system

Abstract

The system according to the embodiment aims to recognize people and objects in the surrounding area and provide navigation by voice. [Solution] A system according to an embodiment includes a recognition unit, an analysis unit, and a navigation unit. The recognition unit recognizes people and objects in the vicinity. The analysis unit analyzes the information recognized by the recognition unit. The navigation unit provides audio navigation based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to recognize people and objects in the surrounding area and navigate appropriately.

[0005] The system according to the embodiment aims to recognize people and objects in the surrounding area and provide navigation by voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, an analysis unit, and a navigation unit. The recognition unit recognizes people and objects in the vicinity. The analysis unit analyzes the information recognized by the recognition unit. The navigation unit provides audio navigation based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize people and objects in the surrounding area and provide navigation by voice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The audio glasses system according to an embodiment of the present invention incorporates an AI camera built into the audio glasses. The camera automatically analyzes people and objects recognized by the lenses and provides voice navigation. In this audio glasses system, the AI ​​camera built into the audio glasses first recognizes people and objects in the vicinity. The AI ​​then analyzes the information recognized by the AI ​​and provides voice navigation to the user. For example, if a user is searching for a specific person, the AI ​​camera recognizes the person and provides voice guidance to the person's location. Similarly, if a user is searching for a specific object, the AI ​​camera recognizes the object and provides voice guidance. This system allows users to grasp information about their surroundings without relying on their vision, making it extremely useful for the visually impaired and elderly. For example, the AI ​​camera includes a "recognition unit" that recognizes people and objects in the vicinity, and an "analysis unit" that analyzes the recognition results. Next, a "navigation unit" provides voice navigation based on the analysis results. These components are interrelated. The recognition unit uses face recognition and object recognition technology, while the analysis unit analyzes data from the recognition unit. The navigation unit provides voice navigation based on the data from the analysis unit. It is important to include specific details of voice navigation, such as the type of voice, the voice output method, and the voice customization function. This makes the audio glasses system extremely useful for visually impaired people and the elderly, as it allows them to grasp information about their surroundings without relying on their vision.

[0029] The audio glasses system according to the embodiment includes a recognition unit, an analysis unit, and a navigation unit. The recognition unit recognizes people and objects in the vicinity. The recognition unit uses, for example, face recognition technology and object recognition technology. The face recognition technology is realized using technologies such as deep learning and pattern matching. The object recognition technology is realized using technologies such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). For example, the recognition unit can recognize faces using deep learning. The recognition unit can also recognize objects using YOLO. Furthermore, the recognition unit can recognize specific patterns using pattern matching. The analysis unit analyzes the information recognized by the recognition unit. The analysis unit performs analysis based on, for example, the type of data and an analysis algorithm. The analysis algorithm includes, for example, a machine learning algorithm and a statistical analysis algorithm. For example, the analysis unit can analyze data using a machine learning algorithm. Furthermore, the analysis unit can analyze data using a statistical analysis algorithm. Furthermore, the analysis unit can analyze data using data mining technology. The navigation unit performs voice navigation based on the information analyzed by the analysis unit. The navigation unit includes, for example, a voice type, a voice output method, and a voice customization function. Voice types include, for example, male voices, female voices, robot voices, etc. Voice output methods include, for example, speakers, earphones, and bone conduction. Voice customization functions include, for example, volume adjustment, voice speed adjustment, and voice tone change. For example, the navigation unit can perform voice navigation using a male voice. The navigation unit can also output voice using a speaker. Furthermore, the navigation unit can output voice by adjusting the volume. As a result, the audio glasses system according to the embodiment can recognize and analyze surrounding people and objects and provide voice navigation.

[0030] The recognition unit can use face recognition or object recognition technology. Face recognition is achieved using technologies such as deep learning and pattern matching. Deep learning uses large amounts of data to train a model and achieve high-precision face recognition. For example, face recognition using deep learning extracts facial feature points and recognizes faces based on them. Pattern matching compares pre-registered face patterns with patterns in an input image to determine whether they match. For example, face recognition using pattern matching recognizes faces based on facial contours and features such as the eyes, nose, and mouth. Object recognition is achieved using technologies such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). YOLO processes the entire image at once and simultaneously predicts the location and class of an object. For example, object recognition using YOLO can quickly recognize multiple objects in an image. SSD divides an image into multiple grids and predicts the location and class of an object for each grid. For example, object recognition technology using SSD achieves high-speed, high-accuracy object recognition. Faster R-CNN is a technology that combines a region proposal network (RPN), which proposes the location of an object in an image, with a classification network, which predicts the object class. For example, object recognition technology using Faster R-CNN achieves high-accuracy object recognition. This improves recognition accuracy when using face recognition technology or object recognition technology.

[0031] The navigation unit may include a voice type, a voice output method, and a voice customization function. Voice types include, for example, a male voice, a female voice, a robot voice, etc. For example, the navigation unit can provide voice navigation using a male voice. The navigation unit can also provide voice navigation using a female voice. The navigation unit can also provide voice navigation using a robot voice. Voice output methods include, for example, a speaker, earphones, bone conduction, etc. For example, the navigation unit can output voice using a speaker. The navigation unit can also output voice using earphones. The navigation unit can also output voice using bone conduction. Voice customization functions include, for example, volume adjustment, voice speed adjustment, and voice tone change. For example, the navigation unit can output voice by adjusting the volume. The navigation unit can also output voice by adjusting the voice speed. The navigation unit can also output voice by changing the voice tone. Thus, by including voice types, output methods, and customization functions, navigation suited to the user is possible.

