system

Abstract

The system according to this embodiment aims to analyze the user's voice, gaze, and surrounding environment in real time and provide necessary information. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit recognizes the user's voice. The analysis unit analyzes the user's gaze and surrounding environment based on the voice recognized by the reception unit. The provision unit provides necessary information in real time based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the user's voice, line of sight, and surrounding environment are not sufficiently analyzed in real time to provide necessary information.

[0005] The system according to the embodiment aims to analyze the user's voice, line of sight, and surrounding environment in real time and provide necessary information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit recognizes the user's voice. The analysis unit analyzes the line of sight and the surrounding environment based on the voice recognized by the reception unit. The provision unit provides necessary information in real time based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's voice, gaze, and surrounding environment in real time and provide necessary information. [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 vision assist agent according to an embodiment of the present invention is a wearable smart glasses that combines AI and AR technology. This vision assist agent is a system that recognizes the user's voice, analyzes their gaze and surrounding environment, and provides necessary information in real time. The vision assist agent features user behavior analysis using deep learning, real-time environmental recognition and information provision, and an interactive user interface based on voice and gaze tracking. As a result, information access speed is 50% faster than using a normal smartphone, and 95% of users report improved information access. In addition, the relevance of information provided based on user preferences has improved by 80%. The vision assist agent realizes rapid information access for busy urban dwellers, an interface that is easy to use for the visually impaired and the elderly, and customizable information provision tailored to individual users. Furthermore, as a new wearable device combining AI and AR, it aims to optimize information provision according to the user's lifestyle and integrate visual information with interactive engagement. For example, the vision assist agent has a reception unit that recognizes the user's voice. The reception unit recognizes the user's voice and transmits the information to an analysis unit that analyzes the gaze and surrounding environment based on the voice. The analysis unit analyzes the user's behavior using deep learning and recognizes the environment in real time. The analysis unit uses eye-tracking devices and sensors to analyze the user's gaze and surrounding environment in detail. Next, the analysis unit transmits the information to the information delivery unit, which provides necessary information in real time based on the analyzed data. The information delivery unit includes a relevance enhancement unit that improves the relevance of the information provided based on the user's preferences. The relevance enhancement unit improves the relevance of the information provided based on the user's past behavioral history and survey results. Furthermore, the information delivery unit includes an interface unit that is easy for visually impaired and elderly users to use, providing information with ease of operation and high visibility in mind. This allows the vision assist agent to recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.This allows the vision assist agent to recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.

[0029] The vision assist agent according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit recognizes the user's voice. The user's voice includes, but is not limited to, voice commands or natural language utterances. The reception unit recognizes the user's voice using, for example, speech recognition technology. The reception unit can also accurately recognize the user's voice by removing ambient noise using noise cancellation technology. For example, if the user is in a noisy environment, the reception unit enhances noise cancellation to recognize the user's voice. Furthermore, the reception unit can estimate the user's emotions and adjust the voice recognition accuracy based on the estimated emotions. For example, if the user is nervous, the reception unit analyzes the tone and speed of the voice to improve recognition accuracy. The analysis unit analyzes the gaze and surrounding environment based on the voice recognized by the reception unit. Gaze tracking includes, but is not limited to, an eye-tracking device or gaze location information. The analysis unit tracks the user's gaze using, for example, an eye-tracking device and obtains gaze location information. The analysis unit can also use temperature sensors, illuminance sensors, etc., to analyze the surrounding environment. For example, the analysis unit measures ambient temperature using a temperature sensor and ambient illuminance using an illuminance sensor. Furthermore, the analysis unit can also analyze user behavior using deep learning. For example, the analysis unit analyzes user behavior using a convolutional neural network (CNN). The provision unit provides necessary information in real time based on the information analyzed by the analysis unit. Real-time information provision includes, but is not limited to, updates in seconds or delays in milliseconds. The provision unit improves the relevance of the information provided based on user preferences, for example. The provision unit improves the relevance of the information provided based on the user's past behavior history and survey results. For example, the provision unit analyzes the user's past behavior history and provides highly relevant information. Furthermore, the provision unit provides an interface that is easy to use for visually impaired and elderly people. For example, the provision unit provides information considering ease of operation and high visibility.As a result, the vision assist agent according to the embodiment can recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.

[0030] The reception unit recognizes the user's voice. This includes, but is not limited to, voice commands or natural language utterances. The reception unit recognizes the user's voice using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This allows for high-precision conversion of the user's speech into text data. The reception unit can also accurately recognize the user's voice by removing ambient noise using noise cancellation technology. For example, if the user is in a noisy environment, the reception unit enhances noise cancellation to recognize the user's voice. Active noise cancellation (ANC) technology is used as the noise cancellation technique, analyzing ambient noise in real time and generating sound waves with the opposite phase to cancel out the noise. Furthermore, the reception unit can estimate the user's emotions and adjust the voice recognition accuracy based on the estimated emotions. For example, if the user is nervous, the reception unit analyzes the tone and speed of the voice to improve recognition accuracy. Emotion estimation involves analyzing features such as pitch, tone, speed, and volume of the voice, and classifying emotions using a machine learning model. This enables flexible speech recognition that adapts to the user's emotional state. Furthermore, the reception unit can learn the characteristics of the user's voice and build a speech recognition model optimized for each individual user. This results in highly accurate speech recognition that takes into account each user's speech habits and characteristics.

[0031] The analysis unit analyzes the user's gaze and surrounding environment based on the voice recognized by the reception unit. Gaze tracking includes, but is not limited to, eye-tracking devices or gaze location information. For example, the analysis unit tracks the user's gaze using an eye-tracking device and obtains gaze location information. Eye-tracking devices such as infrared cameras and eye movement sensors are used to detect the user's gaze movements with high accuracy. The analysis unit can also use temperature sensors and illuminance sensors to analyze the surrounding environment. For example, the analysis unit measures the ambient temperature using a temperature sensor and the ambient illuminance using an illuminance sensor. This allows for the acquisition of detailed information about the user's environment. Furthermore, the analysis unit can analyze user behavior using deep learning. For example, the analysis unit analyzes user behavior using a convolutional neural network (CNN). CNNs excel in image recognition and video analysis and can recognize user movements and gestures with high accuracy. This allows for a comprehensive analysis of the user's gaze movements and surrounding environment information, enabling an accurate understanding of the user's intentions and situation. Furthermore, the analytics department can process the acquired data in real time and respond immediately to changes in user behavior and the environment. This enables the rapid provision of information tailored to user needs.

[0032] The information delivery unit provides necessary information in real time based on the information analyzed by the analysis unit. Real-time information delivery includes, but is not limited to, updates in seconds or delays in milliseconds. The information delivery unit improves the relevance of the information provided based on user preferences. The information delivery unit improves the relevance of the information provided based on the user's past behavior history and survey results. For example, the information delivery unit analyzes the user's past behavior history and provides highly relevant information. Specifically, it analyzes information the user has searched for and browsing history in the past and prioritizes providing information based on the user's interests and concerns. The information delivery unit also provides an interface that is easy to use for visually impaired and elderly people. For example, the information delivery unit provides information considering ease of operation and high visibility. For visually impaired people, information is provided using audio guides and braille displays, and for the elderly, large fonts and simple operation screens are provided. Furthermore, the information delivery unit can collect user feedback and continuously improve the accuracy and relevance of the information provided. For example, when users rate and comment on the information provided, the information delivery unit analyzes that feedback and reflects it in future information delivery. This enables the information provider to deliver highly accurate information tailored to user needs, thereby improving user satisfaction. Furthermore, the information provider can integrate data from multiple sources to provide comprehensive information. For example, it can collect information from news sites, social media, and specialized websites, and select and provide the most relevant information to the user. This allows the information provider to deliver comprehensive and reliable information to users.

