system

Abstract

The system according to this embodiment aims to enable visually impaired and elderly people to understand their surroundings and act safely. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a warning unit, and a learning unit. The acquisition unit captures the surrounding environment with a camera. The analysis unit analyzes the environmental data acquired by the acquisition unit. The warning unit issues a voice warning based on the data analyzed by the analysis unit. The learning unit learns the user's behavior patterns based on the data 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, there was a problem that it was difficult for visually impaired people and the elderly to grasp the surrounding environment and move safely.

[0005] The system according to an embodiment aims to enable visually impaired people and the elderly to grasp the surrounding environment and move safely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a warning unit, and a learning unit. The acquisition unit captures the surrounding environment with a camera. The analysis unit analyzes the environmental data acquired by the acquisition unit. The warning unit issues audible warnings based on the data analyzed by the analysis unit. The learning unit learns the user's behavior patterns based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable visually impaired people and the elderly to understand their surroundings and act safely. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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). <000009​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) An AI agent system according to an embodiment of the present invention is a system that supports the daily lives of visually impaired and elderly people through smart glasses. This AI agent system captures the surrounding environment with a camera, analyzes it in real time with AI, and provides voice warnings of danger, thereby providing a safe and independent life. The AI ​​agent system quickly detects potential dangers that the user may face by capturing the surrounding environment with a camera mounted on the smart glasses and instantly analyzing it with AI. Based on the analysis results, the AI ​​agent system warns the user of danger with voice. For example, if there is an obstacle while walking, it will alert the user of its presence with voice. Furthermore, the AI ​​agent system learns the user's behavior patterns and provides customized support for each individual user. This enables optimal support tailored to the user's needs. This system is expected to reduce the accident rate among visually impaired and elderly people by 30% annually and improve the average time they can act independently by 50%. It is also expected to increase their participation in social activities by 40%. This AI agent system utilizes the latest image recognition technology and AI analysis, and achieves intuitive operability through a user-centered design. Furthermore, it aims to contribute to society through sustainable technological development. This allows the AI ​​agent system to support the daily lives of visually impaired and elderly people, providing them with a safe and independent lifestyle.

[0029] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, a warning unit, and a learning unit. The acquisition unit captures the surrounding environment with a camera. The acquisition unit can capture the surrounding environment using, for example, a camera mounted on smart glasses. The acquisition unit can also acquire the surrounding environment in real time using AI. The analysis unit analyzes the environmental data acquired by the acquisition unit. The analysis unit can analyze the environmental data using, for example, image analysis technology. The analysis unit can also analyze the environmental data in real time using AI. The warning unit issues a warning in voice based on the data analyzed by the analysis unit. The warning unit can issue a warning using, for example, speech synthesis technology. The warning unit can also issue an appropriate warning based on the analysis results using AI. The learning unit learns the user's behavior patterns based on the data analyzed by the analysis unit. The learning unit can learn behavior patterns using, for example, machine learning technology. The learning unit can also learn the user's behavior patterns in real time using AI. As a result, the AI ​​agent system according to this embodiment can support the daily lives of visually impaired people and the elderly, and provide them with a safe and independent life.

[0030] The acquisition unit captures the surrounding environment using a camera. For example, the acquisition unit can capture the surrounding environment using a camera mounted on smart glasses. The camera mounted on smart glasses uses a wide-angle lens to cover a wide field of view and capture the surrounding situation in detail. The camera is high resolution and may also be equipped with an infrared sensor to acquire clear images even in low-light environments. The acquisition unit can also acquire the surrounding environment in real time using AI. The AI ​​processes the video data from the camera in real time and performs functions such as object recognition, face recognition, and text recognition. For example, the AI ​​can identify pedestrians, vehicles, obstacles, etc., and track their location and movement. The AI ​​can also acquire surrounding audio information and use speech recognition technology to detect important audio events. As a result, the acquisition unit can collect comprehensive environmental data that includes not only visual information but also audio information. Furthermore, the acquisition unit can send the collected data to a cloud server and cooperate with other systems and devices. As a result, the acquisition unit can efficiently collect a wide range of data and improve the overall information processing capability of the system.

[0031] The analysis unit analyzes the environmental data acquired by the acquisition unit. For example, the analysis unit can analyze environmental data using image analysis technology. Image analysis technology includes object detection, face recognition, and text recognition, and these technologies are combined to perform a detailed analysis of the environmental data. The analysis unit can also analyze environmental data in real time using AI. The AI ​​uses deep learning algorithms to extract important information from the acquired video data and generate analysis results. For example, the AI ​​can analyze pedestrian movement to suggest safe walking routes or analyze vehicle movement to understand traffic conditions. Furthermore, the AI ​​can use text recognition technology to analyze textual information on signs and notices and provide users with necessary information. In addition, the analysis unit can analyze audio data and identify surrounding audio events. For example, the AI ​​can detect important audio events such as car horns and ambulance sirens and issue warnings to the user. This allows the analysis unit to integrate visual and audio information to perform a comprehensive environmental analysis and provide users with appropriate information.

[0032] The warning unit issues voice warnings based on data analyzed by the analysis unit. The warning unit can, for example, use speech synthesis technology to provide warnings. Speech synthesis technology generates speech from text to produce natural-sounding voices, providing users with clear and easy-to-understand warnings. The warning unit can also use AI to provide appropriate warnings based on the analysis results. Based on the data provided by the analysis unit, the AI ​​selects information important to the user and issues warnings at the appropriate time. For example, the AI ​​can issue voice warnings if a pedestrian is approaching a dangerous intersection or if a vehicle is rapidly approaching. Furthermore, the AI ​​can learn the user's behavior patterns and past warning history to provide individually optimized warnings. This allows the warning unit to play a crucial role in ensuring user safety and preventing accidents and dangers. In addition, the warning unit can use other warning methods besides voice, such as vibration and light. For example, it can communicate warnings through sight and touch by vibrating the smart glasses frame or flashing LED lights. This allows the warning unit to provide warnings to the user through a variety of means, ensuring that they pay attention.

[0033] The learning unit learns user behavior patterns based on data analyzed by the analysis unit. For example, the learning unit can learn behavior patterns using machine learning techniques. Machine learning techniques are used to train models based on large amounts of data and predict user behavior patterns and preferences. The learning unit can also learn user behavior patterns in real time using AI. The AI ​​analyzes the user's travel and behavior history to learn daily behavior patterns and reactions in specific situations. For example, the AI ​​can learn that a user tends to visit a specific place at a specific time and provide appropriate support based on that information. The AI ​​can also learn user preferences and interests and provide individually customized information and services. This allows the learning unit to provide personalized support tailored to user needs, making daily life more comfortable and convenient. Furthermore, the learning unit can collect user feedback and continuously improve its learning model. For example, by providing feedback on the information and services provided, the AI ​​can update its learning model based on that feedback, providing more accurate predictions and support. This allows the learning unit to continuously learn user behavior patterns and improve the overall system performance.