[0032] The recognition unit can switch recognition algorithms depending on specific environmental conditions. The recognition unit switches recognition algorithms depending on specific environmental conditions, such as dark places or backlight. In dark places, recognition accuracy is improved using an infrared camera. For example, the recognition unit can use an infrared camera to improve recognition accuracy in dark places. In backlight, recognition accuracy is improved using high dynamic range (HDR) technology. For example, the recognition unit can use HDR technology to improve recognition accuracy in backlight. In rainy weather, recognition accuracy is maintained using a filter that removes water droplets. For example, the recognition unit can maintain recognition accuracy by using a filter that removes water droplets in rainy weather. In this way, recognition accuracy can be maintained by switching recognition algorithms depending on environmental conditions. For example, the recognition unit can use an infrared camera to improve recognition accuracy in dark places. Furthermore, the recognition unit can use HDR technology to improve recognition accuracy in backlight. Furthermore, the recognition unit can maintain recognition accuracy by using a filter that removes water droplets in rainy weather.

[0033] The recognition unit can determine the recognition priority by referring to the user's past recognition history. The recognition unit can determine the recognition priority by referring to, for example, the user's past recognition history. The past recognition history includes information such as date, time, location, and recognition target. For example, the recognition unit can prioritize recognizing people that the user has frequently recognized in the past. The recognition unit can also prioritize recognizing objects that the user has recognized in the past. Furthermore, the recognition unit can predict targets to be recognized in a specific time period from the user's past recognition history and determine the priority. In this way, by referring to the past recognition history, important targets can be recognized preferentially. For example, the recognition unit can prioritize recognizing people that the user has frequently recognized in the past. The recognition unit can also prioritize recognizing objects that the user has recognized in the past. Furthermore, the recognition unit can predict targets to be recognized in a specific time period from the user's past recognition history and determine the priority.

[0034] The recognition unit can filter the recognition targets based on the user's geographical location information. The recognition unit filters the recognition targets, for example, taking into account the user's geographical location information. The geographical location information is acquired using GPS data, Wi-Fi location information, beacons, etc. For example, when the user is in a specific location, the recognition unit can prioritize recognizing people and objects related to that location. Furthermore, when the user is moving, the recognition unit can prioritize recognizing people and objects related to the destination. Furthermore, when the user is in a specific area, the recognition unit can filter and provide information related to the area. In this way, by taking the geographical location information into consideration, related targets can be prioritized. For example, when the user is in a specific location, the recognition unit can prioritize recognizing people and objects related to that location. Furthermore, when the user is moving, the recognition unit can prioritize recognizing people and objects related to the destination. Furthermore, when the user is in a specific area, the recognition unit can filter and provide information related to the area.

[0035] The recognition unit can analyze a user's social media activity and prioritize recognition of related objects. The recognition unit, for example, analyzes a user's social media activity and prioritize recognition of related objects. Social media activity includes information such as the content of posts, the number of likes, and the number of followers. For example, the recognition unit can prioritize recognition of people with whom the user frequently interacts on social media. The recognition unit can also prioritize recognition of objects in which the user has shown interest on social media. Furthermore, the recognition unit can prioritize recognition of people and objects related to a specific event from the user's social media activity. In this way, related objects can be prioritized by analyzing social media activity. For example, the recognition unit can prioritize recognition of people with whom the user frequently interacts on social media. The recognition unit can also prioritize recognition of objects in which the user has shown interest on social media. Furthermore, the recognition unit can prioritize recognition of people and objects related to a specific event from the user's social media activity.

[0036] The analysis unit can analyze the data from the recognition unit in real time and provide immediate feedback. The analysis unit, for example, analyzes the data from the recognition unit in real time and provides immediate feedback. The real-time analysis includes criteria such as the data update frequency and delay time. For example, the analysis unit can analyze the data from the recognition unit in real time and provide immediate audio feedback. The analysis unit can also analyze the data from the recognition unit in real time and provide immediate visual feedback. Furthermore, the analysis unit can analyze the data from the recognition unit in real time and provide immediate haptic feedback. This enables a rapid response by analyzing in real time and providing immediate feedback. For example, the analysis unit can analyze the data from the recognition unit in real time and provide immediate audio feedback. The analysis unit can also analyze the data from the recognition unit in real time and provide immediate visual feedback. Furthermore, the analysis unit can analyze the data from the recognition unit in real time and provide immediate haptic feedback.

[0037] The analysis unit can perform analysis based on attribute information of the recognition target. The analysis unit performs analysis taking into account, for example, the attribute information of the recognition target. The attribute information includes information such as age, gender, and occupation. For example, the analysis unit can provide an appropriate analysis result taking into account the age of the recognition target. The analysis unit can also provide an appropriate analysis result taking into account the gender of the recognition target. Furthermore, the analysis unit can also provide an appropriate analysis result taking into account the occupation of the recognition target. In this way, an appropriate analysis result can be provided by taking into account the attribute information. For example, the analysis unit can provide an appropriate analysis result taking into account the age of the recognition target. The analysis unit can also provide an appropriate analysis result taking into account the gender of the recognition target. Furthermore, the analysis unit can provide an appropriate analysis result taking into account the occupation of the recognition target.

[0038] The analysis unit can improve the accuracy of analysis by referring to the user's past analysis history. The analysis unit can improve the accuracy of analysis by referring to, for example, the user's past analysis history. The past analysis history includes information such as date, time, location, and analysis target. For example, the analysis unit can improve the analysis accuracy in similar situations by referring to the user's past analysis history. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis history and learning specific patterns. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis history. In this way, the analysis accuracy is improved by referring to the past analysis history. For example, the analysis unit can improve the analysis accuracy in similar situations by referring to the user's past analysis history. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis history and learning specific patterns. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis history.

[0039] The analysis unit can customize the analysis results based on the user's current activity status. The analysis unit customizes the analysis results, for example, by taking into account the user's current activity status. The current activity status includes information such as whether the user is moving, resting, or working. For example, the analysis unit can provide concise and quick analysis results when the user is walking. The analysis unit can also provide detailed analysis results when the user is sitting. Furthermore, the analysis unit can provide analysis results at an appropriate time when the user is exercising. In this way, the analysis results can be provided at an appropriate time by taking into account the current activity status. For example, the analysis unit can provide concise and quick analysis results when the user is walking. The analysis unit can also provide detailed analysis results when the user is sitting. Furthermore, the analysis unit can provide analysis results at an appropriate time when the user is exercising.