[0033] The vision assist agent includes a user behavior analysis unit that uses deep learning. Deep learning algorithms include, but are not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The user behavior analysis unit can, for example, analyze user behavior using a CNN. For instance, it can learn user behavior patterns and predict specific actions. Furthermore, the user behavior analysis unit can analyze user behavior as time-series data using an RNN. For example, it can predict future actions based on the user's past behavior history. In addition, the user behavior analysis unit can analyze user behavior using generative AI. For example, the user behavior analysis unit inputs user behavior data into the generative AI, which then extracts and analyzes behavior patterns. This improves the accuracy of user behavior analysis through the use of deep learning.

[0034] The vision assist agent includes a real-time environmental recognition unit. The real-time environmental recognition unit can recognize the surrounding environment in real time. Real-time environmental recognition includes, but is not limited to, the type of sensor and the frequency of data updates. The real-time environmental recognition unit recognizes the surrounding environment using, for example, a temperature sensor and an illuminance sensor. For example, the real-time environmental recognition unit measures the ambient temperature using a temperature sensor and the ambient illuminance using an illuminance sensor. The real-time environmental recognition unit can also measure the ambient sound level using a sound sensor. For example, the real-time environmental recognition unit measures the ambient noise level using a sound sensor. Furthermore, the real-time environmental recognition unit can also recognize the surrounding environment using a generative AI. For example, the real-time environmental recognition unit inputs sensor data into the generative AI, and the generative AI recognizes changes in the environment in real time. This enables real-time environmental recognition.

[0035] The vision assist agent features an interactive user interface unit that utilizes voice and eye-tracking. This interactive user interface unit tracks the user's voice and gaze to provide interactive operation. The interactive user interface includes, but is not limited to, voice commands and eye-tracking operations. For example, the interactive user interface unit can recognize the user's voice using speech recognition technology and perform operations based on voice commands. For instance, if the user says "proceed to the next page," the interactive user interface unit will perform the operation to proceed to the next page. Furthermore, the interactive user interface unit can track the user's gaze using an eye-tracking device and perform operations based on the location information of the gaze. For example, if the user looks at a specific icon, the interactive user interface unit will perform the operation to select that icon. Additionally, the interactive user interface unit can track the user's voice and gaze using generative AI. For example, the interactive user interface unit inputs the user's voice and gaze data into the generative AI, which then provides interactive operation. This enables the provision of an interactive user interface utilizing voice and eye-tracking.

[0036] The vision assist agent includes a relevance enhancement unit that improves the relevance of information provided based on user preferences. The relevance enhancement unit improves the relevance of information provided based on user preferences. Identifying user preferences includes, but is not limited to, past behavioral history and survey results. For example, the relevance enhancement unit analyzes the user's past behavioral history and provides highly relevant information. For example, the relevance enhancement unit provides relevant information based on keywords the user has searched for and pages they have viewed in the past. The relevance enhancement unit can also identify user preferences based on the user's survey results. For example, the relevance enhancement unit provides relevant information based on the interests and concerns the user has indicated in a survey. Furthermore, the relevance enhancement unit can also identify user preferences using generative AI. For example, the relevance enhancement unit inputs user behavioral data into the generative AI, which identifies user preferences and provides highly relevant information. This improves the relevance of information provided based on user preferences.

[0037] The vision assist agent features an interface that is easy for visually impaired and elderly users to use. The interface provides a user-friendly interface for visually impaired and elderly users. Criteria for ease of use include, but are not limited to, ease of operation and high visibility. For example, the interface provides simple and intuitive operating procedures, taking ease of operation into consideration. For instance, the interface provides voice guidance and haptic feedback to enable visually impaired users to operate it easily. The interface can also adjust font size and contrast to ensure high visibility. For example, the interface displays larger text and emphasizes contrast to make it easier for elderly users to read. Furthermore, the interface can use generative AI to assist user operations. For example, the interface inputs user operation data into the generative AI, which then assists with the operation. This allows for the provision of an interface that is easy for visually impaired and elderly users to use.

[0038] The reception unit can analyze the user's past speech history and select the optimal speech recognition model. For example, the reception unit can learn specific phrases and expressions the user has used in the past and customize the speech recognition model. For example, the reception unit can also analyze the user's speech patterns and select the most suitable speech recognition model. Furthermore, the reception unit can select a speech recognition model that corresponds to a specific accent or dialect from the user's speech history. In this way, the optimal speech recognition model can be selected by analyzing the user's past speech history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's speech history data into a generating AI and have the generating AI perform the selection of the optimal speech recognition model.

[0039] The reception unit can perform noise cancellation according to the ambient noise level when recognizing voices. For example, if the user is in a noisy environment, the AI ​​in the reception unit can detect the ambient noise and enhance noise cancellation. For example, if the user is in a quiet environment, the AI ​​in the reception unit can minimize noise cancellation and perform natural speech recognition. Furthermore, if the user is moving, the AI ​​in the reception unit can monitor the noise level in real time and dynamically adjust the noise cancellation. This improves recognition accuracy by performing noise cancellation according to the ambient noise level. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI perform the noise cancellation adjustments.

[0040] The reception unit can prioritize the recognition of highly relevant information by considering the user's geographical location when recognizing voice. For example, if the user is in a specific location, the reception unit can prioritize the recognition of information related to that location. For example, if the user is on the move, the reception unit can also recognize relevant information based on the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize the recognition of information related to that area. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the recognition of highly relevant information.

[0041] The reception unit can analyze the user's social media activity and recognize relevant information when recognizing voice. For example, the reception unit can prioritize recognizing topics that the user frequently mentions on social media. For example, the reception unit can also analyze the content of the user's social media posts and recognize relevant information. Furthermore, the reception unit can consider the activities of the user's social media followers and friends and recognize relevant information. In this way, relevant information can be recognized by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the recognition of relevant information.

[0042] The analysis unit can select the optimal analysis method by referring to the user's past behavioral history during analysis. For example, the analysis unit can select the optimal analysis method based on the user's past behavioral patterns. For example, the analysis unit can also select an analysis method appropriate to a specific situation from the user's past behavioral history. Furthermore, the analysis unit can analyze the user's behavioral history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0043] The analysis unit can switch analysis methods in real time in response to changes in the surrounding environment during analysis. For example, if the ambient brightness changes, the analysis unit can adjust the eye-tracking analysis method in real time. For example, if the ambient noise level changes, the analysis unit can also adjust the voice analysis method in real time. Furthermore, if the ambient temperature or humidity changes, the analysis unit can also adjust the environmental analysis method in real time. This improves analysis accuracy by switching analysis methods in real time in response to changes in the surrounding environment. 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 environmental data into a generating AI and have the generating AI execute the switching of analysis methods.

[0044] The analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location during the analysis process. For example, if the user is in a specific location, the analysis unit will prioritize the analysis of information related to that location. For example, if the user is on the move, the analysis unit can also analyze relevant information based on the user's current location. Furthermore, if the user is in a specific area, the analysis unit can also prioritize the analysis of information related to that area. This allows for the prioritization of highly relevant information by considering the user's geographical location. 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 the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0045] The analysis unit can analyze a user's social media activity and analyze relevant information during the analysis process. For example, the analysis unit can prioritize analyzing topics that the user frequently mentions on social media. For example, the analysis unit can also analyze the content of a user's social media posts and analyze relevant information. Furthermore, the analysis unit can also analyze relevant information by considering the activities of the user's social media followers and friends. In this way, relevant information can be analyzed by analyzing the user's social media activity. 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 the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0046] The information delivery unit can select the optimal information delivery method by referring to the user's past information usage history at the time of delivery. For example, the information delivery unit can select the optimal method based on the information delivery methods the user has used in the past. For example, the information delivery unit can also select an information delivery method appropriate to a specific situation from the user's past information usage history. Furthermore, the information delivery unit can analyze the user's information usage history and select the most effective information delivery method. In this way, the optimal information delivery method can be selected by referring to the user's past information usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's information usage history data into a generating AI and have the generating AI perform the selection of the optimal information delivery method.

[0047] The information provider can customize the content of the information provided at the time of delivery according to the user's current situation. For example, if the user is in a specific location, the provider can provide information related to that location. For example, if the user is on the move, the provider can also customize the information based on the user's current situation. Furthermore, if the user is in a specific area, the provider can also provide information related to that area. This allows for the provision of more appropriate information by customizing the content of the information provided according to the user's current situation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input user situation data into a generating AI and have the generating AI perform the customization of the information provided.