[0034] The warning unit can alert the user by voice if there is an obstacle while walking. For example, the warning unit provides a voice warning based on the obstacle detection result. The warning unit can also use AI to provide appropriate warnings based on the obstacle detection result. For example, the warning unit ensures user safety by providing a voice alert if there is an obstacle while walking. The warning unit can also provide appropriate warnings depending on the type and location of the obstacle. This ensures user safety by providing a voice alert for obstacles while walking.

[0035] The learning unit can learn user behavior patterns and provide customized support to individual users. For example, the learning unit collects user behavior data and learns behavior patterns. The learning unit can also use AI to learn user behavior patterns in real time. For example, the learning unit provides optimal support to individual users based on their behavior patterns. The learning unit can also use AI to provide customized support based on user behavior patterns. This allows the learning unit to provide optimal support to individual users by learning their behavior patterns.

[0036] The acquisition unit may include a camera mounted on the smart glasses. The acquisition unit, for example, captures the surrounding environment using the camera mounted on the smart glasses. The acquisition unit can also use AI to acquire the surrounding environment in real time using the camera mounted on the smart glasses. The acquisition unit acquires appropriate environmental data according to the resolution and field of view of the camera mounted on the smart glasses, for example. The acquisition unit can also use AI to acquire environmental data by making the most of the performance of the camera mounted on the smart glasses. This makes it possible to effectively acquire the surrounding environment by using the camera mounted on the smart glasses.

[0037] The analysis unit can analyze environmental data in real time. For example, the analysis unit can immediately analyze environmental data acquired in real time. The analysis unit can also use AI to analyze environmental data in real time. For example, by analyzing environmental data in real time, the analysis unit can enable a rapid response. By using AI to analyze environmental data in real time, the analysis unit can also provide more accurate analysis results. This enables a rapid response by analyzing environmental data in real time.

[0038] The acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring information about the surrounding environment. For example, the acquisition unit can store the user's past behavior history in a database and refer to it as needed. The acquisition unit can also refer to the user's past behavior history in real time using AI. For example, the acquisition unit can prioritize acquiring routes that the user has frequently taken in the past. The acquisition unit can also select the optimal acquisition method based on the user's past behavior history using AI. For example, the acquisition unit can acquire information needed for a specific time period from the user's past behavior history. The acquisition unit can also analyze the user's past behavior patterns using AI and select the optimal acquisition method. This allows the system to select the optimal acquisition method by referring to the user's past behavior history.

[0039] The data acquisition unit can filter the captured environmental data according to the user's current activity. For example, the data acquisition unit can detect the user's current activity and perform filtering accordingly. The data acquisition unit can also use AI to filter based on the user's current activity. For example, if the user is walking, the data acquisition unit will prioritize acquiring information about obstacles. The data acquisition unit can also use AI to filter necessary information according to the user's current activity. For example, if the user is resting, the data acquisition unit will filter out ambient noise and movement to provide a quiet environment. The data acquisition unit can also use AI to acquire optimal information based on the user's current activity. This allows for the priority acquisition of necessary information by filtering according to the user's current activity.

[0040] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information in the environmental data it captures. For example, the data acquisition unit can acquire the user's geographical location information as GPS data. The data acquisition unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the data acquisition unit will prioritize the acquisition of information related to that location. The data acquisition unit can also acquire optimal information based on the user's geographical location information using AI. For example, if the user is on the move, the data acquisition unit will acquire the necessary information based on their current location. The data acquisition unit can also prioritize the acquisition of highly relevant information based on the user's geographical location information using AI. This allows for the priority acquisition of highly relevant information by considering the user's geographical location information.

[0041] The data acquisition unit can analyze the user's social media activity in the captured environmental data and obtain relevant information. For example, the data acquisition unit can store the user's social media activity in a database and refer to it as needed. The data acquisition unit can also use AI to analyze the user's social media activity in real time. For example, the data acquisition unit can prioritize obtaining information about places that the user has shared on social media. The data acquisition unit can also use AI to obtain optimal information based on the user's social media activity. For example, the data acquisition unit can obtain information about topics that the user has shown interest in on social media. The data acquisition unit can also use AI to obtain relevant information based on the user's social media activity. In this way, relevant information can be obtained by analyzing the user's social media activity.

[0042] The analysis unit can apply the optimal analysis algorithm by referring to past analysis results when analyzing environmental data. For example, the analysis unit can store past analysis results in a database and refer to them as needed. The analysis unit can also use AI to refer to past analysis results in real time. For example, the analysis unit can select the optimal algorithm for a specific situation from past analysis results. The analysis unit can also use AI to apply the optimal analysis algorithm based on past analysis results. For example, the analysis unit can dynamically adjust the analysis algorithm based on past analysis results. The analysis unit can also use AI to improve the accuracy of the analysis by referring to past analysis results. This allows the application of the optimal analysis algorithm by referring to past analysis results.

[0043] The analysis unit can apply analysis methods tailored to the user's current activity when analyzing environmental data. For example, the analysis unit can detect the user's current activity and apply an analysis method accordingly. The analysis unit can also use AI to apply analysis methods based on the user's current activity. For example, if the user is walking, the analysis unit can apply an analysis method specifically designed for obstacle detection. The analysis unit can also use AI to apply the most suitable analysis method according to the user's current activity. For example, if the user is resting, the analysis unit can analyze surrounding sounds and movements to provide a quiet environment. The analysis unit can also use AI to analyze necessary information based on the user's current activity. This allows for the effective provision of necessary information by applying analysis methods tailored to the user's current activity.

[0044] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information when analyzing environmental data. For example, the analysis unit can acquire the user's geographical location information as GPS data. The analysis unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the analysis unit will prioritize analyzing information related to that location. The analysis unit can also use AI to analyze the most relevant information based on the user's geographical location information. For example, if the user is on the move, the analysis unit will analyze the necessary information based on their current location. The analysis unit can also use AI to improve the accuracy of its analysis based on the user's geographical location information. In this way, the accuracy of the analysis can be improved by considering the user's geographical location information.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases when analyzing environmental data. For example, the analysis unit can store relevant literature in a database and refer to it as needed. The analysis unit can also use AI to refer to relevant literature and databases in real time. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can also use AI to apply the optimal analysis algorithm based on relevant literature and databases. For example, the analysis unit can improve the accuracy of its analysis results by referring to databases. The analysis unit can also use AI to dynamically adjust the accuracy of the analysis based on relevant literature and databases. This allows for improved analysis accuracy by referring to relevant literature and databases.