[0040] The navigation unit can refer to the user's past navigation history and provide an optimal navigation route. The navigation unit can, for example, refer to the user's past navigation history and provide an optimal navigation route. The past navigation history includes information such as date and time, route, and destination. For example, the navigation unit can refer to the user's past navigation history and suggest an optimal route. The navigation unit can also refer to the user's past navigation history and suggest a route that avoids congestion. Furthermore, the navigation unit can also refer to the user's past navigation history and suggest the most efficient route. In this way, the optimal route can be provided by referring to the past navigation history. For example, the navigation unit can refer to the user's past navigation history and suggest an optimal route. The navigation unit can also refer to the user's past navigation history and suggest a route that avoids congestion. Furthermore, the navigation unit can refer to the user's past navigation history and suggest the most efficient route.

[0041] The navigation unit can switch the audio output method according to specific environmental conditions. The navigation unit switches the audio output method according to specific environmental conditions, such as noise levels. Specific environmental conditions include brightness, weather, noise levels, and the like. For example, the navigation unit can increase the volume of the audio when providing guidance in noisy places. The navigation unit can also decrease the volume when providing guidance in quiet places. Furthermore, the navigation unit can optimize the audio output method when headphones are used. This enables appropriate navigation by switching the audio output method according to environmental conditions. For example, the navigation unit can increase the volume of the audio when providing guidance in noisy places. The navigation unit can also decrease the volume when providing guidance in quiet places. Furthermore, the navigation unit can optimize the audio output method when headphones are used.

[0042] The navigation unit can provide an optimal navigation route based on the user's geographical location information. The navigation unit can provide the optimal navigation route by taking into account, for example, the user's geographical location information. The geographical location information is obtained using GPS data, Wi-Fi location information, beacons, etc. For example, when the user is in a specific location, the navigation unit can provide an optimal route related to the location. Also, when the user is moving, the navigation unit can provide an optimal route related to a destination. Furthermore, when the user is in a specific area, the navigation unit can provide information related to the area. In this way, the optimal route can be provided by taking into account the geographical location information. For example, when the user is in a specific location, the navigation unit can provide an optimal route related to the location. Also, when the user is moving, the navigation unit can provide an optimal route related to a destination. Furthermore, when the user is in a specific area, the navigation unit can provide information related to the area.

[0043] The navigation unit can analyze the user's social media activity and provide related navigation information. The navigation unit can, for example, analyze the user's social media activity and provide related navigation information. Social media activity includes information such as the content of posts, the number of likes, and the number of followers. For example, the navigation unit can prioritize guidance to places where the user frequently interacts on social media. The navigation unit can also prioritize guidance to places in which the user has shown interest on social media. Furthermore, the navigation unit can prioritize guidance to places related to a specific event based on the user's social media activity. In this way, related navigation information can be provided by analyzing the social media activity. For example, the navigation unit can prioritize guidance to places where the user frequently interacts on social media. The navigation unit can also prioritize guidance to places in which the user has shown interest on social media. Furthermore, the navigation unit can prioritize guidance to places related to a specific event based on the user's social media activity.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The audio glasses system can further include a health management unit that monitors the user's health condition. The health management unit acquires biometric data such as heart rate, blood pressure, and body temperature, and monitors the user's health condition in real time. For example, the health management unit can issue a warning if the heart rate is abnormally high. Also, if the blood pressure is high, the health management unit can provide audio guidance to help the user relax. Furthermore, if the body temperature is high, the health management unit can encourage the user to take a rest. This allows the system to constantly monitor the user's health condition and provide appropriate advice.

[0046] The audio glasses system may further include a behavioral learning unit that learns the user's behavioral patterns. The behavioral learning unit records the user's daily behavior and analyzes the patterns. For example, if the user takes a walk at the same time every morning, health advice can be provided based on that time. Also, if the user frequently visits a particular place, information related to that place can be provided. Furthermore, if the user engages in a particular activity at a particular time of day, information related to that activity can be provided in advance. This enables more personalized information to be provided based on the user's behavioral patterns.

[0047] The audio glasses system may further include an environmental sound analysis unit that analyzes environmental sounds around the user and provides appropriate audio feedback. The environmental sound analysis unit analyzes the ambient noise level and type of sound and provides audio feedback accordingly. For example, if the surroundings are very noisy, the volume of the audio feedback can be automatically increased. Also, if the surroundings are quiet, the volume of the audio feedback can be reduced. Furthermore, if a specific sound (e.g., an ambulance siren) is heard, information about that sound can be provided. This makes it possible to provide appropriate audio feedback according to the surrounding environment.

[0048] The audio glasses system may further include an exercise advice unit that acquires the user's exercise data and provides exercise advice. The exercise advice unit acquires exercise data such as the user's number of steps, distance traveled, and calories burned, and provides appropriate exercise advice. For example, if the user has reached a certain number of steps, the exercise advice unit may encourage the user to take a rest. If the user has not reached a target amount of exercise, the exercise advice unit may suggest additional exercise. Furthermore, if the user is exercising excessively, the exercise advice unit may advise the user to moderate their exercise. In this way, appropriate exercise advice can be provided based on the user's exercise data.

[0049] The audio glasses system may further include a dietary advice unit that acquires the user's dietary data and provides dietary advice. The dietary advice unit acquires data such as the user's dietary content, calorie intake, and nutritional balance, and provides appropriate dietary advice. For example, if the user is consuming excessive calories, the unit may advise the user to reduce their calories. Also, if the user is lacking in nutritional balance, the unit may suggest a balanced diet. Furthermore, if the user is lacking in a specific nutrient, the unit may suggest ingredients containing that nutrient. This makes it possible to provide appropriate dietary advice based on the user's dietary data.