[0048] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific location, the information provider can prioritize providing information related to that location. For example, if the user is on the move, the information provider can also provide relevant information based on the user's current location. Furthermore, if the user is in a specific area, the information provider can also prioritize providing information related to that area. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0049] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider may prioritize providing topics that the user frequently mentions on social media. For example, the service provider may also analyze the content of the user's social media posts and provide relevant information. Furthermore, the service provider may consider the activities of the user's social media followers and friends and provide relevant information. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0050] The user behavior analysis unit can select the optimal analysis method by referring to the user's past behavioral history during behavioral analysis. For example, the user behavior analysis unit can select the optimal analysis method based on the user's past behavioral patterns. For example, the user behavior analysis unit can also select an analysis method appropriate to a specific situation from the user's past behavioral history. Furthermore, the user behavior analysis unit can analyze the user's behavioral history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past behavioral history. Some or all of the above processing in the user behavior analysis unit may be performed using AI, for example, or without using AI. For example, the user behavior analysis unit can input user behavioral history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0051] The user behavior analysis unit can prioritize the analysis of highly relevant behaviors by considering the user's geographical location information during behavioral analysis. For example, if the user is in a specific location, the user behavior analysis unit can prioritize the analysis of behaviors related to that location. For example, if the user is on the move, the user behavior analysis unit can also analyze relevant behaviors based on the user's current location. Furthermore, if the user is in a specific area, the user behavior analysis unit can also prioritize the analysis of behaviors related to that area. In this way, by considering the user's geographical location information, the analysis of highly relevant behaviors can be prioritized. Some or all of the above processing in the user behavior analysis unit may be performed using AI, for example, or without AI. For example, the user behavior analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant behaviors.

[0052] The real-time environment recognition unit can select the optimal recognition method by referring to the user's past environment history during environment recognition. For example, the real-time environment recognition unit can select the optimal recognition method based on environment patterns the user has experienced in the past. For example, the real-time environment recognition unit can also select a recognition method appropriate to a specific situation from the user's past environment history. Furthermore, the real-time environment recognition unit can analyze the user's environment history and select the most effective recognition method. In this way, the optimal recognition method can be selected by referring to the user's past environment history. Some or all of the above processing in the real-time environment recognition unit may be performed using AI, for example, or without using AI. For example, the real-time environment recognition unit can input the user's environment history data into a generating AI and have the generating AI perform the selection of the optimal recognition method.

[0053] The real-time environment recognition unit can prioritize the recognition of highly relevant environments by considering the user's geographical location information during environment recognition. For example, if the user is in a specific location, the real-time environment recognition unit will prioritize the recognition of environments related to that location. For example, if the user is moving, the real-time environment recognition unit can also recognize relevant environments based on the user's current location. Furthermore, if the user is in a specific area, the real-time environment recognition unit can also prioritize the recognition of environments related to that area. In this way, by considering the user's geographical location information, highly relevant environments can be prioritized. Some or all of the above processing in the real-time environment recognition unit may be performed using AI, for example, or without AI. For example, the real-time environment recognition unit can input the user's geographical location data into a generating AI and have the generating AI perform the recognition of highly relevant environments.

[0054] The interactive user interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interactive user interface unit can select the optimal display method based on the interface design the user has used in the past. For example, the interactive user interface unit can also select a display method appropriate to a specific situation from the user's past operation history. Furthermore, the interactive user interface unit can analyze the user's operation history and select the most effective display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the interactive user interface unit may be performed using AI, for example, or without AI. For example, the interactive user interface unit can input user operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0055] The interactive user interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interactive user interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interactive user interface unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the interactive user interface unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the interactive user interface unit may be performed using AI, for example, or without AI. For example, the interactive user interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0056] The relevance improvement unit can select the optimal relevance improvement method by referring to the user's past information usage history when improving relevance. For example, the relevance improvement unit can select the optimal method based on the information provision methods the user has used in the past. For example, the relevance improvement unit can also select an information provision method appropriate to a specific situation from the user's past information usage history. Furthermore, the relevance improvement unit can analyze the user's information usage history and select the most effective information provision method. In this way, the optimal relevance improvement method can be selected by referring to the user's past information usage history. Some or all of the above processing in the relevance improvement unit may be performed using AI, for example, or without using AI. For example, the relevance improvement unit can input the user's information usage history data into a generating AI and have the generating AI perform the selection of the optimal relevance improvement method.

[0057] The relevance enhancement unit can prioritize enhancing highly relevant information by considering the user's geographical location information during the relevance enhancement process. For example, if the user is in a specific location, the relevance enhancement unit can prioritize enhancing information related to that location. For example, if the user is on the move, the relevance enhancement unit can also enhance relevant information based on the user's current location. Furthermore, if the user is in a specific area, the relevance enhancement unit can also prioritize enhancing information related to that area. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the relevance enhancement unit may be performed using AI, for example, or without AI. For example, the relevance enhancement unit can input the user's geographical location data into a generating AI and have the generating AI perform the enhancement of highly relevant information.

[0058] An interface designed for the visually impaired and elderly can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface can select the optimal display method based on the user's past interface designs. It can also select a display method appropriate to a specific situation based on the user's past operation history. Furthermore, the interface can analyze the user's operation history to select the most effective display method. This allows for the selection of the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface designed for the visually impaired and elderly may be performed using AI, or without AI. For example, the interface can input user operation history data into a generating AI and have the generating AI select the optimal display method.

[0059] The user-friendly interface for the visually impaired and the elderly can select the optimal display method by considering the user's device information when displaying the interface. For example, if the user is using a smartphone, the user-friendly interface for the visually impaired and the elderly can provide a display method that is adapted to the screen size. For example, if the user is using a tablet, the user-friendly interface for the visually impaired and the elderly can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the user-friendly interface for the visually impaired and the elderly can provide a simple and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the user-friendly interface for the visually impaired and the elderly may be performed using AI, for example, or without AI. For example, the user-friendly interface for the visually impaired and the elderly can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The vision assist agent can also be equipped with a health management unit that monitors the user's health status. This unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature in real time. For instance, it can measure the user's heart rate using a heart rate sensor and issue an alert if an abnormality is detected. It can also measure the user's blood pressure using a blood pressure sensor and notify a medical institution if an abnormality is detected. Furthermore, it can measure the user's body temperature using a body temperature sensor and prompt the user to rest if a fever is detected. This allows the vision assist agent to monitor the user's health status in real time and take appropriate action if an abnormality is detected.

[0062] Vision assist agents can also be equipped with a schedule management unit to manage the user's schedule. This unit can, for example, manage appointments in conjunction with the user's calendar app. It can, for instance, check the user's schedule and send notifications when upcoming appointments are approaching. It can also suggest the optimal route based on the user's schedule. Furthermore, it can set reminders to ensure important appointments are not forgotten. In this way, vision assist agents can support the user's schedule management and enable efficient time management.

[0063] The vision assist agent can also be equipped with a meal management unit to manage the user's diet. This unit can, for example, record the user's meal history and analyze nutritional balance. It can record the calories and nutrients consumed by the user and suggest healthy meals. Furthermore, based on the user's meal history, the unit can suggest menus for the next meal. In addition, the unit can provide customized meal plans according to the user's health condition and goals. This allows the vision assist agent to support the user's meal management and help them achieve a healthy lifestyle.

[0064] Vision assist agents can also be equipped with a sleep management unit to manage the user's sleep. This unit can, for example, monitor the user's sleep patterns and analyze sleep quality. It can record the user's sleep duration and depth, and suggest areas for improvement. Furthermore, it can analyze the user's sleep environment and suggest an optimal environment. In addition, it can provide a customized sleep plan based on the user's sleep history. This allows vision assist agents to support the user's sleep management and help them achieve high-quality sleep.