[0046] The warning unit can select the most appropriate warning method by referring to the user's past behavior history when issuing a warning. For example, the warning unit can store the user's past behavior history in a database and refer to it as needed. The warning unit can also use AI to refer to the user's past behavior history in real time. For example, the warning unit can prioritize using warning methods that the user has responded to in the past. The warning unit can also use AI to select the most appropriate warning method based on the user's past behavior history. For example, the warning unit can select the most appropriate warning method for a specific situation from the user's past behavior history. The warning unit can also use AI to analyze the user's past behavior patterns and select the most appropriate warning method. This allows the system to select the most appropriate warning method by referring to the user's past behavior history.

[0047] The warning unit can apply a warning method that is appropriate to the user's current activity when issuing a warning. For example, the warning unit can detect the user's current activity and apply a warning method accordingly. The warning unit can also use AI to apply a warning method based on the user's current activity. For example, if the user is walking, the warning unit will prioritize warnings about obstacles. The warning unit can also use AI to apply the most appropriate warning method according to the user's current activity. For example, if the user is resting, the warning unit will warn about surrounding sounds and movements to provide a quiet environment. The warning unit can also use AI to provide necessary warnings based on the user's current activity. This allows the system to effectively provide necessary warnings by applying a warning method that is appropriate to the user's current activity.

[0048] The warning unit can select the optimal warning method by considering the user's geographical location when issuing a warning. For example, the warning unit can acquire the user's geographical location as GPS data. The warning unit can also acquire the user's geographical location in real time using AI. For example, if the user is in a specific location, the warning unit will prioritize warnings related to that location. The warning unit can also select the optimal warning method based on the user's geographical location using AI. For example, if the user is on the move, the warning unit will issue necessary warnings based on their current location. The warning unit can also provide optimal warnings based on the user's geographical location using AI. For example, if the user is approaching a destination, the warning unit will prioritize warnings related to the destination. The warning unit can also provide optimal warning methods based on the user's geographical location using AI. This allows the system to select the optimal warning method by considering the user's geographical location.

[0049] The alerting unit can analyze the user's social media activity and issue relevant warnings when issuing alerts. For example, the alerting unit can store the user's social media activity in a database and refer to it as needed. The alerting unit can also use AI to analyze the user's social media activity in real time. For example, the alerting unit can prioritize alerts related to places the user has shared on social media. The alerting unit can also use AI to provide optimal alerts based on the user's social media activity. For example, the alerting unit can issue alerts related to topics the user has shown interest in on social media. The alerting unit can also use AI to issue relevant warnings based on the user's social media activity. For example, the alerting unit can issue alerts based on information shared by the user's social media followers. The alerting unit can also use AI to provide optimal alerts based on the user's social media activity. This allows the system to provide relevant warnings by analyzing the user's social media activity.

[0050] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can store past learning data in a database and refer to it as needed. The learning unit can also use AI to refer to past learning data in real time. For example, the learning unit can select the optimal algorithm for a specific situation from past learning data. The learning unit can also use AI to dynamically optimize the learning algorithm based on past learning data. For example, the learning unit adjusts the learning algorithm based on past learning data. The learning unit can also use AI to improve the accuracy of learning by referring to past learning data. This allows for the optimization of the learning algorithm by referring to past learning data.

[0051] The learning unit can apply learning methods tailored to the user's current activity during learning. For example, the learning unit can detect the user's current activity and apply a learning method accordingly. The learning unit can also use AI to apply learning methods based on the user's current activity. For example, if the user is walking, the learning unit can apply a learning method specifically for obstacle detection. The learning unit can also use AI to apply the optimal learning method according to the user's current activity. For example, if the user is resting, the learning unit can learn surrounding sounds and movements to provide a quiet environment. The learning unit can also use AI to learn necessary information based on the user's current activity. For example, if the user is shopping, the learning unit can learn and provide location information of products. The learning unit can also use AI to provide the optimal learning method based on the user's current activity. This allows the system to effectively learn necessary information by applying learning methods tailored to the user's current activity.

[0052] The learning unit can weight the training data by considering the user's geographical location information during the learning process. For example, the learning unit can acquire the user's geographical location information as GPS data. The learning unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the learning unit will prioritize learning information related to that location. The learning unit can also dynamically weight the training data based on the user's geographical location information using AI. For example, if the user is on the move, the learning unit will learn the necessary information based on their current location. The learning unit can also provide optimal training data based on the user's geographical location information using AI. For example, if the user is approaching a destination, the learning unit will prioritize learning information related to that destination. The learning unit can also optimize the weighting of the training data based on the user's geographical location information using AI. This allows the training data to be weighted by considering the user's geographical location information.

[0053] The learning unit can improve the accuracy of its learning by referring to relevant literature and databases during the learning process. For example, the learning unit can store relevant literature in a database and refer to it as needed. The learning unit can also use AI to refer to relevant literature and databases in real time. For example, the learning unit can optimize its learning algorithm by referring to relevant literature. The learning unit can also use AI to apply the optimal learning algorithm based on relevant literature and databases. For example, the learning unit can improve the accuracy of its learning results by referring to databases. The learning unit can also use AI to dynamically adjust the accuracy of learning based on relevant literature and databases. This allows the learning accuracy to be improved by referring to relevant literature and databases.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can monitor the user's health status and adjust the camera's acquisition range according to that status. For example, it can measure the user's heart rate and blood pressure with sensors, and if the user's health status deteriorates, it can widen the camera's acquisition range to acquire more surrounding information. Conversely, if the user's health status is stable, it can narrow the camera's acquisition range to acquire only the necessary information. Furthermore, it can determine the priority of information to acquire according to the user's health status, prioritizing the acquisition of important information. In this way, by adjusting the camera's acquisition range according to the user's health status, necessary information can be acquired effectively.

[0056] The warning unit can select the most appropriate warning method by referring to the user's past behavior history. For example, it can prioritize the use of warning methods that the user has responded to in the past. It can also select the most appropriate warning method for a specific situation based on the user's past behavior history. Furthermore, it can analyze the user's past behavior patterns to select the most appropriate warning method. In this way, the system can select the most appropriate warning method by referring to the user's past behavior history.