[0050] The audio glasses system may further include a sleep advice unit that acquires the user's sleep data and provides sleep advice. The sleep advice unit acquires data such as the user's sleep time, sleep quality, and number of times they turn over in their sleep, and provides appropriate sleep advice. For example, if the user is not getting enough sleep, it may advise them to go to bed earlier. Also, if the user is not getting deep sleep, it may suggest ways to relax. Furthermore, if the user turns over a lot in their sleep, it may suggest that they review their bedding. In this way, it is possible to provide appropriate sleep advice based on the user's sleep data.

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

[0052] Step 1: The recognition unit recognizes people and objects in the vicinity. The recognition unit uses, for example, face recognition technology or object recognition technology. Face recognition technology is realized using technologies such as deep learning and pattern matching. Object recognition technology is realized using technologies such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). For example, the recognition unit can recognize faces using deep learning. The recognition unit can also recognize objects using YOLO. Furthermore, the recognition unit can recognize specific patterns using pattern matching. Step 2: The analysis unit analyzes the information recognized by the recognition unit. The analysis unit performs the analysis based on, for example, the type of data and the analysis algorithm. The analysis algorithm includes, for example, a machine learning algorithm and a statistical analysis algorithm. For example, the analysis unit can analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using a statistical analysis algorithm. Furthermore, the analysis unit can analyze the data using data mining technology. Step 3: The navigation unit performs voice navigation based on the information analyzed by the analysis unit. The navigation unit includes, for example, voice type, voice output method, and voice customization function. Voice types include, for example, male voice, female voice, robot voice, etc. Voice output methods include, for example, speaker, earphone, bone conduction, etc. Voice customization functions include, for example, volume adjustment, voice speed adjustment, voice tone change, etc. For example, the navigation unit can perform voice navigation using a male voice. The navigation unit can also output voice using a speaker. Furthermore, the navigation unit can output voice with the volume adjusted.

[0053] (Example 2) The audio glasses system according to an embodiment of the present invention incorporates an AI camera built into the audio glasses. The camera automatically analyzes people and objects recognized by the lenses and provides voice navigation. In this audio glasses system, the AI ​​camera built into the audio glasses first recognizes people and objects in the vicinity. The AI ​​then analyzes the information recognized by the AI ​​and provides voice navigation to the user. For example, if a user is searching for a specific person, the AI ​​camera recognizes the person and provides voice guidance to the person's location. Similarly, if a user is searching for a specific object, the AI ​​camera recognizes the object and provides voice guidance. This system allows users to grasp information about their surroundings without relying on their vision, making it extremely useful for the visually impaired and elderly. For example, the AI ​​camera includes a "recognition unit" that recognizes people and objects in the vicinity, and an "analysis unit" that analyzes the recognition results. Next, a "navigation unit" provides voice navigation based on the analysis results. These components are interrelated. The recognition unit uses face recognition and object recognition technology, while the analysis unit analyzes data from the recognition unit. The navigation unit provides voice navigation based on the data from the analysis unit. It is important to include specific details of voice navigation, such as the type of voice, the voice output method, and the voice customization function. This makes the audio glasses system extremely useful for visually impaired people and the elderly, as it allows them to grasp information about their surroundings without relying on their vision.

[0054] The audio glasses system according to the embodiment includes a recognition unit, an analysis unit, and a navigation unit. The recognition unit recognizes people and objects in the vicinity. The recognition unit uses, for example, face recognition technology and object recognition technology. The face recognition technology is realized using technologies such as deep learning and pattern matching. The object recognition technology is realized using technologies such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). For example, the recognition unit can recognize faces using deep learning. The recognition unit can also recognize objects using YOLO. Furthermore, the recognition unit can recognize specific patterns using pattern matching. The analysis unit analyzes the information recognized by the recognition unit. The analysis unit performs analysis based on, for example, the type of data and an analysis algorithm. The analysis algorithm includes, for example, a machine learning algorithm and a statistical analysis algorithm. For example, the analysis unit can analyze data using a machine learning algorithm. Furthermore, the analysis unit can analyze data using a statistical analysis algorithm. Furthermore, the analysis unit can analyze data using data mining technology. The navigation unit performs voice navigation based on the information analyzed by the analysis unit. The navigation unit includes, for example, a voice type, a voice output method, and a voice customization function. Voice types include, for example, male voices, female voices, robot voices, etc. Voice output methods include, for example, speakers, earphones, and bone conduction. Voice customization functions include, for example, volume adjustment, voice speed adjustment, and voice tone change. For example, the navigation unit can perform voice navigation using a male voice. The navigation unit can also output voice using a speaker. Furthermore, the navigation unit can output voice by adjusting the volume. As a result, the audio glasses system according to the embodiment can recognize and analyze surrounding people and objects and provide voice navigation.

[0055] The recognition unit can use face recognition or object recognition technology. Face recognition is achieved using technologies such as deep learning and pattern matching. Deep learning uses large amounts of data to train a model and achieve high-precision face recognition. For example, face recognition using deep learning extracts facial feature points and recognizes faces based on them. Pattern matching compares pre-registered face patterns with patterns in an input image to determine whether they match. For example, face recognition using pattern matching recognizes faces based on facial contours and features such as the eyes, nose, and mouth. Object recognition is achieved using technologies such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). YOLO processes the entire image at once and simultaneously predicts the location and class of an object. For example, object recognition using YOLO can quickly recognize multiple objects in an image. SSD divides an image into multiple grids and predicts the location and class of an object for each grid. For example, object recognition technology using SSD achieves high-speed, high-accuracy object recognition. Faster R-CNN is a technology that combines a region proposal network (RPN), which proposes the location of an object in an image, with a classification network, which predicts the object class. For example, object recognition technology using Faster R-CNN achieves high-accuracy object recognition. This improves recognition accuracy when using face recognition technology or object recognition technology.