[0065] The vision assist agent can also be equipped with a learning support unit to assist the user's learning. This unit can, for example, record the user's learning history and manage their learning progress. It can also suggest the next learning topic based on the user's learning history. Furthermore, it can suggest the optimal learning method tailored to the user's learning style. In addition, it can provide a customized learning plan according to the user's learning goals. This allows the vision assist agent to support the user's learning and achieve efficient learning.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception unit recognizes the user's voice. The user's voice includes voice commands and natural language utterances. The reception unit can recognize the user's voice using speech recognition technology and can also remove ambient noise using noise cancellation technology. In addition, the reception unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated emotions. Step 2: The analysis unit analyzes the user's gaze and surrounding environment based on the voice recognized by the reception unit. Eye-tracking devices and gaze location information can be used for eye tracking, and temperature sensors and illuminance sensors can be used for analyzing the surrounding environment. Furthermore, the analysis unit can also analyze user behavior using deep learning. Step 3: The service provider provides necessary information in real time based on the data analyzed by the analysis unit. The service provider improves the relevance of the information provided based on user preferences and past behavioral history. It also provides an interface that is easy to use for visually impaired and elderly users.

[0068] (Example of form 2) The vision assist agent according to an embodiment of the present invention is a wearable smart glasses that combines AI and AR technology. This vision assist agent is a system that recognizes the user's voice, analyzes their gaze and surrounding environment, and provides necessary information in real time. The vision assist agent features user behavior analysis using deep learning, real-time environmental recognition and information provision, and an interactive user interface based on voice and gaze tracking. As a result, information access speed is 50% faster than using a normal smartphone, and 95% of users report improved information access. In addition, the relevance of information provided based on user preferences has improved by 80%. The vision assist agent realizes rapid information access for busy urban dwellers, an interface that is easy to use for the visually impaired and the elderly, and customizable information provision tailored to individual users. Furthermore, as a new wearable device combining AI and AR, it aims to optimize information provision according to the user's lifestyle and integrate visual information with interactive engagement. For example, the vision assist agent has a reception unit that recognizes the user's voice. The reception unit recognizes the user's voice and transmits the information to an analysis unit that analyzes the gaze and surrounding environment based on the voice. The analysis unit analyzes the user's behavior using deep learning and recognizes the environment in real time. The analysis unit uses eye-tracking devices and sensors to analyze the user's gaze and surrounding environment in detail. Next, the analysis unit transmits the information to the information delivery unit, which provides necessary information in real time based on the analyzed data. The information delivery unit includes a relevance enhancement unit that improves the relevance of the information provided based on the user's preferences. The relevance enhancement unit improves the relevance of the information provided based on the user's past behavioral history and survey results. Furthermore, the information delivery unit includes an interface unit that is easy for visually impaired and elderly users to use, providing information with ease of operation and high visibility in mind. This allows the vision assist agent to recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.This allows the vision assist agent to recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.

[0069] The vision assist agent according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit recognizes the user's voice. The user's voice includes, but is not limited to, voice commands or natural language utterances. The reception unit recognizes the user's voice using, for example, speech recognition technology. The reception unit can also accurately recognize the user's voice by removing ambient noise using noise cancellation technology. For example, if the user is in a noisy environment, the reception unit enhances noise cancellation to recognize the user's voice. Furthermore, the reception unit can estimate the user's emotions and adjust the voice recognition accuracy based on the estimated emotions. For example, if the user is nervous, the reception unit analyzes the tone and speed of the voice to improve recognition accuracy. The analysis unit analyzes the gaze and surrounding environment based on the voice recognized by the reception unit. Gaze tracking includes, but is not limited to, an eye-tracking device or gaze location information. The analysis unit tracks the user's gaze using, for example, an eye-tracking device and obtains gaze location information. The analysis unit can also use temperature sensors, illuminance sensors, etc., to analyze the surrounding environment. For example, the analysis unit measures ambient temperature using a temperature sensor and ambient illuminance using an illuminance sensor. Furthermore, the analysis unit can also analyze user behavior using deep learning. For example, the analysis unit analyzes user behavior using a convolutional neural network (CNN). The provision unit provides necessary information in real time based on the information analyzed by the analysis unit. Real-time information provision includes, but is not limited to, updates in seconds or delays in milliseconds. The provision unit improves the relevance of the information provided based on user preferences, for example. The provision unit improves the relevance of the information provided based on the user's past behavior history and survey results. For example, the provision unit analyzes the user's past behavior history and provides highly relevant information. Furthermore, the provision unit provides an interface that is easy to use for visually impaired and elderly people. For example, the provision unit provides information considering ease of operation and high visibility.As a result, the vision assist agent according to the embodiment can recognize the user's voice, analyze their gaze and surrounding environment, and provide necessary information in real time.

[0070] The reception unit recognizes the user's voice. This includes, but is not limited to, voice commands or natural language utterances. The reception unit recognizes the user's voice using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This allows for high-precision conversion of the user's speech into text data. The reception unit can also accurately recognize the user's voice by removing ambient noise using noise cancellation technology. For example, if the user is in a noisy environment, the reception unit enhances noise cancellation to recognize the user's voice. Active noise cancellation (ANC) technology is used as the noise cancellation technique, analyzing ambient noise in real time and generating sound waves with the opposite phase to cancel out the noise. Furthermore, the reception unit can estimate the user's emotions and adjust the voice recognition accuracy based on the estimated emotions. For example, if the user is nervous, the reception unit analyzes the tone and speed of the voice to improve recognition accuracy. Emotion estimation involves analyzing features such as pitch, tone, speed, and volume of the voice, and classifying emotions using a machine learning model. This enables flexible speech recognition that adapts to the user's emotional state. Furthermore, the reception unit can learn the characteristics of the user's voice and build a speech recognition model optimized for each individual user. This results in highly accurate speech recognition that takes into account each user's speech habits and characteristics.

[0071] The analysis unit analyzes the user's gaze and surrounding environment based on the voice recognized by the reception unit. Gaze tracking includes, but is not limited to, eye-tracking devices or gaze location information. For example, the analysis unit tracks the user's gaze using an eye-tracking device and obtains gaze location information. Eye-tracking devices such as infrared cameras and eye movement sensors are used to detect the user's gaze movements with high accuracy. The analysis unit can also use temperature sensors and illuminance sensors to analyze the surrounding environment. For example, the analysis unit measures the ambient temperature using a temperature sensor and the ambient illuminance using an illuminance sensor. This allows for the acquisition of detailed information about the user's environment. Furthermore, the analysis unit can analyze user behavior using deep learning. For example, the analysis unit analyzes user behavior using a convolutional neural network (CNN). CNNs excel in image recognition and video analysis and can recognize user movements and gestures with high accuracy. This allows for a comprehensive analysis of the user's gaze movements and surrounding environment information, enabling an accurate understanding of the user's intentions and situation. Furthermore, the analytics department can process the acquired data in real time and respond immediately to changes in user behavior and the environment. This enables the rapid provision of information tailored to user needs.

[0072] The information delivery unit provides necessary information in real time based on the information analyzed by the analysis unit. Real-time information delivery includes, but is not limited to, updates in seconds or delays in milliseconds. The information delivery unit improves the relevance of the information provided based on user preferences. The information delivery unit improves the relevance of the information provided based on the user's past behavior history and survey results. For example, the information delivery unit analyzes the user's past behavior history and provides highly relevant information. Specifically, it analyzes information the user has searched for and browsing history in the past and prioritizes providing information based on the user's interests and concerns. The information delivery unit also provides an interface that is easy to use for visually impaired and elderly people. For example, the information delivery unit provides information considering ease of operation and high visibility. For visually impaired people, information is provided using audio guides and braille displays, and for the elderly, large fonts and simple operation screens are provided. Furthermore, the information delivery unit can collect user feedback and continuously improve the accuracy and relevance of the information provided. For example, when users rate and comment on the information provided, the information delivery unit analyzes that feedback and reflects it in future information delivery. This enables the information provider to deliver highly accurate information tailored to user needs, thereby improving user satisfaction. Furthermore, the information provider can integrate data from multiple sources to provide comprehensive information. For example, it can collect information from news sites, social media, and specialized websites, and select and provide the most relevant information to the user. This allows the information provider to deliver comprehensive and reliable information to users.

