based on the importance of the information. The level of detail of haptic feedback includes specific adjustment methods and criteria such as the feedback pattern and duration. Some or all of the above processing in the haptic unit may be performed using AI, for example, or without AI. For example, the haptic unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the haptic feedback.

[0106] The haptic unit can estimate the user's emotions and adjust the haptic feedback pattern based on the estimated emotions. For example, the haptic unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the haptic unit can estimate the user's emotions using facial recognition technology and provide a gentle haptic feedback pattern when the user is relaxed. It can also estimate the user's emotions using voice analysis technology and provide a strong haptic feedback pattern when the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and provide a varied haptic feedback pattern when the user is excited. This allows for more appropriate feedback by adjusting the haptic feedback pattern according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the haptic unit may be performed using AI, for example, or without AI. For example, the haptic unit can input data acquired by facial recognition technology into a generating AI, and have the generating AI perform emotion estimation.

[0107] The haptic unit can determine the priority of haptic feedback based on the timing of information collection. For example, the haptic unit can prioritize haptic feedback for the most recent information. The haptic unit can also lower the priority of haptic feedback for older information. The haptic unit can also adjust the priority of haptic feedback for information collected during a specific period. For example, the haptic unit can prioritize haptic feedback for the most recent information. The haptic unit can also lower the priority of haptic feedback for older information. The haptic unit can also adjust the priority of haptic feedback for information collected during a specific period. This allows for more appropriate feedback to be provided by determining the priority of haptic feedback based on the timing of information collection. The priority of haptic feedback includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the haptic unit may be performed using AI, for example, or not using AI. For example, the haptic unit can input information collection timing data into a generating AI and have the generating AI perform the determination of haptic feedback priority.

[0108] The visual unit can estimate the user's emotions and adjust the display method of visual information based on the estimated user emotions. For example, the visual unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using speech analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the visual unit can estimate the user's emotions using facial recognition technology and provide detailed visual information if the user is relaxed. It can also estimate the user's emotions using speech analysis technology and provide concise visual information that gets to the point if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and provide visually stimulating visual information if the user is excited. This allows for more appropriate information to be provided by adjusting the display method of visual information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input data acquired by facial recognition technology into a generating AI, and have the generating AI perform emotion estimation.

[0109] The visual unit can adjust the level of detail of visual information based on its importance when displaying visual information. For example, the visual unit can evaluate the importance of information by considering the user's level of interest. The visual unit can also evaluate the importance of information by considering its urgency. The visual unit can also evaluate the importance of information by considering its relevance. For example, the visual unit can provide detailed visual information for important information. The visual unit can also provide concise visual information for general information. The visual unit can also provide visual information quickly for information that is highly urgent. This allows for more appropriate information to be provided by adjusting the level of detail of visual information based on its importance. The level of detail of visual information includes specific adjustment methods and criteria, such as detailed explanations and concise summaries. Some or all of the above processing in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the visual information.

[0110] The visual unit can estimate the user's emotions and adjust the display order of visual information based on the estimated emotions. For example, the visual unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using speech analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For instance, the visual unit can estimate the user's emotions using facial recognition technology and prioritize displaying detailed visual information if the user is relaxed. It can also estimate the user's emotions using speech analysis technology and prioritize displaying concise visual information if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and prioritize displaying visually stimulating visual information if the user is excited. This allows for more appropriate information to be provided by adjusting the display order of visual information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input data acquired by facial recognition technology into a generating AI, and have the generating AI perform emotion estimation.

[0111] The visual unit can determine the priority of visual information based on when the information was collected when displaying visual information. For example, the visual unit can prioritize displaying the latest information. The visual unit can also display current information while referring to past information. The visual unit can also prioritize displaying information collected during a specific period. For example, the visual unit can prioritize displaying the latest information. The visual unit can also display current information while referring to past information. The visual unit can also prioritize displaying information collected during a specific period. This makes it possible to provide more appropriate information by determining the priority of visual information based on when the information was collected. The priority of visual information includes specific determination methods and criteria such as importance, urgency, and relevance. Some or all of the above processing in the visual unit may be performed using AI, for example, or without AI. For example, the visual unit can input information collection time data into a generating AI and have the generating AI perform the determination of the priority of visual information.