[0057] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location. For example, by acquiring the user's geographical location as GPS data, if the user is in a specific location, it can prioritize the acquisition of information related to that location. Furthermore, if the user is on the move, it can acquire necessary information based on their current location. It can also acquire the most relevant information based on the user's geographical location. In this way, by considering the user's geographical location, it can prioritize the acquisition of highly relevant information.

[0058] The warning unit can apply warning methods tailored to the user's current activity. For example, if the user is walking, it can prioritize warnings about obstacles. If the user is resting, it can provide a quiet environment by warning about surrounding sounds and movements. Furthermore, it can apply the most appropriate warning method based on the user's current activity. This allows for the effective provision of necessary warnings by applying warning methods tailored to the user's current activity.

[0059] The acquisition unit can analyze a user's social media activity and retrieve relevant information. For example, it can store a user's social media activity in a database and refer to it as needed. It can also prioritize the acquisition of information about locations shared by the user on social media. Furthermore, it can acquire optimal information based on the user's social media activity. In this way, relevant information can be obtained by analyzing a user's social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The acquisition unit captures the surrounding environment with a camera. The acquisition unit can capture the surrounding environment using, for example, a camera mounted on smart glasses. The acquisition unit can also acquire the surrounding environment in real time using AI. Step 2: The analysis unit analyzes the environmental data acquired by the acquisition unit. The analysis unit can analyze the environmental data using, for example, image analysis technology. The analysis unit can also analyze the environmental data in real time using AI. Step 3: The warning unit issues an audible warning based on the data analyzed by the analysis unit. The warning unit can issue warnings using, for example, speech synthesis technology. The warning unit can also use AI to issue appropriate warnings based on the analysis results. Step 4: The learning unit learns user behavior patterns based on the data analyzed by the analysis unit. The learning unit can learn behavior patterns using, for example, machine learning techniques. The learning unit can also learn user behavior patterns in real time using AI.

[0062] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that supports the daily lives of visually impaired and elderly people through smart glasses. This AI agent system captures the surrounding environment with a camera, analyzes it in real time with AI, and provides voice warnings of danger, thereby providing a safe and independent life. The AI ​​agent system quickly detects potential dangers that the user may face by capturing the surrounding environment with a camera mounted on the smart glasses and instantly analyzing it with AI. Based on the analysis results, the AI ​​agent system warns the user of danger with voice. For example, if there is an obstacle while walking, it will alert the user of its presence with voice. Furthermore, the AI ​​agent system learns the user's behavior patterns and provides customized support for each individual user. This enables optimal support tailored to the user's needs. This system is expected to reduce the accident rate among visually impaired and elderly people by 30% annually and improve the average time they can act independently by 50%. It is also expected to increase their participation in social activities by 40%. This AI agent system utilizes the latest image recognition technology and AI analysis, and achieves intuitive operability through a user-centered design. Furthermore, it aims to contribute to society through sustainable technological development. This allows the AI ​​agent system to support the daily lives of visually impaired and elderly people, providing them with a safe and independent lifestyle.

[0063] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, a warning unit, and a learning unit. The acquisition unit captures the surrounding environment with a camera. The acquisition unit can capture the surrounding environment using, for example, a camera mounted on smart glasses. The acquisition unit can also acquire the surrounding environment in real time using AI. The analysis unit analyzes the environmental data acquired by the acquisition unit. The analysis unit can analyze the environmental data using, for example, image analysis technology. The analysis unit can also analyze the environmental data in real time using AI. The warning unit issues a warning in voice based on the data analyzed by the analysis unit. The warning unit can issue a warning using, for example, speech synthesis technology. The warning unit can also issue an appropriate warning based on the analysis results using AI. The learning unit learns the user's behavior patterns based on the data analyzed by the analysis unit. The learning unit can learn behavior patterns using, for example, machine learning technology. The learning unit can also learn the user's behavior patterns in real time using AI. As a result, the AI ​​agent system according to this embodiment can support the daily lives of visually impaired people and the elderly, and provide them with a safe and independent life.

[0064] The acquisition unit captures the surrounding environment using a camera. For example, the acquisition unit can capture the surrounding environment using a camera mounted on smart glasses. The camera mounted on smart glasses uses a wide-angle lens to cover a wide field of view and capture the surrounding situation in detail. The camera is high resolution and may also be equipped with an infrared sensor to acquire clear images even in low-light environments. The acquisition unit can also acquire the surrounding environment in real time using AI. The AI ​​processes the video data from the camera in real time and performs functions such as object recognition, face recognition, and text recognition. For example, the AI ​​can identify pedestrians, vehicles, obstacles, etc., and track their location and movement. The AI ​​can also acquire surrounding audio information and use speech recognition technology to detect important audio events. As a result, the acquisition unit can collect comprehensive environmental data that includes not only visual information but also audio information. Furthermore, the acquisition unit can send the collected data to a cloud server and cooperate with other systems and devices. As a result, the acquisition unit can efficiently collect a wide range of data and improve the overall information processing capability of the system.

[0065] The analysis unit analyzes the environmental data acquired by the acquisition unit. For example, the analysis unit can analyze environmental data using image analysis technology. Image analysis technology includes object detection, face recognition, and text recognition, and these technologies are combined to perform a detailed analysis of the environmental data. The analysis unit can also analyze environmental data in real time using AI. The AI ​​uses deep learning algorithms to extract important information from the acquired video data and generate analysis results. For example, the AI ​​can analyze pedestrian movement to suggest safe walking routes or analyze vehicle movement to understand traffic conditions. Furthermore, the AI ​​can use text recognition technology to analyze textual information on signs and notices and provide users with necessary information. In addition, the analysis unit can analyze audio data and identify surrounding audio events. For example, the AI ​​can detect important audio events such as car horns and ambulance sirens and issue warnings to the user. This allows the analysis unit to integrate visual and audio information to perform a comprehensive environmental analysis and provide users with appropriate information.

[0066] The warning unit issues voice warnings based on data analyzed by the analysis unit. The warning unit can, for example, use speech synthesis technology to provide warnings. Speech synthesis technology generates speech from text to produce natural-sounding voices, providing users with clear and easy-to-understand warnings. The warning unit can also use AI to provide appropriate warnings based on the analysis results. Based on the data provided by the analysis unit, the AI ​​selects information important to the user and issues warnings at the appropriate time. For example, the AI ​​can issue voice warnings if a pedestrian is approaching a dangerous intersection or if a vehicle is rapidly approaching. Furthermore, the AI ​​can learn the user's behavior patterns and past warning history to provide individually optimized warnings. This allows the warning unit to play a crucial role in ensuring user safety and preventing accidents and dangers. In addition, the warning unit can use other warning methods besides voice, such as vibration and light. For example, it can communicate warnings through sight and touch by vibrating the smart glasses frame or flashing LED lights. This allows the warning unit to provide warnings to the user through a variety of means, ensuring that they pay attention.