[0073] The vision assist agent includes a user behavior analysis unit that uses deep learning. Deep learning algorithms include, but are not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The user behavior analysis unit can, for example, analyze user behavior using a CNN. For instance, it can learn user behavior patterns and predict specific actions. Furthermore, the user behavior analysis unit can analyze user behavior as time-series data using an RNN. For example, it can predict future actions based on the user's past behavior history. In addition, the user behavior analysis unit can analyze user behavior using generative AI. For example, the user behavior analysis unit inputs user behavior data into the generative AI, which then extracts and analyzes behavior patterns. This improves the accuracy of user behavior analysis through the use of deep learning.

[0074] The vision assist agent includes a real-time environmental recognition unit. The real-time environmental recognition unit can recognize the surrounding environment in real time. Real-time environmental recognition includes, but is not limited to, the type of sensor and the frequency of data updates. The real-time environmental recognition unit recognizes the surrounding environment using, for example, a temperature sensor and an illuminance sensor. For example, the real-time environmental recognition unit measures the ambient temperature using a temperature sensor and the ambient illuminance using an illuminance sensor. The real-time environmental recognition unit can also measure the ambient sound level using a sound sensor. For example, the real-time environmental recognition unit measures the ambient noise level using a sound sensor. Furthermore, the real-time environmental recognition unit can also recognize the surrounding environment using a generative AI. For example, the real-time environmental recognition unit inputs sensor data into the generative AI, and the generative AI recognizes changes in the environment in real time. This enables real-time environmental recognition.

[0075] The vision assist agent features an interactive user interface unit that utilizes voice and eye-tracking. This interactive user interface unit tracks the user's voice and gaze to provide interactive operation. The interactive user interface includes, but is not limited to, voice commands and eye-tracking operations. For example, the interactive user interface unit can recognize the user's voice using speech recognition technology and perform operations based on voice commands. For instance, if the user says "proceed to the next page," the interactive user interface unit will perform the operation to proceed to the next page. Furthermore, the interactive user interface unit can track the user's gaze using an eye-tracking device and perform operations based on the location information of the gaze. For example, if the user looks at a specific icon, the interactive user interface unit will perform the operation to select that icon. Additionally, the interactive user interface unit can track the user's voice and gaze using generative AI. For example, the interactive user interface unit inputs the user's voice and gaze data into the generative AI, which then provides interactive operation. This enables the provision of an interactive user interface utilizing voice and eye-tracking.

[0076] The vision assist agent includes a relevance enhancement unit that improves the relevance of information provided based on user preferences. The relevance enhancement unit improves the relevance of information provided based on user preferences. Identifying user preferences includes, but is not limited to, past behavioral history and survey results. For example, the relevance enhancement unit analyzes the user's past behavioral history and provides highly relevant information. For example, the relevance enhancement unit provides relevant information based on keywords the user has searched for and pages they have viewed in the past. The relevance enhancement unit can also identify user preferences based on the user's survey results. For example, the relevance enhancement unit provides relevant information based on the interests and concerns the user has indicated in a survey. Furthermore, the relevance enhancement unit can also identify user preferences using generative AI. For example, the relevance enhancement unit inputs user behavioral data into the generative AI, which identifies user preferences and provides highly relevant information. This improves the relevance of information provided based on user preferences.

[0077] The vision assist agent features an interface that is easy for visually impaired and elderly users to use. The interface provides a user-friendly interface for visually impaired and elderly users. Criteria for ease of use include, but are not limited to, ease of operation and high visibility. For example, the interface provides simple and intuitive operating procedures, taking ease of operation into consideration. For instance, the interface provides voice guidance and haptic feedback to enable visually impaired users to operate it easily. The interface can also adjust font size and contrast to ensure high visibility. For example, the interface displays larger text and emphasizes contrast to make it easier for elderly users to read. Furthermore, the interface can use generative AI to assist user operations. For example, the interface inputs user operation data into the generative AI, which then assists with the operation. This allows for the provision of an interface that is easy for visually impaired and elderly users to use.

[0078] The reception system can estimate the user's emotions and adjust the voice recognition accuracy based on the estimated emotions. For example, if the user is nervous, the AI ​​can analyze the tone and speed of the voice to improve recognition accuracy. For example, if the user is relaxed, the AI ​​can adjust the recognition accuracy by taking into account the natural fluctuations in the voice. Furthermore, if the user is in a hurry, the AI ​​can optimize the recognition accuracy in response to the speed of the voice. This improves recognition accuracy by adjusting the voice recognition accuracy according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception unit can analyze the user's past speech history and select the optimal speech recognition model. For example, the reception unit can learn specific phrases and expressions the user has used in the past and customize the speech recognition model. For example, the reception unit can also analyze the user's speech patterns and select the most suitable speech recognition model. Furthermore, the reception unit can select a speech recognition model that corresponds to a specific accent or dialect from the user's speech history. In this way, the optimal speech recognition model can be selected by analyzing the user's past speech history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's speech history data into a generating AI and have the generating AI perform the selection of the optimal speech recognition model.

[0080] The reception unit can perform noise cancellation according to the ambient noise level when recognizing voices. For example, if the user is in a noisy environment, the AI ​​in the reception unit can detect the ambient noise and enhance noise cancellation. For example, if the user is in a quiet environment, the AI ​​in the reception unit can minimize noise cancellation and perform natural speech recognition. Furthermore, if the user is moving, the AI ​​in the reception unit can monitor the noise level in real time and dynamically adjust the noise cancellation. This improves recognition accuracy by performing noise cancellation according to the ambient noise level. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI perform the noise cancellation adjustments.

[0081] The reception system can estimate the user's emotions and determine the priority of voices to recognize based on the estimated emotions. For example, if the user is stressed, the reception system will prioritize recognizing important instructions or questions. For example, if the user is relaxed, the reception system may recognize all utterances equally. Furthermore, if the user is in a hurry, the reception system may prioritize recognizing short, important utterances. This allows for the priority of recognizing important voices by determining the priority of voices to recognize according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception unit can prioritize the recognition of highly relevant information by considering the user's geographical location when recognizing voice. For example, if the user is in a specific location, the reception unit can prioritize the recognition of information related to that location. For example, if the user is on the move, the reception unit can also recognize relevant information based on the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize the recognition of information related to that area. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the recognition of highly relevant information.