[0056] The navigation unit may include a voice type, a voice output method, and a voice customization function. Voice types include, for example, a male voice, a female voice, a robot voice, etc. For example, the navigation unit can provide voice navigation using a male voice. The navigation unit can also provide voice navigation using a female voice. The navigation unit can also provide voice navigation using a robot voice. Voice output methods include, for example, a speaker, earphones, bone conduction, etc. For example, the navigation unit can output voice using a speaker. The navigation unit can also output voice using earphones. The navigation unit can also output voice using bone conduction. Voice customization functions include, for example, volume adjustment, voice speed adjustment, and voice tone change. For example, the navigation unit can output voice by adjusting the volume. The navigation unit can also output voice by adjusting the voice speed. The navigation unit can also output voice by changing the voice tone. Thus, by including voice types, output methods, and customization functions, navigation suited to the user is possible.

[0057] The recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. The recognition unit, for example, uses facial expression recognition technology to estimate the user's emotion. The facial expression recognition technology analyzes facial expressions to estimate the user's emotion. For example, the recognition unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The recognition unit can also estimate the user's emotion using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. For example, the recognition unit records the user's voice with a microphone and estimates the emotion using a voice analysis algorithm. The recognition unit can also estimate the user's emotion using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotion. For example, the recognition unit measures the user's heart rate with a sensor and estimates the emotion using a biometric algorithm. This allows the recognition accuracy to be adjusted according to the user's emotion, thereby reducing erroneous recognition. For example, if the user is nervous, the recognition accuracy can be increased to reduce false positives, and if the user is relaxed, the recognition accuracy can be returned to normal to improve processing speed, and if the user is in a hurry, the recognition accuracy can be increased to provide quick results.

[0058] The recognition unit can switch recognition algorithms depending on specific environmental conditions. The recognition unit switches recognition algorithms depending on specific environmental conditions, such as dark places or backlight. In dark places, recognition accuracy is improved using an infrared camera. For example, the recognition unit can use an infrared camera to improve recognition accuracy in dark places. In backlight, recognition accuracy is improved using high dynamic range (HDR) technology. For example, the recognition unit can use HDR technology to improve recognition accuracy in backlight. In rainy weather, recognition accuracy is maintained using a filter that removes water droplets. For example, the recognition unit can maintain recognition accuracy by using a filter that removes water droplets in rainy weather. In this way, recognition accuracy can be maintained by switching recognition algorithms depending on environmental conditions. For example, the recognition unit can use an infrared camera to improve recognition accuracy in dark places. Furthermore, the recognition unit can use HDR technology to improve recognition accuracy in backlight. Furthermore, the recognition unit can maintain recognition accuracy by using a filter that removes water droplets in rainy weather.

[0059] The recognition unit can determine the recognition priority by referring to the user's past recognition history. The recognition unit can determine the recognition priority by referring to, for example, the user's past recognition history. The past recognition history includes information such as date, time, location, and recognition target. For example, the recognition unit can prioritize recognizing people that the user has frequently recognized in the past. The recognition unit can also prioritize recognizing objects that the user has recognized in the past. Furthermore, the recognition unit can predict targets to be recognized in a specific time period from the user's past recognition history and determine the priority. In this way, by referring to the past recognition history, important targets can be recognized preferentially. For example, the recognition unit can prioritize recognizing people that the user has frequently recognized in the past. The recognition unit can also prioritize recognizing objects that the user has recognized in the past. Furthermore, the recognition unit can predict targets to be recognized in a specific time period from the user's past recognition history and determine the priority.

[0060] The recognition unit can estimate the user's emotion and determine the priority of recognition targets based on the estimated user's emotion. The recognition unit, for example, uses facial expression recognition technology to estimate the user's emotion. The facial expression recognition technology analyzes facial expressions to estimate the user's emotion. For example, the recognition unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The recognition unit can also estimate the user's emotion using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. For example, the recognition unit records the user's voice with a microphone and estimates the emotion using a voice analysis algorithm. The recognition unit can also estimate the user's emotion using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotion. For example, the recognition unit measures the user's heart rate with a sensor and estimates the emotion using a biometric algorithm. This allows the recognition unit to determine the priority of recognition targets according to the user's emotion, thereby quickly recognizing important targets. For example, if the user is nervous, important people and objects can be recognized first. If the user is relaxed, recognition can be performed with normal priority. Furthermore, if the user is in a hurry, people and objects related to the destination can be recognized first.

[0061] The recognition unit can filter the recognition targets based on the user's geographical location information. The recognition unit filters the recognition targets, for example, taking into account the user's geographical location information. The geographical location information is acquired using GPS data, Wi-Fi location information, beacons, etc. For example, when the user is in a specific location, the recognition unit can prioritize recognizing people and objects related to that location. Furthermore, when the user is moving, the recognition unit can prioritize recognizing people and objects related to the destination. Furthermore, when the user is in a specific area, the recognition unit can filter and provide information related to the area. In this way, by taking the geographical location information into consideration, related targets can be prioritized. For example, when the user is in a specific location, the recognition unit can prioritize recognizing people and objects related to that location. Furthermore, when the user is moving, the recognition unit can prioritize recognizing people and objects related to the destination. Furthermore, when the user is in a specific area, the recognition unit can filter and provide information related to the area.