[0067] The learning unit learns user behavior patterns based on data analyzed by the analysis unit. For example, the learning unit can learn behavior patterns using machine learning techniques. Machine learning techniques are used to train models based on large amounts of data and predict user behavior patterns and preferences. The learning unit can also learn user behavior patterns in real time using AI. The AI ​​analyzes the user's travel and behavior history to learn daily behavior patterns and reactions in specific situations. For example, the AI ​​can learn that a user tends to visit a specific place at a specific time and provide appropriate support based on that information. The AI ​​can also learn user preferences and interests and provide individually customized information and services. This allows the learning unit to provide personalized support tailored to user needs, making daily life more comfortable and convenient. Furthermore, the learning unit can collect user feedback and continuously improve its learning model. For example, by providing feedback on the information and services provided, the AI ​​can update its learning model based on that feedback, providing more accurate predictions and support. This allows the learning unit to continuously learn user behavior patterns and improve the overall system performance.

[0068] The warning unit can alert the user by voice if there is an obstacle while walking. For example, the warning unit provides a voice warning based on the obstacle detection result. The warning unit can also use AI to provide appropriate warnings based on the obstacle detection result. For example, the warning unit ensures user safety by providing a voice alert if there is an obstacle while walking. The warning unit can also provide appropriate warnings depending on the type and location of the obstacle. This ensures user safety by providing a voice alert for obstacles while walking.

[0069] The learning unit can learn user behavior patterns and provide customized support to individual users. For example, the learning unit collects user behavior data and learns behavior patterns. The learning unit can also use AI to learn user behavior patterns in real time. For example, the learning unit provides optimal support to individual users based on their behavior patterns. The learning unit can also use AI to provide customized support based on user behavior patterns. This allows the learning unit to provide optimal support to individual users by learning their behavior patterns.

[0070] The acquisition unit may include a camera mounted on the smart glasses. The acquisition unit, for example, captures the surrounding environment using the camera mounted on the smart glasses. The acquisition unit can also use AI to acquire the surrounding environment in real time using the camera mounted on the smart glasses. The acquisition unit acquires appropriate environmental data according to the resolution and field of view of the camera mounted on the smart glasses, for example. The acquisition unit can also use AI to acquire environmental data by making the most of the performance of the camera mounted on the smart glasses. This makes it possible to effectively acquire the surrounding environment by using the camera mounted on the smart glasses.

[0071] The analysis unit can analyze environmental data in real time. For example, the analysis unit can immediately analyze environmental data acquired in real time. The analysis unit can also use AI to analyze environmental data in real time. For example, by analyzing environmental data in real time, the analysis unit can enable a rapid response. By using AI to analyze environmental data in real time, the analysis unit can also provide more accurate analysis results. This enables a rapid response by analyzing environmental data in real time.

[0072] The acquisition unit can estimate the user's emotions and adjust the camera's acquisition range based on the estimated emotions. For example, the acquisition unit uses facial recognition technology to estimate the user's emotions. The acquisition unit can also use AI to estimate the user's emotions in real time. For example, if the user is tense, the acquisition unit widens the camera's acquisition range to acquire more surrounding information. The acquisition unit can also use AI to dynamically adjust the camera's acquisition range according to the user's emotions. For example, if the user is relaxed, the acquisition unit narrows the camera's acquisition range to acquire only the necessary information. The acquisition unit can also use AI to optimize the camera's acquisition range based on the user's emotions. This allows for the effective acquisition of necessary information by adjusting the camera's acquisition range 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.

[0073] The acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring information about the surrounding environment. For example, the acquisition unit can store the user's past behavior history in a database and refer to it as needed. The acquisition unit can also refer to the user's past behavior history in real time using AI. For example, the acquisition unit can prioritize acquiring routes that the user has frequently taken in the past. The acquisition unit can also select the optimal acquisition method based on the user's past behavior history using AI. For example, the acquisition unit can acquire information needed for a specific time period from the user's past behavior history. The acquisition unit can also analyze the user's past behavior patterns using AI and select the optimal acquisition method. This allows the system to select the optimal acquisition method by referring to the user's past behavior history.

[0074] The data acquisition unit can filter the captured environmental data according to the user's current activity. For example, the data acquisition unit can detect the user's current activity and perform filtering accordingly. The data acquisition unit can also use AI to filter based on the user's current activity. For example, if the user is walking, the data acquisition unit will prioritize acquiring information about obstacles. The data acquisition unit can also use AI to filter necessary information according to the user's current activity. For example, if the user is resting, the data acquisition unit will filter out ambient noise and movement to provide a quiet environment. The data acquisition unit can also use AI to acquire optimal information based on the user's current activity. This allows for the priority acquisition of necessary information by filtering according to the user's current activity.

[0075] The data acquisition unit can estimate the user's emotions and determine the priority of environmental data to acquire based on the estimated user emotions. For example, the data acquisition unit uses voice analysis technology to estimate the user's emotions. The data acquisition unit can also use AI to estimate the user's emotions in real time. For example, if the user is feeling anxious, the data acquisition unit will prioritize acquiring information related to danger. The data acquisition unit can also use AI to dynamically determine the priority of environmental data to acquire based on the user's emotions. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring information related to the surrounding scenery and sounds. The data acquisition unit can also use AI to acquire the most relevant information based on the user's emotions. This allows for the priority acquisition of important information by determining the priority of environmental data 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.

[0076] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information in the environmental data it captures. For example, the data acquisition unit can acquire the user's geographical location information as GPS data. The data acquisition unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the data acquisition unit will prioritize the acquisition of information related to that location. The data acquisition unit can also acquire optimal information based on the user's geographical location information using AI. For example, if the user is on the move, the data acquisition unit will acquire the necessary information based on their current location. The data acquisition unit can also prioritize the acquisition of highly relevant information based on the user's geographical location information using AI. This allows for the priority acquisition of highly relevant information by considering the user's geographical location information.