[0083] The reception unit can analyze the user's social media activity and recognize relevant information when recognizing voice. For example, the reception unit can prioritize recognizing topics that the user frequently mentions on social media. For example, the reception unit can also analyze the content of the user's social media posts and recognize relevant information. Furthermore, the reception unit can consider the activities of the user's social media followers and friends and recognize relevant information. In this way, relevant information can be recognized by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the recognition of relevant information.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the gaze and environment analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can accurately analyze even subtle eye movements. For example, if the user is relaxed, the analysis unit can also analyze broad eye movements. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to accommodate rapid eye movements. In this way, the analysis accuracy is improved by adjusting the accuracy of the gaze and environment analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit can select the optimal analysis method by referring to the user's past behavioral history during analysis. For example, the analysis unit can select the optimal analysis method based on the user's past behavioral patterns. For example, the analysis unit can also select an analysis method appropriate to a specific situation from the user's past behavioral history. Furthermore, the analysis unit can analyze the user's behavioral history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0086] The analysis unit can switch analysis methods in real time in response to changes in the surrounding environment during analysis. For example, if the ambient brightness changes, the analysis unit can adjust the eye-tracking analysis method in real time. For example, if the ambient noise level changes, the analysis unit can also adjust the voice analysis method in real time. Furthermore, if the ambient temperature or humidity changes, the analysis unit can also adjust the environmental analysis method in real time. This improves analysis accuracy by switching analysis methods in real time in response to changes in the surrounding environment. 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 environmental data into a generating AI and have the generating AI execute the switching of analysis methods.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This improves readability by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location during the analysis process. For example, if the user is in a specific location, the analysis unit will prioritize the analysis of information related to that location. For example, if the user is on the move, the analysis unit can also analyze relevant information based on the user's current location. Furthermore, if the user is in a specific area, the analysis unit can also prioritize the analysis of information related to that area. This allows for the prioritization of highly relevant information by considering the user's geographical location. 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 the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0089] The analysis unit can analyze a user's social media activity and analyze relevant information during the analysis process. For example, the analysis unit can prioritize analyzing topics that the user frequently mentions on social media. For example, the analysis unit can also analyze the content of a user's social media posts and analyze relevant information. Furthermore, the analysis unit can also analyze relevant information by considering the activities of the user's social media followers and friends. In this way, relevant information can be analyzed by analyzing the user's social media activity. 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 the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0090] The information provider can estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is stressed, the provider can deliver important information at the right time. For example, if the user is relaxed, the provider can flexibly adjust the timing of information delivery. Furthermore, if the user is in a hurry, the provider can deliver information quickly. In this way, by adjusting the timing of information delivery according to the user's emotions, information can be delivered at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The information delivery unit can select the optimal information delivery method by referring to the user's past information usage history at the time of delivery. For example, the information delivery unit can select the optimal method based on the information delivery methods the user has used in the past. For example, the information delivery unit can also select an information delivery method appropriate to a specific situation from the user's past information usage history. Furthermore, the information delivery unit can analyze the user's information usage history and select the most effective information delivery method. In this way, the optimal information delivery method can be selected by referring to the user's past information usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's information usage history data into a generating AI and have the generating AI perform the selection of the optimal information delivery method.

[0092] The information provider can customize the content of the information provided at the time of delivery according to the user's current situation. For example, if the user is in a specific location, the provider can provide information related to that location. For example, if the user is on the move, the provider can also customize the information based on the user's current situation. Furthermore, if the user is in a specific area, the provider can also provide information related to that area. This allows for the provision of more appropriate information by customizing the content of the information provided according to the user's current situation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input user situation data into a generating AI and have the generating AI perform the customization of the information provided.

[0093] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, the provider will prioritize providing important information. For example, if the user is relaxed, the provider may provide all information equally. Furthermore, if the user is in a hurry, the provider may prioritize providing short, important information. This ensures that important information is provided preferentially by prioritizing the information provided 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.

[0094] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific location, the information provider can prioritize providing information related to that location. For example, if the user is on the move, the information provider can also provide relevant information based on the user's current location. Furthermore, if the user is in a specific area, the information provider can also prioritize providing information related to that area. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0095] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider may prioritize providing topics that the user frequently mentions on social media. For example, the service provider may also analyze the content of the user's social media posts and provide relevant information. Furthermore, the service provider may consider the activities of the user's social media followers and friends and provide relevant information. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0096] The user behavior analysis unit can estimate the user's emotions and adjust the accuracy of the behavioral analysis based on the estimated emotions. For example, if the user is nervous, the user behavior analysis unit can accurately analyze even subtle changes in behavior. For example, if the user is relaxed, the user behavior analysis unit can also analyze broad changes in behavior. Furthermore, if the user is in a hurry, the user behavior analysis unit can adjust the accuracy of the analysis to respond to rapid changes in behavior. In this way, the accuracy of the analysis is improved by adjusting the accuracy of the behavioral analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The user behavior analysis unit can select the optimal analysis method by referring to the user's past behavioral history during behavioral analysis. For example, the user behavior analysis unit can select the optimal analysis method based on the user's past behavioral patterns. For example, the user behavior analysis unit can also select an analysis method appropriate to a specific situation from the user's past behavioral history. Furthermore, the user behavior analysis unit can analyze the user's behavioral history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past behavioral history. Some or all of the above processing in the user behavior analysis unit may be performed using AI, for example, or without using AI. For example, the user behavior analysis unit can input user behavioral history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0098] The user behavior analysis unit can estimate the user's emotions and adjust how the results of the behavior analysis are displayed based on the estimated emotions. For example, if the user is stressed, the user behavior analysis unit can provide a simple and easy-to-read display. For example, if the user is relaxed, the user behavior analysis unit can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the user behavior analysis unit can provide a concise display. This improves readability by adjusting how the results of the behavior analysis are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The user behavior analysis unit can prioritize the analysis of highly relevant behaviors by considering the user's geographical location information during behavioral analysis. For example, if the user is in a specific location, the user behavior analysis unit can prioritize the analysis of behaviors related to that location. For example, if the user is on the move, the user behavior analysis unit can also analyze relevant behaviors based on the user's current location. Furthermore, if the user is in a specific area, the user behavior analysis unit can also prioritize the analysis of behaviors related to that area. In this way, by considering the user's geographical location information, the analysis of highly relevant behaviors can be prioritized. Some or all of the above processing in the user behavior analysis unit may be performed using AI, for example, or without AI. For example, the user behavior analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant behaviors.

[0100] The real-time environment recognition unit can estimate the user's emotions and adjust the accuracy of environment recognition based on the estimated emotions. For example, if the user is tense, the real-time environment recognition unit can accurately recognize even subtle changes in the environment. For example, if the user is relaxed, the real-time environment recognition unit can also recognize broad changes in the environment. Furthermore, if the user is in a hurry, the real-time environment recognition unit can adjust the recognition accuracy to cope with rapid changes in the environment. In this way, the recognition accuracy is improved by adjusting the accuracy of environment recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The real-time environment recognition unit can select the optimal recognition method by referring to the user's past environment history during environment recognition. For example, the real-time environment recognition unit can select the optimal recognition method based on environment patterns the user has experienced in the past. For example, the real-time environment recognition unit can also select a recognition method appropriate to a specific situation from the user's past environment history. Furthermore, the real-time environment recognition unit can analyze the user's environment history and select the most effective recognition method. In this way, the optimal recognition method can be selected by referring to the user's past environment history. Some or all of the above processing in the real-time environment recognition unit may be performed using AI, for example, or without using AI. For example, the real-time environment recognition unit can input the user's environment history data into a generating AI and have the generating AI perform the selection of the optimal recognition method.

[0102] The real-time environment recognition unit can estimate the user's emotions and adjust the method of displaying the environment recognition results based on the estimated user emotions. For example, if the user is tense, the real-time environment recognition unit can provide a simple and highly visible display method. For example, if the user is relaxed, the real-time environment recognition unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the real-time environment recognition unit can provide a concise display method. This improves visibility by adjusting the method of displaying the environment recognition results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The real-time environment recognition unit can prioritize the recognition of highly relevant environments by considering the user's geographical location information during environment recognition. For example, if the user is in a specific location, the real-time environment recognition unit will prioritize the recognition of environments related to that location. For example, if the user is moving, the real-time environment recognition unit can also recognize relevant environments based on the user's current location. Furthermore, if the user is in a specific area, the real-time environment recognition unit can also prioritize the recognition of environments related to that area. In this way, by considering the user's geographical location information, highly relevant environments can be prioritized. Some or all of the above processing in the real-time environment recognition unit may be performed using AI, for example, or without AI. For example, the real-time environment recognition unit can input the user's geographical location data into a generating AI and have the generating AI perform the recognition of highly relevant environments.

[0104] The interactive user interface can estimate the user's emotions and adjust the interface display based on the estimated emotions. For example, if the user is tense, the interactive user interface can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the interactive user interface can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the interactive user interface can provide a simple and highly visible interface to facilitate the input process. This improves visibility by adjusting the interface display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The interactive user interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interactive user interface unit can select the optimal display method based on the interface design the user has used in the past. For example, the interactive user interface unit can also select a display method appropriate to a specific situation from the user's past operation history. Furthermore, the interactive user interface unit can analyze the user's operation history and select the most effective display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the interactive user interface unit may be performed using AI, for example, or without AI. For example, the interactive user interface unit can input user operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0106] The interactive user interface can estimate the user's emotions and adjust the interface's operation procedures based on those emotions. For example, if the user is tense, the interactive user interface can provide simple and intuitive operation procedures. For example, if the user is relaxed, the interactive user interface can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the interactive user interface can provide procedures that allow for quick operation. This improves usability by adjusting the interface's operation procedures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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.