[0062] The recognition unit can analyze a user's social media activity and prioritize recognition of related objects. The recognition unit, for example, analyzes a user's social media activity and prioritize recognition of related objects. Social media activity includes information such as the content of posts, the number of likes, and the number of followers. For example, the recognition unit can prioritize recognition of people with whom the user frequently interacts on social media. The recognition unit can also prioritize recognition of objects in which the user has shown interest on social media. Furthermore, the recognition unit can prioritize recognition of people and objects related to a specific event from the user's social media activity. In this way, related objects can be prioritized by analyzing social media activity. For example, the recognition unit can prioritize recognition of people with whom the user frequently interacts on social media. The recognition unit can also prioritize recognition of objects in which the user has shown interest on social media. Furthermore, the recognition unit can prioritize recognition of people and objects related to a specific event from the user's social media activity.

[0063] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. The facial expression recognition technology analyzes facial expressions to estimate the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. For example, the analysis unit records the user's voice with a microphone and estimates the emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotion. For example, the analysis unit measures the user's heart rate with a sensor and estimates the emotion using a biometric algorithm. This makes it possible to provide appropriate information by adjusting the presentation method of the analysis result according to the user's emotion. For example, if the user is nervous, the system can provide a concise and clear analysis result, if the user is relaxed, the system can provide a detailed analysis result, and if the user is in a hurry, the system can provide a summary analysis result.

[0064] The analysis unit can analyze the data from the recognition unit in real time and provide immediate feedback. The analysis unit, for example, analyzes the data from the recognition unit in real time and provides immediate feedback. The real-time analysis includes criteria such as the data update frequency and delay time. For example, the analysis unit can analyze the data from the recognition unit in real time and provide immediate audio feedback. The analysis unit can also analyze the data from the recognition unit in real time and provide immediate visual feedback. Furthermore, the analysis unit can analyze the data from the recognition unit in real time and provide immediate haptic feedback. This enables a rapid response by analyzing in real time and providing immediate feedback. For example, the analysis unit can analyze the data from the recognition unit in real time and provide immediate audio feedback. The analysis unit can also analyze the data from the recognition unit in real time and provide immediate visual feedback. Furthermore, the analysis unit can analyze the data from the recognition unit in real time and provide immediate haptic feedback.

[0065] The analysis unit can perform analysis based on attribute information of the recognition target. The analysis unit performs analysis taking into account, for example, the attribute information of the recognition target. The attribute information includes information such as age, gender, and occupation. For example, the analysis unit can provide an appropriate analysis result taking into account the age of the recognition target. The analysis unit can also provide an appropriate analysis result taking into account the gender of the recognition target. Furthermore, the analysis unit can also provide an appropriate analysis result taking into account the occupation of the recognition target. In this way, an appropriate analysis result can be provided by taking into account the attribute information. For example, the analysis unit can provide an appropriate analysis result taking into account the age of the recognition target. The analysis unit can also provide an appropriate analysis result taking into account the gender of the recognition target. Furthermore, the analysis unit can provide an appropriate analysis result taking into account the occupation of the recognition target.

[0066] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. The facial expression recognition technology analyzes facial expressions to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotions. For example, the analysis unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The analysis unit can also estimate the user's emotions using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotions. For example, the analysis unit measures the user's heart rate with a sensor and estimates the emotions using a biometric algorithm. This allows important information to be provided quickly by prioritizing analysis results according to the user's emotions. For example, if the user is nervous, important analysis results can be provided with priority. If the user is relaxed, analysis results can be provided with normal priority. Furthermore, if the user is in a hurry, analysis results that need to be provided quickly can be provided with priority.

[0067] The analysis unit can improve the accuracy of analysis by referring to the user's past analysis history. The analysis unit can improve the accuracy of analysis by referring to, for example, the user's past analysis history. The past analysis history includes information such as date, time, location, and analysis target. For example, the analysis unit can improve the analysis accuracy in similar situations by referring to the user's past analysis history. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis history and learning specific patterns. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis history. In this way, the analysis accuracy is improved by referring to the past analysis history. For example, the analysis unit can improve the analysis accuracy in similar situations by referring to the user's past analysis history. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis history and learning specific patterns. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis history.

[0068] The analysis unit can customize the analysis results based on the user's current activity status. The analysis unit customizes the analysis results, for example, by taking into account the user's current activity status. The current activity status includes information such as whether the user is moving, resting, or working. For example, the analysis unit can provide concise and quick analysis results when the user is walking. The analysis unit can also provide detailed analysis results when the user is sitting. Furthermore, the analysis unit can provide analysis results at an appropriate time when the user is exercising. In this way, the analysis results can be provided at an appropriate time by taking into account the current activity status. For example, the analysis unit can provide concise and quick analysis results when the user is walking. The analysis unit can also provide detailed analysis results when the user is sitting. Furthermore, the analysis unit can provide analysis results at an appropriate time when the user is exercising.

[0069] The navigation unit can estimate the user's emotions and adjust the tone of the navigation voice based on the estimated user's emotions. The navigation unit, for example, uses facial expression recognition technology to estimate the user's emotions. The facial expression recognition technology analyzes facial expressions to estimate the user's emotions. For example, the navigation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The navigation unit can also estimate the user's emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotions. For example, the navigation unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The navigation unit can also estimate the user's emotions using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotions. For example, the navigation unit measures the user's heart rate with a sensor and estimates the emotions using a biometric algorithm. This enables more appropriate navigation by adjusting the tone of the voice according to the user's emotions. For example, if the user is nervous, the guidance can be given in a calm voice tone, if the user is relaxed, the guidance can be given in a bright voice tone, and if the user is in a hurry, the guidance can be given in a quick and concise voice tone.

[0070] The navigation unit can refer to the user's past navigation history and provide an optimal navigation route. The navigation unit can, for example, refer to the user's past navigation history and provide an optimal navigation route. The past navigation history includes information such as date and time, route, and destination. For example, the navigation unit can refer to the user's past navigation history and suggest an optimal route. The navigation unit can also refer to the user's past navigation history and suggest a route that avoids congestion. Furthermore, the navigation unit can also refer to the user's past navigation history and suggest the most efficient route. In this way, the optimal route can be provided by referring to the past navigation history. For example, the navigation unit can refer to the user's past navigation history and suggest an optimal route. The navigation unit can also refer to the user's past navigation history and suggest a route that avoids congestion. Furthermore, the navigation unit can refer to the user's past navigation history and suggest the most efficient route.