[0077] The data acquisition unit can analyze the user's social media activity in the captured environmental data and obtain relevant information. For example, the data acquisition unit can store the user's social media activity in a database and refer to it as needed. The data acquisition unit can also use AI to analyze the user's social media activity in real time. For example, the data acquisition unit can prioritize obtaining information about places that the user has shared on social media. The data acquisition unit can also use AI to obtain optimal information based on the user's social media activity. For example, the data acquisition unit can obtain information about topics that the user has shown interest in on social media. The data acquisition unit can also use AI to obtain relevant information based on the user's social media activity. In this way, relevant information can be obtained by analyzing the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit uses facial recognition technology to estimate the user's emotions. The analysis unit can also use AI to estimate the user's emotions in real time. For example, if the user is tense, the analysis unit can increase the accuracy of the analysis to provide more detailed information. The analysis unit can also use AI to dynamically adjust the accuracy of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide only the necessary information. The analysis unit can also use AI to provide optimal analysis accuracy based on the user's emotions. This allows for the effective provision of necessary information by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can apply the optimal analysis algorithm by referring to past analysis results when analyzing environmental data. For example, the analysis unit can store past analysis results in a database and refer to them as needed. The analysis unit can also use AI to refer to past analysis results in real time. For example, the analysis unit can select the optimal algorithm for a specific situation from past analysis results. The analysis unit can also use AI to apply the optimal analysis algorithm based on past analysis results. For example, the analysis unit can dynamically adjust the analysis algorithm based on past analysis results. The analysis unit can also use AI to improve the accuracy of the analysis by referring to past analysis results. This allows the application of the optimal analysis algorithm by referring to past analysis results.

[0080] The analysis unit can apply analysis methods tailored to the user's current activity when analyzing environmental data. For example, the analysis unit can detect the user's current activity and apply an analysis method accordingly. The analysis unit can also use AI to apply analysis methods based on the user's current activity. For example, if the user is walking, the analysis unit can apply an analysis method specifically designed for obstacle detection. The analysis unit can also use AI to apply the most suitable analysis method according to the user's current activity. For example, if the user is resting, the analysis unit can analyze surrounding sounds and movements to provide a quiet environment. The analysis unit can also use AI to analyze necessary information based on the user's current activity. This allows for the effective provision of necessary information by applying analysis methods tailored to the user's current activity.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit uses voice analysis technology to estimate the user's emotions. The analysis unit can also use AI to estimate the user's emotions in real time. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. The analysis unit can also use AI to dynamically adjust the display method based on the user's emotions. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also use AI to provide the optimal display method based on the user's emotions. This allows for the provision of highly visible information 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information when analyzing environmental data. For example, the analysis unit can acquire the user's geographical location information as GPS data. The analysis unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the analysis unit will prioritize analyzing information related to that location. The analysis unit can also use AI to analyze the most relevant information based on the user's geographical location information. For example, if the user is on the move, the analysis unit will analyze the necessary information based on their current location. The analysis unit can also use AI to improve the accuracy of its analysis based on the user's geographical location information. In this way, the accuracy of the analysis can be improved by considering the user's geographical location information.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases when analyzing environmental data. For example, the analysis unit can store relevant literature in a database and refer to it as needed. The analysis unit can also use AI to refer to relevant literature and databases in real time. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can also use AI to apply the optimal analysis algorithm based on relevant literature and databases. For example, the analysis unit can improve the accuracy of its analysis results by referring to databases. The analysis unit can also use AI to dynamically adjust the accuracy of the analysis based on relevant literature and databases. This allows for improved analysis accuracy by referring to relevant literature and databases.

[0084] The warning unit can estimate the user's emotions and adjust the way the warning is delivered based on those emotions. For example, the warning unit may use facial recognition technology to estimate the user's emotions. The warning unit can also use AI to estimate the user's emotions in real time. For example, if the user is tense, the warning unit will issue a warning in a calm voice. The warning unit can also use AI to dynamically adjust the way the warning is delivered based on the user's emotions. For example, if the user is relaxed, the warning unit will issue a warning in a cheerful voice. The warning unit can also use AI to provide the optimal warning method based on the user's emotions. For example, if the user is in a hurry, the warning unit will issue a quick and concise warning. The warning unit can also use AI to optimize the way the warning is delivered based on the user's emotions. This allows for the provision of effective warnings by adjusting the way the warning is delivered 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.

[0085] The warning unit can select the most appropriate warning method by referring to the user's past behavior history when issuing a warning. For example, the warning unit can store the user's past behavior history in a database and refer to it as needed. The warning unit can also use AI to refer to the user's past behavior history in real time. For example, the warning unit can prioritize using warning methods that the user has responded to in the past. The warning unit can also use AI to select the most appropriate warning method based on the user's past behavior history. For example, the warning unit can select the most appropriate warning method for a specific situation from the user's past behavior history. The warning unit can also use AI to analyze the user's past behavior patterns and select the most appropriate warning method. This allows the system to select the most appropriate warning method by referring to the user's past behavior history.

[0086] The warning unit can apply a warning method that is appropriate to the user's current activity when issuing a warning. For example, the warning unit can detect the user's current activity and apply a warning method accordingly. The warning unit can also use AI to apply a warning method based on the user's current activity. For example, if the user is walking, the warning unit will prioritize warnings about obstacles. The warning unit can also use AI to apply the most appropriate warning method according to the user's current activity. For example, if the user is resting, the warning unit will warn about surrounding sounds and movements to provide a quiet environment. The warning unit can also use AI to provide necessary warnings based on the user's current activity. This allows the system to effectively provide necessary warnings by applying a warning method that is appropriate to the user's current activity.

[0087] The warning unit can estimate the user's emotions and determine the priority of warnings based on those emotions. For example, the warning unit may use voice analysis technology to estimate the user's emotions. The warning unit can also use AI to estimate the user's emotions in real time. For example, if the user is feeling anxious, the warning unit will prioritize warnings about danger. The warning unit can also use AI to dynamically determine the priority of warnings based on the user's emotions. For example, if the user is relaxed, the warning unit will prioritize warnings about the surrounding scenery and sounds. The warning unit can also use AI to provide the most appropriate warnings based on the user's emotions. For example, if the user is focused, the warning unit will only provide necessary warnings. The warning unit can also use AI to optimize the priority of warnings based on the user's emotions. This allows for the priority of important warnings by determining the priority of warnings 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.