[0107] The interactive user interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interactive user interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interactive user interface unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the interactive user interface unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the interactive user interface unit may be performed using AI, for example, or without AI. For example, the interactive user interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0108] The relevance enhancement unit can estimate the user's emotions and adjust how it improves the relevance of information based on the estimated emotions. For example, if the user is stressed, the relevance enhancement unit may prioritize providing important information. For example, if the user is relaxed, the relevance enhancement unit may also provide all information equally. Furthermore, if the user is in a hurry, the relevance enhancement unit may also prioritize providing short, important information. In this way, relevance is improved by adjusting how it improves the relevance of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The relevance improvement unit can select the optimal relevance improvement method by referring to the user's past information usage history when improving relevance. For example, the relevance improvement unit can select the optimal method based on the information provision methods the user has used in the past. For example, the relevance improvement unit can also select an information provision method appropriate to a specific situation from the user's past information usage history. Furthermore, the relevance improvement unit can analyze the user's information usage history and select the most effective information provision method. In this way, the optimal relevance improvement method can be selected by referring to the user's past information usage history. Some or all of the above processing in the relevance improvement unit may be performed using AI, for example, or without using AI. For example, the relevance improvement unit can input the user's information usage history data into a generating AI and have the generating AI perform the selection of the optimal relevance improvement method.

[0110] The relevance enhancement unit can estimate the user's emotions and adjust the way it displays the relevance enhancement results based on the estimated emotions. For example, if the user is stressed, the relevance enhancement unit can provide a simple and highly visible display method. For example, if the user is relaxed, the relevance enhancement unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the relevance enhancement unit can provide a concise display method. This improves visibility by adjusting the way the relevance enhancement results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The relevance enhancement unit can prioritize enhancing highly relevant information by considering the user's geographical location information during the relevance enhancement process. For example, if the user is in a specific location, the relevance enhancement unit can prioritize enhancing information related to that location. For example, if the user is on the move, the relevance enhancement unit can also enhance relevant information based on the user's current location. Furthermore, if the user is in a specific area, the relevance enhancement unit can also prioritize enhancing information related to that area. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the relevance enhancement unit may be performed using AI, for example, or without AI. For example, the relevance enhancement unit can input the user's geographical location data into a generating AI and have the generating AI perform the enhancement of highly relevant information.

[0112] An interface designed for the visually impaired and elderly can estimate the user's emotions and adjust the interface display based on those emotions. For example, if the user is stressed, the interface can provide a calming color scheme to reduce visual stress. Similarly, if the user is enjoying themselves, it can provide a bright color scheme to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to facilitate input. This improves visibility by adjusting the interface display 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] An interface designed for the visually impaired and elderly can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface can select the optimal display method based on the user's past interface designs. It can also select a display method appropriate to a specific situation based on the user's past operation history. Furthermore, the interface can analyze the user's operation history to select the most effective display method. This allows for the selection of the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface designed for the visually impaired and elderly may be performed using AI, or without AI. For example, the interface can input user operation history data into a generating AI and have the generating AI select the optimal display method.

[0114] An interface designed to be user-friendly for the visually impaired and the elderly can estimate the user's emotions and adjust the interface's operation procedures based on those emotions. For example, if the user is tense, the interface can provide simple and intuitive operation procedures. If the user is relaxed, it can also provide detailed operation procedures. Furthermore, if the user is in a hurry, it can provide procedures that allow for quick operation. This improves usability by adjusting the interface's operation procedures 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.

[0115] The user-friendly interface for the visually impaired and the elderly can select the optimal display method by considering the user's device information when displaying the interface. For example, if the user is using a smartphone, the user-friendly interface for the visually impaired and the elderly can provide a display method that is adapted to the screen size. For example, if the user is using a tablet, the user-friendly interface for the visually impaired and the elderly can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the user-friendly interface for the visually impaired and the elderly can provide a simple and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the user-friendly interface for the visually impaired and the elderly may be performed using AI, for example, or without AI. For example, the user-friendly interface for the visually impaired and the elderly can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The vision assist agent can also be equipped with a health management unit that monitors the user's health status. This unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature in real time. For instance, it can measure the user's heart rate using a heart rate sensor and issue an alert if an abnormality is detected. It can also measure the user's blood pressure using a blood pressure sensor and notify a medical institution if an abnormality is detected. Furthermore, it can measure the user's body temperature using a body temperature sensor and prompt the user to rest if a fever is detected. This allows the vision assist agent to monitor the user's health status in real time and take appropriate action if an abnormality is detected.

[0118] A vision assist agent can also be equipped with a music provider that estimates the user's emotions and selects music based on those emotions. For example, if the user is relaxed, the music provider can provide calming music. For example, if the user is stressed, the music provider can provide relaxing music. Furthermore, if the user is having fun, the music provider can provide upbeat music. In addition, if the user wants to concentrate, the music provider can provide music that enhances concentration. In this way, the vision assist agent can provide the optimal music according to the user's emotions and support the user's mood and state.

[0119] Vision assist agents can also be equipped with a schedule management unit to manage the user's schedule. This unit can, for example, manage appointments in conjunction with the user's calendar app. It can, for instance, check the user's schedule and send notifications when upcoming appointments are approaching. It can also suggest the optimal route based on the user's schedule. Furthermore, it can set reminders to ensure important appointments are not forgotten. In this way, vision assist agents can support the user's schedule management and enable efficient time management.

[0120] The vision assist agent can also be equipped with an exercise suggestion unit that estimates the user's emotions and suggests exercises based on those emotions. For example, if the user is feeling stressed, the exercise suggestion unit may suggest relaxing yoga or stretching. If the user is feeling energetic, the exercise suggestion unit may suggest active exercises such as running or dancing. If the user is feeling tired, the exercise suggestion unit may suggest light walking or relaxation exercises. Furthermore, if the user wants to concentrate, the exercise suggestion unit may suggest exercises that enhance concentration. In this way, the vision assist agent can suggest the most suitable exercise according to the user's emotions, supporting the user's health and well-being.

[0121] The vision assist agent can also be equipped with a meal management unit to manage the user's diet. This unit can, for example, record the user's meal history and analyze nutritional balance. It can record the calories and nutrients consumed by the user and suggest healthy meals. Furthermore, based on the user's meal history, the unit can suggest menus for the next meal. In addition, the unit can provide customized meal plans according to the user's health condition and goals. This allows the vision assist agent to support the user's meal management and help them achieve a healthy lifestyle.

[0122] The vision assist agent may also include a relaxation suggestion unit that estimates the user's emotions and proposes relaxation methods based on those emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest deep breathing or meditation. For example, if the user is feeling tense, the relaxation suggestion unit may suggest aromatherapy with relaxing effects. Furthermore, if the user is tired, the relaxation suggestion unit may suggest a relaxation massage. In addition, if the user wants to relax, the relaxation suggestion unit may provide calming music or nature sounds. In this way, the vision assist agent can propose the most suitable relaxation method according to the user's emotions, supporting the user's stress reduction and relaxation.

[0123] Vision assist agents can also be equipped with a sleep management unit to manage the user's sleep. This unit can, for example, monitor the user's sleep patterns and analyze sleep quality. It can record the user's sleep duration and depth, and suggest areas for improvement. Furthermore, it can analyze the user's sleep environment and suggest an optimal environment. In addition, it can provide a customized sleep plan based on the user's sleep history. This allows vision assist agents to support the user's sleep management and help them achieve high-quality sleep.

[0124] The vision assist agent may also be equipped with a communication suggestion unit that estimates the user's emotions and proposes communication methods based on those emotions. For example, if the user is feeling stressed, the communication suggestion unit may propose communication in a relaxed atmosphere. For example, if the user is feeling tense, the communication suggestion unit may propose topics that have a relaxing effect. Furthermore, if the user is having fun, the communication suggestion unit may propose enjoyable topics. In addition, if the user wants to concentrate, the communication suggestion unit may propose communication methods that enhance concentration. In this way, the vision assist agent can propose the most suitable communication method according to the user's emotions and support smooth communication.