[0112] The environmental recognition unit can estimate the user's emotions and adjust the accuracy of environmental recognition based on the estimated emotions. For example, the environmental recognition unit can estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions using speech analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the environmental recognition unit can estimate the user's emotions using facial expression recognition technology and perform detailed environmental recognition if the user is relaxed. It can also estimate the user's emotions using speech analysis technology and perform concise environmental recognition if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and perform environmental recognition using visually stimulating expressions if the user is excited. This allows for more appropriate environmental recognition by adjusting the accuracy of environmental recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the environment recognition unit may be performed using AI, for example, or without AI. For example, the environment recognition unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0113] The environment recognition unit can optimize its environment recognition algorithm by referring to past environmental data during environment recognition. For example, the environment recognition unit can select the optimal environment recognition algorithm based on past environmental data. The environment recognition unit can also apply algorithms that improve the efficiency of environment recognition from past environmental data. The environment recognition unit can also analyze past environmental data and dynamically adjust the environment recognition algorithm. For example, the environment recognition unit can select the optimal environment recognition algorithm based on past environmental data. The environment recognition unit can also apply algorithms that improve the efficiency of environment recognition from past environmental data. The environment recognition unit can also analyze past environmental data and dynamically adjust the environment recognition algorithm. This improves the efficiency of environment recognition by optimizing the environment recognition algorithm by referring to past environmental data. Optimization of the environment recognition algorithm includes specific methods and criteria such as parameter adjustment and model selection. Some or all of the above-described processes in the environment recognition unit may be performed using AI, for example, or without AI. For example, the environment recognition unit can input past environmental data into a generating AI and have the generating AI perform the optimization of the environment recognition algorithm.

[0114] The environmental recognition unit can estimate the user's emotions and adjust the frequency of environmental recognition based on the estimated emotions. For example, the environmental recognition unit can estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions using speech analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the environmental recognition unit can estimate the user's emotions using facial expression recognition technology and increase the frequency of environmental recognition if the user is relaxed. It can also estimate the user's emotions using speech analysis technology and decrease the frequency of environmental recognition if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and adjust the frequency of environmental recognition if the user is excited. This allows for more appropriate environmental recognition by adjusting the frequency of environmental recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the environment recognition unit may be performed using AI, for example, or without AI. For example, the environment recognition unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0115] The environmental recognition unit can weight environmental recognition data based on the timing of information collection during environmental recognition. For example, the environmental recognition unit can give higher weight to the most recent information. The environmental recognition unit can also give lower weight to past information. The environmental recognition unit can also adjust the weighting for information collected during a specific period. For example, the environmental recognition unit can give higher weight to the most recent information. The environmental recognition unit can also give lower weight to past information. The environmental recognition unit can also adjust the weighting for information collected during a specific period. This enables more appropriate environmental recognition by weighting environmental recognition data based on the timing of information collection. The weighting of environmental recognition data includes specific methods and criteria such as data importance and reliability. Some or all of the above processing in the environmental recognition unit may be performed using AI, for example, or without AI. For example, the environmental recognition unit can input information collection timing data into a generating AI and have the generating AI perform the weighting of environmental recognition data.

[0116] The personalization unit can estimate the user's emotions and adjust the personalization method based on the estimated emotions. For example, the personalization unit can estimate the user's emotions using facial recognition technology. The personalization unit can also estimate the user's emotions using voice analysis technology. The personalization unit can also estimate the user's emotions using biosignals. For example, the personalization unit can estimate the user's emotions using facial recognition technology and perform detailed personalization if the user is relaxed. The personalization unit can estimate the user's emotions using voice analysis technology and perform concise personalization if the user is in a hurry. The personalization unit can estimate the user's emotions using biosignals and perform personalization using visually stimulating expressions if the user is excited. This allows for more appropriate personalization by adjusting the personalization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data acquired using facial recognition technology into a generating AI, which can then perform emotion estimation.