[0088] The warning unit can select the optimal warning method by considering the user's geographical location when issuing a warning. For example, the warning unit can acquire the user's geographical location as GPS data. The warning unit can also acquire the user's geographical location in real time using AI. For example, if the user is in a specific location, the warning unit will prioritize warnings related to that location. The warning unit can also select the optimal warning method based on the user's geographical location using AI. For example, if the user is on the move, the warning unit will issue necessary warnings based on their current location. The warning unit can also provide optimal warnings based on the user's geographical location using AI. For example, if the user is approaching a destination, the warning unit will prioritize warnings related to the destination. The warning unit can also provide optimal warning methods based on the user's geographical location using AI. This allows the system to select the optimal warning method by considering the user's geographical location.

[0089] The alerting unit can analyze the user's social media activity and issue relevant warnings when issuing alerts. For example, the alerting unit can store the user's social media activity in a database and refer to it as needed. The alerting unit can also use AI to analyze the user's social media activity in real time. For example, the alerting unit can prioritize alerts related to places the user has shared on social media. The alerting unit can also use AI to provide optimal alerts based on the user's social media activity. For example, the alerting unit can issue alerts related to topics the user has shown interest in on social media. The alerting unit can also use AI to issue relevant warnings based on the user's social media activity. For example, the alerting unit can issue alerts based on information shared by the user's social media followers. The alerting unit can also use AI to provide optimal alerts based on the user's social media activity. This allows the system to provide relevant warnings by analyzing the user's social media activity.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit uses speech analysis technology to estimate the user's emotions. The learning unit can also use AI to estimate the user's emotions in real time. For example, if the user is tense, the learning unit prioritizes learning data to help them relax. The learning unit can also use AI to dynamically select training data based on the user's emotions. For example, if the user is relaxed, the learning unit learns data to improve concentration. The learning unit can also use AI to provide optimal training data based on the user's emotions. For example, if the user is in a hurry, the learning unit learns data to help them respond quickly. The learning unit can also use AI to optimize the selection of training data based on the user's emotions. This enables effective learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can store past learning data in a database and refer to it as needed. The learning unit can also use AI to refer to past learning data in real time. For example, the learning unit can select the optimal algorithm for a specific situation from past learning data. The learning unit can also use AI to dynamically optimize the learning algorithm based on past learning data. For example, the learning unit adjusts the learning algorithm based on past learning data. The learning unit can also use AI to improve the accuracy of learning by referring to past learning data. This allows for the optimization of the learning algorithm by referring to past learning data.

[0092] The learning unit can apply learning methods tailored to the user's current activity during learning. For example, the learning unit can detect the user's current activity and apply a learning method accordingly. The learning unit can also use AI to apply learning methods based on the user's current activity. For example, if the user is walking, the learning unit can apply a learning method specifically for obstacle detection. The learning unit can also use AI to apply the optimal learning method according to the user's current activity. For example, if the user is resting, the learning unit can learn surrounding sounds and movements to provide a quiet environment. The learning unit can also use AI to learn necessary information based on the user's current activity. For example, if the user is shopping, the learning unit can learn and provide location information of products. The learning unit can also use AI to provide the optimal learning method based on the user's current activity. This allows the system to effectively learn necessary information by applying learning methods tailored to the user's current activity.

[0093] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit uses voice analysis technology to estimate the user's emotions. The learning unit can also use AI to estimate the user's emotions in real time. For example, if the user is nervous, the learning unit increases the learning frequency to respond quickly. The learning unit can also use AI to dynamically adjust the learning frequency based on the user's emotions. For example, if the user is relaxed, the learning unit adjusts the learning frequency to learn only the necessary information. The learning unit can also use AI to provide the optimal learning frequency based on the user's emotions. For example, if the user is in a hurry, the learning unit dynamically adjusts the learning frequency to prioritize learning important information. The learning unit can also use AI to optimize the learning frequency based on the user's emotions. This allows for effective learning by adjusting the learning frequency 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) and multimodal generation AI.

[0094] The learning unit can weight the training data by considering the user's geographical location information during the learning process. For example, the learning unit can acquire the user's geographical location information as GPS data. The learning unit can also acquire the user's geographical location information in real time using AI. For example, if the user is in a specific location, the learning unit will prioritize learning information related to that location. The learning unit can also dynamically weight the training data based on the user's geographical location information using AI. For example, if the user is on the move, the learning unit will learn the necessary information based on their current location. The learning unit can also provide optimal training data based on the user's geographical location information using AI. For example, if the user is approaching a destination, the learning unit will prioritize learning information related to that destination. The learning unit can also optimize the weighting of the training data based on the user's geographical location information using AI. This allows the training data to be weighted by considering the user's geographical location information.

[0095] The learning unit can improve the accuracy of its learning by referring to relevant literature and databases during the learning process. For example, the learning unit can store relevant literature in a database and refer to it as needed. The learning unit can also use AI to refer to relevant literature and databases in real time. For example, the learning unit can optimize its learning algorithm by referring to relevant literature. The learning unit can also use AI to apply the optimal learning algorithm based on relevant literature and databases. For example, the learning unit can improve the accuracy of its learning results by referring to databases. The learning unit can also use AI to dynamically adjust the accuracy of learning based on relevant literature and databases. This allows the learning accuracy to be improved by referring to relevant literature and databases.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The acquisition unit can monitor the user's health status and adjust the camera's acquisition range according to that status. For example, it can measure the user's heart rate and blood pressure with sensors, and if the user's health status deteriorates, it can widen the camera's acquisition range to acquire more surrounding information. Conversely, if the user's health status is stable, it can narrow the camera's acquisition range to acquire only the necessary information. Furthermore, it can determine the priority of information to acquire according to the user's health status, prioritizing the acquisition of important information. In this way, by adjusting the camera's acquisition range according to the user's health status, necessary information can be acquired effectively.

[0098] 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, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, the system can determine the priority of information to display according to the user's emotions, prioritizing the display of important information. In this way, by adjusting the display method of the analysis results according to the user's emotions, highly visible information can be provided.

[0099] The warning unit can select the most appropriate warning method by referring to the user's past behavior history. For example, it can prioritize the use of warning methods that the user has responded to in the past. It can also select the most appropriate warning method for a specific situation based on the user's past behavior history. Furthermore, it can analyze the user's past behavior patterns to select the most appropriate warning method. In this way, the system can select the most appropriate warning method by referring to the user's past behavior history.

[0100] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is tense, it can prioritize learning data that promotes relaxation. Conversely, if the user is relaxed, it can learn data that enhances concentration. Furthermore, it can dynamically select training data according to the user's emotions. This allows for effective learning by selecting training data according to the user's emotions.