[0125] The vision assist agent can also be equipped with a learning support unit to assist the user's learning. This unit can, for example, record the user's learning history and manage their learning progress. It can also suggest the next learning topic based on the user's learning history. Furthermore, it can suggest the optimal learning method tailored to the user's learning style. In addition, it can provide a customized learning plan according to the user's learning goals. This allows the vision assist agent to support the user's learning and achieve efficient learning.

[0126] The vision assist agent can also be equipped with a travel suggestion unit that estimates the user's emotions and proposes travel plans based on those emotions. For example, if the user wants to relax, the travel suggestion unit may suggest a quiet resort. For example, if the user is seeking adventure, the travel suggestion unit may suggest an active travel destination. Furthermore, if the user wants to enjoy a cultural experience, the travel suggestion unit may suggest a historical tourist destination. In addition, if the user wants to enjoy nature, the travel suggestion unit may suggest a travel destination rich in nature. In this way, the vision assist agent can propose the optimal travel plan according to the user's emotions and enrich the user's travel experience.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception unit recognizes the user's voice. The user's voice includes voice commands and natural language utterances. The reception unit can recognize the user's voice using speech recognition technology and can also remove ambient noise using noise cancellation technology. In addition, the reception unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated emotions. Step 2: The analysis unit analyzes the user's gaze and surrounding environment based on the voice recognized by the reception unit. Eye-tracking devices and gaze location information can be used for eye tracking, and temperature sensors and illuminance sensors can be used for analyzing the surrounding environment. Furthermore, the analysis unit can also analyze user behavior using deep learning. Step 3: The service provider provides necessary information in real time based on the data analyzed by the analysis unit. The service provider improves the relevance of the information provided based on user preferences and past behavioral history. It also provides an interface that is easy to use for visually impaired and elderly users.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, user behavior analysis unit, real-time environment recognition unit, interactive user interface unit, relevance improvement unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and recognizes the user's voice. The analysis unit analyzes the user's gaze and surrounding environment using the eye-tracking device and sensors of the smart device 14. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides necessary information in real time. The user behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's behavior using deep learning. The real-time environment recognition unit recognizes the surrounding environment using the sensors of the smart device 14. The interactive user interface unit provides interactive operation using the voice recognition technology and eye-tracking device of the smart device 14. The relevance improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the relevance of the information provided based on the user's preferences. The interface is implemented by the control unit 46A of the smart device 14, providing an interface that is easy to use for visually impaired and elderly people. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, user behavior analysis unit, real-time environment recognition unit, interactive user interface unit, relevance enhancement unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and recognizes the user's voice. The analysis unit analyzes the user's gaze and surrounding environment using the eye-tracking device and sensors of the smart glasses 214. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides necessary information in real time. The user behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's behavior using deep learning. The real-time environment recognition unit recognizes the surrounding environment using the sensors of the smart glasses 214. The interactive user interface unit provides interactive operation using the voice recognition technology and eye-tracking device of the smart glasses 214. The relevance enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the relevance of the information provided based on the user's preferences. The interface is implemented by the control unit 46A of the smart glasses 214, providing an interface that is easy for visually impaired people and the elderly to use. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, user behavior analysis unit, real-time environment recognition unit, interactive user interface unit, relevance improvement unit, and interface unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and recognizes the user's voice. The analysis unit analyzes the user's gaze and surrounding environment using the eye-tracking device and sensors of the headset terminal 314. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides necessary information in real time. The user behavior analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's behavior using deep learning. The real-time environment recognition unit recognizes the surrounding environment using the sensors of the headset terminal 314. The interactive user interface unit provides interactive operation using the voice recognition technology and eye-tracking device of the headset terminal 314. The relevance enhancement unit is implemented by the specific processing unit 290 of the data processing device 12, and improves the relevance of the information provided based on the user's preferences. The interface unit is implemented by the control unit 46A of the headset terminal 314, and provides an interface that is easy to use for visually impaired and elderly people. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, user behavior analysis unit, real-time environment recognition unit, interactive user interface unit, relevance enhancement unit, and interface unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and recognizes the user's voice. The analysis unit analyzes the gaze and surrounding environment using the gaze tracking device and sensors of the robot 414. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides necessary information in real time. The user behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's behavior using deep learning. The real-time environment recognition unit recognizes the surrounding environment using the sensors of the robot 414. The interactive user interface unit provides interactive operation using the voice recognition technology and gaze tracking device of the robot 414. The relevance enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the relevance of the information provided based on the user's preferences. The interface is implemented by the control unit 46A of the robot 414, providing an interface that is easy to use for visually impaired and elderly people. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A reception desk that recognizes user feedback, An analysis unit analyzes gaze and surrounding environment based on the voice recognized by the reception unit, A provisioning unit provides the necessary information in real time based on the information analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) It includes a user behavior analysis unit that uses deep learning. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a real-time environment recognition unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features an interactive user interface that utilizes voice and eye-tracking. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a relevance enhancement unit that improves the relevance of information provided based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features an interface that is easy for visually impaired people and the elderly to use. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the accuracy of voice recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past speech history and selects the optimal speech recognition model. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When recognizing voices, noise cancellation is performed according to the ambient noise level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voices to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When recognizing voices, the system prioritizes recognizing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During voice recognition, the system analyzes the user's social media activity and identifies relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the accuracy of gaze and environment analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, the analysis method is switched in real time in response to changes in the surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of information delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past information usage history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the content of the information provided will be customized according to the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned user behavior analysis unit, It estimates the user's emotions and adjusts the accuracy of behavioral analysis based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned user behavior analysis unit, During behavioral analysis, the optimal analysis method is selected by referring to the user's past behavioral history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned user behavior analysis unit, Adjusting how we estimate user emotions and display behavioral analysis results based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned user behavior analysis unit, When analyzing user behavior, prioritize analyzing highly relevant behaviors by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The real-time environment recognition unit is It estimates the user's emotions and adjusts the accuracy of environmental recognition based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The real-time environment recognition unit is During environment recognition, the system selects the optimal recognition method by referring to the user's past environment history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The real-time environment recognition unit is Adjusting how we estimate user emotions and display environmental awareness results based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The real-time environment recognition unit is During environment recognition, the system prioritizes recognizing environments that are highly relevant, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned interactive user interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned interactive user interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned interactive user interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned interactive user interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned relationship improvement unit is, We estimate user sentiment and adjust how we improve the relevance of information based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned relationship improvement unit is, When improving relevance, the system selects the most suitable relevance improvement method by referring to the user's past information usage history. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned relationship improvement unit is, Adjusting how we estimate user sentiment and display relevance improvement results based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned relationship improvement unit is, When improving relevance, the system prioritizes improving highly relevant information by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned interface section, which is easy to use for visually impaired and elderly people, It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned interface section, which is easy to use for visually impaired and elderly people, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 6, characterized by the features described herein. (Note 43) The aforementioned interface section, which is easy to use for visually impaired and elderly people, It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 44) The aforementioned interface section, which is easy to use for visually impaired and elderly people, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0201] 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. A reception desk that recognizes user feedback, An analysis unit analyzes gaze and surrounding environment based on the voice recognized by the reception unit, A provisioning unit provides the necessary information in real time based on the information analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. Equipped with a real-time environment recognition unit. The system according to feature 1.

3. It features an interactive user interface that utilizes voice and eye-tracking. The system according to feature 1.

4. It includes a relevance enhancement unit that improves the relevance of information provided based on user preferences. The system according to feature 1.

5. It features an interface that is easy for visually impaired people and the elderly to use. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the accuracy of voice recognition based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past speech history and selects the optimal speech recognition model. The system according to feature 1.

8. The aforementioned reception unit is When recognizing voices, noise cancellation is performed according to the ambient noise level. The system according to feature 1.

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