[0071] The navigation unit can switch the audio output method according to specific environmental conditions. The navigation unit switches the audio output method according to specific environmental conditions, such as noise levels. Specific environmental conditions include brightness, weather, noise levels, and the like. For example, the navigation unit can increase the volume of the audio when providing guidance in noisy places. The navigation unit can also decrease the volume when providing guidance in quiet places. Furthermore, the navigation unit can optimize the audio output method when headphones are used. This enables appropriate navigation by switching the audio output method according to environmental conditions. For example, the navigation unit can increase the volume of the audio when providing guidance in noisy places. The navigation unit can also decrease the volume when providing guidance in quiet places. Furthermore, the navigation unit can optimize the audio output method when headphones are used.

[0072] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. The navigation unit, for example, uses facial expression recognition technology to estimate the user's emotions. The facial expression recognition technology analyzes facial expressions to estimate the user's emotions. For example, the navigation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The navigation unit can also estimate the user's emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotions. For example, the navigation unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The navigation unit can also estimate the user's emotions using biometric data. The biometric data analyzes biological data such as heart rate and electrodermal activity to estimate the emotions. For example, the navigation unit measures the user's heart rate with a sensor and estimates the emotions using a biometric algorithm. This allows important information to be provided quickly by determining navigation priorities according to the user's emotions. For example, if the user is nervous, important navigation information can be provided with priority. If the user is relaxed, navigation can be performed with normal priority. Furthermore, if the user is in a hurry, navigation information that needs to be provided quickly can be provided with priority.

[0073] The navigation unit can provide an optimal navigation route based on the user's geographical location information. The navigation unit can provide the optimal navigation route by taking into account, for example, the user's geographical location information. The geographical location information is obtained using GPS data, Wi-Fi location information, beacons, etc. For example, when the user is in a specific location, the navigation unit can provide an optimal route related to the location. Also, when the user is moving, the navigation unit can provide an optimal route related to a destination. Furthermore, when the user is in a specific area, the navigation unit can provide information related to the area. In this way, the optimal route can be provided by taking into account the geographical location information. For example, when the user is in a specific location, the navigation unit can provide an optimal route related to the location. Also, when the user is moving, the navigation unit can provide an optimal route related to a destination. Furthermore, when the user is in a specific area, the navigation unit can provide information related to the area.

[0074] The navigation unit can analyze the user's social media activity and provide related navigation information. The navigation unit can, for example, analyze the user's social media activity and provide related navigation information. Social media activity includes information such as the content of posts, the number of likes, and the number of followers. For example, the navigation unit can prioritize guidance to places where the user frequently interacts on social media. The navigation unit can also prioritize guidance to places in which the user has shown interest on social media. Furthermore, the navigation unit can prioritize guidance to places related to a specific event based on the user's social media activity. In this way, related navigation information can be provided by analyzing the social media activity. For example, the navigation unit can prioritize guidance to places where the user frequently interacts on social media. The navigation unit can also prioritize guidance to places in which the user has shown interest on social media. Furthermore, the navigation unit can prioritize guidance to places related to a specific event based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the recognition unit, analysis unit, and navigation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes surrounding people and objects using the AI ​​camera of the smart device 14. The analysis unit analyzes information recognized by the specific processing unit 290 of the data processing device 12. The navigation unit provides navigation by voice based on the information analyzed by the specific processing unit 290 of the data processing device 12. The navigation unit can customize the type of voice and output method using, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recognition unit, analysis unit, and navigation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes surrounding people and objects using the AI ​​camera of the smart glasses 214. The analysis unit analyzes information recognized by the specific processing unit 290 of the data processing device 12. The navigation unit provides navigation by voice based on the information analyzed by the specific processing unit 290 of the data processing device 12. The navigation unit can customize the type of voice and output method using, for example, the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described recognition unit, analysis unit, and navigation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit recognizes surrounding people and objects using the AI ​​camera of the headset type terminal 314. The analysis unit analyzes information recognized by the specific processing unit 290 of the data processing device 12. The navigation unit provides navigation by voice based on the information analyzed by the specific processing unit 290 of the data processing device 12. The navigation unit can customize the type of voice and output method by, for example, the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recognition unit, analysis unit, and navigation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes surrounding people and objects using an AI camera of the robot 414. The analysis unit analyzes information recognized by the specific processing unit 290 of the data processing device 12. The navigation unit provides navigation by voice based on the information analyzed by the specific processing unit 290 of the data processing device 12. The navigation unit can customize the type of voice and output method by, for example, the control unit 46A of the robot 414.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The audio glasses system can further include a health management unit that monitors the user's health condition. The health management unit acquires biometric data such as heart rate, blood pressure, and body temperature, and monitors the user's health condition in real time. For example, the health management unit can issue a warning if the heart rate is abnormally high. Also, if the blood pressure is high, the health management unit can provide audio guidance to help the user relax. Furthermore, if the body temperature is high, the health management unit can encourage the user to take a rest. This allows the system to constantly monitor the user's health condition and provide appropriate advice.

[0077] The audio glasses system may further include a behavioral learning unit that learns the user's behavioral patterns. The behavioral learning unit records the user's daily behavior and analyzes the patterns. For example, if the user takes a walk at the same time every morning, health advice can be provided based on that time. Also, if the user frequently visits a particular place, information related to that place can be provided. Furthermore, if the user engages in a particular activity at a particular time of day, information related to that activity can be provided in advance. This enables more personalized information to be provided based on the user's behavioral patterns.