[0117] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during personalization. For example, the personalization unit can select the optimal personalization method based on the user's past behavior history. The personalization unit can also analyze the user's behavior patterns and propose the most efficient personalization method. The personalization unit can also select the optimal personalization method for a specific time period based on the user's past behavior history. For example, the personalization unit can select the optimal personalization method based on the user's past behavior history. The personalization unit can also analyze the user's behavior patterns and propose the most efficient personalization method. The personalization unit can also select the optimal personalization method for a specific time period based on the user's past behavior history. This makes it possible to personalize more appropriately by selecting the optimal personalization method by referring to the user's past behavior history. Personalization methods include specific selection methods and criteria such as the user's past behavior history and current situation. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0118] The personalization unit can estimate the user's emotions and determine the priority of personalization based on the estimated emotions. For example, the personalization unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosignals. For example, the personalization unit can estimate the user's emotions using facial recognition technology and prioritize detailed personalization if the user is relaxed. It can also estimate the user's emotions using voice analysis technology and prioritize concise personalization if the user is in a hurry. Finally, it can estimate the user's emotions using biosignals and prioritize personalization using visually stimulating expressions if the user is excited. This allows for more appropriate personalization by determining the priority of personalization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data acquired by facial recognition technology into a generating AI and have the generating AI perform emotion estimation.

[0119] The personalization unit can select the optimal personalization method when personalizing, taking into account the user's geographical location information. For example, the personalization unit can select the optimal personalization method based on the user's geographical location information. The personalization unit can also adjust the personalization method considering the user's geographical location information. The personalization unit can also prioritize providing highly relevant information based on the user's geographical location information. For example, if the user is in a specific region, the personalization unit can select a personalization method related to that region. If the user is traveling, the personalization unit can select a personalization method related to the travel destination. If the user is at home, the personalization unit can select a personalization method based on information around the user's home. This allows for more appropriate personalization by selecting the optimal personalization method considering the user's geographical location information. Geographical location information includes, for example, specific collection methods and consideration methods such as GPS data and location-based services. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal personalization method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The data collection unit can analyze what information users have collected in the past based on their behavioral history and reflect this in current data collection. For example, the data collection unit can prioritize collecting information from sources that users have frequently accessed in the past. Furthermore, the data collection unit can collect information appropriate for a given time period based on the information users accessed during that time. In addition, the data collection unit can analyze user behavior patterns and optimize data collection methods under specific circumstances. This enables more appropriate data collection by leveraging user behavioral history.

[0122] The haptic unit can estimate the user's emotions and adjust the haptic feedback pattern based on those emotions. For example, if the user is relaxed, the haptic unit can provide a gentle vibration pattern. If the user is tense, it can provide a rhythmic vibration pattern. Furthermore, if the user is excited, it can provide a varied vibration pattern. This allows for more appropriate information transmission by providing haptic feedback that responds to the user's emotions.

[0123] The visual unit can adjust how user visual information is displayed according to the user's current activity. For example, when the user is driving, the visual unit can display important information in a larger, more visually prominent size. When the user is in a meeting, the visual unit can prioritize displaying information relevant to the meeting's content. Furthermore, when the user is relaxed, the visual unit can display visual information in calming colors. This enables the provision of visual information tailored to the user's activity.

[0124] The environmental recognition unit can estimate the user's emotions and adjust the accuracy of environmental recognition based on those emotions. For example, if the user is relaxed, the environmental recognition unit can provide detailed environmental information. If the user is in a hurry, it can provide only essential information concisely. Furthermore, if the user is excited, it can provide visually stimulating environmental information. This enables environmental recognition that responds to the user's emotions.

[0125] The personalization function can select the optimal method of information delivery based on the user's geographical location. For example, if the user is in a specific region, the personalization function can prioritize providing information related to that region. If the user is traveling, it can also provide information related to their travel destination. Furthermore, if the user is at home, it can select the method of information delivery based on information about the area around their home. This enables information delivery that takes the user's geographical location into consideration.