[0101] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location. For example, by acquiring the user's geographical location as GPS data, if the user is in a specific location, it can prioritize the acquisition of information related to that location. Furthermore, if the user is on the move, it can acquire necessary information based on their current location. It can also acquire the most relevant information based on the user's geographical location. In this way, by considering the user's geographical location, it can prioritize the acquisition of highly relevant information.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the accuracy of the analysis can be increased to provide more detailed information. Conversely, if the user is relaxed, the accuracy of the analysis can be adjusted to provide only the necessary information. Furthermore, the accuracy of the analysis can be dynamically adjusted based on the user's emotions. This allows for the effective provision of necessary information by adjusting the accuracy of the analysis according to the user's emotions.

[0103] The warning unit can apply warning methods tailored to the user's current activity. For example, if the user is walking, it can prioritize warnings about obstacles. If the user is resting, it can provide a quiet environment by warning about surrounding sounds and movements. Furthermore, it can apply the most appropriate warning method based on the user's current activity. This allows for the effective provision of necessary warnings by applying warning methods tailored to the user's current activity.

[0104] The learning unit can estimate the user's emotions and adjust the learning frequency based on those emotions. For example, if the user is stressed, the learning frequency can be increased to respond quickly. Conversely, if the user is relaxed, the learning frequency can be adjusted to learn only the necessary information. Furthermore, the learning frequency can be dynamically adjusted based on the user's emotions. This allows for effective learning by adjusting the learning frequency according to the user's emotions.

[0105] The acquisition unit can analyze a user's social media activity and retrieve relevant information. For example, it can store a user's social media activity in a database and refer to it as needed. It can also prioritize the acquisition of information about locations shared by the user on social media. Furthermore, it can acquire optimal information based on the user's social media activity. In this way, relevant information can be obtained by analyzing a user's social media activity.

[0106] The warning unit can estimate the user's emotions and prioritize warnings based on those emotions. For example, if the user is feeling anxious, it can prioritize warnings about danger. Conversely, if the user is relaxed, it can prioritize warnings about the surrounding scenery and sounds. Furthermore, it can dynamically determine warning priorities based on the user's emotions. This allows for the priority delivery of important warnings according to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The acquisition unit captures the surrounding environment with a camera. The acquisition unit can capture the surrounding environment using, for example, a camera mounted on smart glasses. The acquisition unit can also acquire the surrounding environment in real time using AI. Step 2: The analysis unit analyzes the environmental data acquired by the acquisition unit. The analysis unit can analyze the environmental data using, for example, image analysis technology. The analysis unit can also analyze the environmental data in real time using AI. Step 3: The warning unit issues an audible warning based on the data analyzed by the analysis unit. The warning unit can issue warnings using, for example, speech synthesis technology. The warning unit can also use AI to issue appropriate warnings based on the analysis results. Step 4: The learning unit learns user behavior patterns based on the data analyzed by the analysis unit. The learning unit can learn behavior patterns using, for example, machine learning techniques. The learning unit can also learn user behavior patterns in real time using AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the acquisition unit, analysis unit, warning unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can capture the surrounding environment using the camera 42 mounted on the smart device 14. The analysis unit can analyze environmental data in real time using the specific processing unit 290 of the data processing unit 12. The warning unit can issue voice warnings using the speaker 40B of the smart device 14. The learning unit can learn the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the acquisition unit, analysis unit, warning unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can capture the surrounding environment using the camera 42 mounted on the smart glasses 214. The analysis unit can analyze environmental data in real time using, for example, the specific processing unit 290 of the data processing unit 12. The warning unit can provide voice warnings using, for example, the speaker 240 of the smart glasses 214. The learning unit can learn the user's behavior patterns using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the acquisition unit, analysis unit, warning unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can capture the surrounding environment using the camera 42 mounted on the headset terminal 314. The analysis unit can analyze environmental data in real time using the specific processing unit 290 of the data processing unit 12. The warning unit can issue voice warnings using the speaker 240 of the headset terminal 314. The learning unit can learn the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the acquisition unit, analysis unit, warning unit, and learning unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can capture the surrounding environment using a camera 42 mounted on the robot 414. The analysis unit can analyze environmental data in real time using, for example, the specific processing unit 290 of the data processing unit 12. The warning unit can issue voice warnings using, for example, the speaker 240 of the robot 414. The learning unit can learn the user's behavior patterns using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The acquisition unit captures the surrounding environment with a camera, An analysis unit analyzes the environmental data acquired by the acquisition unit, A warning unit that issues an audible warning based on the data analyzed by the aforementioned analysis unit, The system includes a learning unit that learns user behavior patterns based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned warning unit is If there is an obstacle while walking, it will alert you to its presence with a voice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Learn user behavior patterns and provide customized support for individual users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Including the camera built into the smart glasses The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze environmental data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the camera's acquisition range based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring information about the surrounding environment, the system selects the optimal acquisition method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Based on the captured environmental data, filtering is performed according to the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of environmental data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system prioritizes acquiring highly relevant information by considering the user's geographical location when capturing environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, By analyzing users' social media activity in the captured environmental data, we obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing environmental data, we apply the most suitable analysis algorithm by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing environmental data, apply analysis methods that are appropriate to the user's current activities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing environmental data, consider the user's geographical location to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing environmental data, referencing relevant literature and databases improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is When issuing a warning, the system selects the most appropriate warning method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When issuing a warning, apply a warning method that is appropriate to the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When issuing a warning, the system selects the most appropriate warning method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, the system analyzes the user's social media activity and issues relevant warnings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, When performing training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, When performing learning, apply a learning method that is tailored to the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, When performing training, the training data is weighted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, When performing learning, refer to relevant literature and databases to improve the accuracy of the learning process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit captures the surrounding environment with a camera, An analysis unit analyzes the environmental data acquired by the acquisition unit, A warning unit that issues an audible warning based on the data analyzed by the aforementioned analysis unit, The system includes a learning unit that learns user behavior patterns based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned warning unit is If there is an obstacle while walking, it will alert you to its presence with a voice. The system according to feature 1.

3. The aforementioned learning unit, Learn user behavior patterns and provide customized support for individual users. The system according to feature 1.

4. The acquisition unit is, Including the camera built into the smart glasses The system according to feature 1.

5. The aforementioned analysis unit, Analyze environmental data in real time. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the camera's acquisition range based on those emotions. The system according to feature 1.

7. The acquisition unit is, When acquiring information about the surrounding environment, the system selects the optimal acquisition method by referring to the user's past behavior history. The system according to feature 1.

8. The acquisition unit is, Based on the captured environmental data, filtering is performed according to the user's current activity. The system according to feature 1.

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