[0126] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is feeling stressed, the data collection unit can reduce the frequency of information collection. If the user is concentrating, it can temporarily stop information collection. Furthermore, if the user is tired, it can refrain from collecting information. This makes it possible to collect information in accordance with the user's emotions.

[0127] The feedback section can provide customized feedback tailored to the user's preferences. For example, the feedback section can adjust the content and format of notifications based on the user's settings. It can also dynamically adjust the content of notifications according to the user's usage. Furthermore, it can customize the format of notifications according to the user's preferences. This improves the user experience by providing feedback tailored to the user's preferences.

[0128] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide analysis results using visually stimulating expressions. This makes it possible to provide analysis results that are tailored to the user's emotions.

[0129] The learning unit can optimize its learning algorithm by referring to the user's past behavior history. For example, the learning unit can select the optimal learning algorithm based on past behavior history. It can also apply algorithms that improve learning efficiency based on past behavior history. Furthermore, it can analyze past behavior history and dynamically adjust the learning algorithm. As a result, learning efficiency is improved by optimizing the learning algorithm by referring to past behavior history.

[0130] The haptic unit can estimate the user's emotions and adjust the intensity of the haptic feedback based on those emotions. For example, if the user is relaxed, the haptic unit can provide gentle haptic feedback. If the user is in a hurry, it can provide strong haptic feedback. Furthermore, if the user is excited, it can provide varied haptic feedback. This makes it possible to provide haptic feedback that is tailored to the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects information. The collection unit can collect environmental information using sensors, for example. It can also collect information through user input. Furthermore, it can collect information from the internet. For example, the collection unit can collect ambient temperature information using a temperature sensor. It can also collect text information entered by the user. It can also collect the latest news information from the internet. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can extract useful information from large amounts of data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also recognize data patterns using machine learning algorithms. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit can provide feedback, for example, by sending notifications. It can also provide feedback by issuing alerts. Furthermore, the feedback unit can provide feedback by generating reports. For example, the feedback unit can convey important information to users by sending notifications. The feedback unit can also notify users of emergencies by issuing alerts. The feedback unit can also provide detailed information by generating reports. Step 4: The learning unit learns user behavior. The learning unit can, for example, track user behavior using machine learning algorithms. It can also analyze user behavior patterns. Furthermore, the learning unit can accumulate user behavior history. For example, the learning unit can track user behavior in real time using machine learning algorithms. By analyzing user behavior patterns, the learning unit can understand user preferences and tendencies. By accumulating user behavior history, the learning unit can also make predictions based on past data.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback unit, learning unit, haptic unit, visual unit, environment recognition unit, and personalization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information through the sensors of the smart device 14 and user input, and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The feedback unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and provides notifications and alerts to the user. The learning unit learns the user's behavior using the specific processing unit 290 of the data processing unit 12 and provides optimal feedback. The haptic unit provides haptic feedback using the vibration motor of the smart device 14. The visual unit processes visual information using the camera of the smart device 14 or AR glasses and provides it to the user. The environment recognition unit recognizes the surrounding situation using the sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The personalization unit analyzes the user's fault status using the specific processing unit 290 of the data processing device 12 and selects the optimal method of information transmission. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback unit, learning unit, haptic unit, visual unit, environment recognition unit, and personalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information through the sensors of the smart glasses 214 and user input, and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The feedback unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and provides notifications and alerts to the user. The learning unit learns the user's behavior using the specific processing unit 290 of the data processing unit 12 and provides optimal feedback. The haptic unit provides haptic feedback using the vibration motor of the smart glasses 214. The visual unit processes visual information using the camera of the smart glasses 214 or AR glasses and provides it to the user. The environmental recognition unit uses the sensors of the smart glasses 214 to recognize the surrounding environment, which is then analyzed by the identification processing unit 290 of the data processing device 12. The personalization unit uses the identification processing unit 290 of the data processing device 12 to analyze the user's condition and select the optimal method of information transmission. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback unit, learning unit, haptic unit, visual unit, environment recognition unit, and personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information through the sensors of the headset terminal 314 and user input, and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The feedback unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and provides notifications and alerts to the user. The learning unit learns the user's behavior using the specific processing unit 290 of the data processing unit 12 and provides optimal feedback. The haptic unit provides haptic feedback using the vibration motor of the headset terminal 314. The visual unit processes visual information using the camera of the headset terminal 314 or AR glasses and provides it to the user. The environmental recognition unit uses the sensors of the headset terminal 314 to recognize the surrounding environment, which is then analyzed by the identification processing unit 290 of the data processing device 12. The personalization unit uses the identification processing unit 290 of the data processing device 12 to analyze the user's condition and select the optimal method of information transmission. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, feedback unit, learning unit, tactile unit, visual unit, environment recognition unit, and personalization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information through the robot 414's sensors and user input, and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and provides notifications and alerts to the user. The learning unit learns the user's behavior using the specific processing unit 290 of the data processing unit 12 and provides optimal feedback. The tactile unit provides tactile feedback using the robot 414's vibration motor. The visual unit processes visual information using the robot 414's camera and display and provides it to the user. The environment recognition unit recognizes the surrounding situation using the robot 414's sensors and analyzes it using the specific processing unit 290 of the data processing unit 12. The personalization unit analyzes the user's fault status using the specific processing unit 290 of the data processing device 12 and selects the optimal method of information transmission. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A feedback unit provides feedback based on the analysis results obtained by the aforementioned analysis unit, It comprises a learning unit that learns user behavior, A system characterized by the following features. (Note 2) Equipped with a tactile section that provides haptic feedback functionality. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a visual unit that processes visual information. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an environment recognition unit that recognizes the environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a personalization unit that selects an information transmission method tailored to the individual's disability status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Gathering information using sensory organs other than hearing The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Provides customized feedback tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, adjust the order of the feedback based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tactile part is, It estimates the user's emotions and adjusts the intensity of haptic feedback based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned tactile part is, When providing haptic feedback, adjust the level of detail in the feedback based on the importance of the information. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned tactile part is, It estimates the user's emotions and adjusts the haptic feedback pattern based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned tactile part is, When providing haptic feedback, prioritize the feedback based on when the information was collected. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned visual unit is It estimates the user's emotions and adjusts how visual information is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned visual unit is When displaying visual information, adjust the level of detail of the visual information based on its importance. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned visual unit is It estimates the user's emotions and adjusts the display order of visual information based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned visual unit is When displaying visual information, the priority of visual information is determined based on when the information was collected. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned environment recognition unit, It estimates the user's emotions and adjusts the accuracy of environmental recognition based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned environment recognition unit, During environment recognition, the environment recognition algorithm is optimized by referring to past environment data. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned environment recognition unit, It estimates the user's emotions and adjusts the frequency of environmental awareness based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned environment recognition unit, During environmental recognition, the environmental recognition data is weighted based on the timing of information collection. The system described in Appendix 4, characterized by the features described herein. (Note 42) The personalization unit described above is It estimates the user's emotions and adjusts the personalization method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The personalization unit described above is During personalization, the system selects the optimal personalization method by referring to the user's past behavior history. The system described in Appendix 5, characterized by the features described herein. (Note 44) The personalization unit described above is It estimates the user's emotions and determines personalization priorities based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 45) The personalization unit described above is When personalizing, the system selects the optimal personalization method by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A feedback unit provides feedback based on the analysis results obtained by the aforementioned analysis unit, It comprises a learning unit that learns user behavior, A system characterized by the following features.

2. Equipped with a tactile section that provides haptic feedback functionality. The system according to feature 1.

3. It is equipped with a visual unit that processes visual information. The system according to feature 1.

4. It is equipped with an environment recognition unit that recognizes the environment. The system according to feature 1.

5. It features a personalization unit that selects an information transmission method tailored to the individual's disability status. The system according to feature 1.

6. The aforementioned collection unit is Gathering information using sensory organs other than hearing The system according to feature 1.

7. The aforementioned feedback unit is Provides customized feedback tailored to the user's preferences. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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