[0078] The audio glasses system may further include a music selection unit that estimates the user's emotions and selects music based on the estimated emotions. The music selection unit analyzes the user's emotions and automatically plays music that matches those emotions. For example, if the user is relaxed, relaxing music can be played. Also, if the user is feeling stressed, music to reduce stress can be played. Furthermore, if the user wants to cheer up, energetic music can be played. In this way, music that matches the user's emotions can be provided, improving their mood.

[0079] The audio glasses system may further include an environmental sound analysis unit that analyzes environmental sounds around the user and provides appropriate audio feedback. The environmental sound analysis unit analyzes the ambient noise level and type of sound and provides audio feedback accordingly. For example, if the surroundings are very noisy, the volume of the audio feedback can be automatically increased. Also, if the surroundings are quiet, the volume of the audio feedback can be reduced. Furthermore, if a specific sound (e.g., an ambulance siren) is heard, information about that sound can be provided. This makes it possible to provide appropriate audio feedback according to the surrounding environment.

[0080] The audio glasses system may further include a visual feedback unit that estimates the user's emotions and provides visual feedback based on the estimated emotions. The visual feedback unit analyzes the user's emotions and provides visual feedback according to the emotions. For example, if the user is tense, it may display a video that helps the user relax. Also, if the user is relaxed, it may display a video that helps the user to cheer up. Furthermore, if the user is stressed, it may display a video that helps to reduce stress. In this way, visual feedback according to the user's emotions is provided, and the user's mood can be improved.

[0081] The audio glasses system may further include an exercise advice unit that acquires the user's exercise data and provides exercise advice. The exercise advice unit acquires exercise data such as the user's number of steps, distance traveled, and calories burned, and provides appropriate exercise advice. For example, if the user has reached a certain number of steps, the exercise advice unit may encourage the user to take a rest. If the user has not reached a target amount of exercise, the exercise advice unit may suggest additional exercise. Furthermore, if the user is exercising excessively, the exercise advice unit may advise the user to moderate their exercise. In this way, appropriate exercise advice can be provided based on the user's exercise data.

[0082] The audio glasses system may further include a notification management unit that estimates the user's emotions and determines the priority of notifications based on the estimated emotions. The notification management unit analyzes the user's emotions and determines the priority of notifications according to the emotions. For example, if the user is nervous, it may prioritize displaying only important notifications. Alternatively, if the user is relaxed, it may prioritize displaying regular notifications. Furthermore, if the user is in a hurry, it may prioritize displaying urgent notifications. This enables appropriate notification management according to the user's emotions.

[0083] The audio glasses system may further include a dietary advice unit that acquires the user's dietary data and provides dietary advice. The dietary advice unit acquires data such as the user's dietary content, calorie intake, and nutritional balance, and provides appropriate dietary advice. For example, if the user is consuming excessive calories, the unit may advise the user to reduce their calories. Also, if the user is lacking in nutritional balance, the unit may suggest a balanced diet. Furthermore, if the user is lacking in a specific nutrient, the unit may suggest ingredients containing that nutrient. This makes it possible to provide appropriate dietary advice based on the user's dietary data.

[0084] The audio glasses system may further include a reminder management unit that estimates the user's emotions and adjusts the contents of reminders based on the estimated emotions. The reminder management unit analyzes the user's emotions and adjusts the contents of reminders according to the emotions. For example, if the user is nervous, the reminder contents can be displayed in a concise manner. If the user is relaxed, detailed reminders can be displayed. Furthermore, if the user is in a hurry, only important reminders can be displayed. This enables appropriate reminder management according to the user's emotions.

[0085] The audio glasses system may further include a sleep advice unit that acquires the user's sleep data and provides sleep advice. The sleep advice unit acquires data such as the user's sleep time, sleep quality, and number of times they turn over in their sleep, and provides appropriate sleep advice. For example, if the user is not getting enough sleep, it may advise them to go to bed earlier. Also, if the user is not getting deep sleep, it may suggest ways to relax. Furthermore, if the user turns over a lot in their sleep, it may suggest that they review their bedding. In this way, it is possible to provide appropriate sleep advice based on the user's sleep data.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The recognition unit recognizes people and objects in the vicinity. The recognition unit uses, for example, face recognition technology or object recognition technology. Face recognition technology is realized using technologies such as deep learning and pattern matching. Object recognition technology is realized using technologies such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). For example, the recognition unit can recognize faces using deep learning. The recognition unit can also recognize objects using YOLO. Furthermore, the recognition unit can recognize specific patterns using pattern matching. Step 2: The analysis unit analyzes the information recognized by the recognition unit. The analysis unit performs the analysis based on, for example, the type of data and the analysis algorithm. The analysis algorithm includes, for example, a machine learning algorithm and a statistical analysis algorithm. For example, the analysis unit can analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using a statistical analysis algorithm. Furthermore, the analysis unit can analyze the data using data mining technology. Step 3: The navigation unit performs voice navigation based on the information analyzed by the analysis unit. The navigation unit includes, for example, voice type, voice output method, and voice customization function. Voice types include, for example, male voice, female voice, robot voice, etc. Voice output methods include, for example, speaker, earphone, bone conduction, etc. Voice customization functions include, for example, volume adjustment, voice speed adjustment, voice tone change, etc. For example, the navigation unit can perform voice navigation using a male voice. The navigation unit can also output voice using a speaker. Furthermore, the navigation unit can output voice with the volume adjusted.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0159] [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recognition unit that recognizes people and objects in the surrounding area; an analysis unit that analyzes the information recognized by the recognition unit; a navigation unit that performs voice navigation based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The recognition unit Use facial or object recognition technology The system of claim 1 .

3. The navigation unit Audio type or audio output method, including audio customization features The system of claim 1 .

4. The recognition unit Estimate the user's emotion and adjust the recognition accuracy based on the estimated user emotion. The system of claim 1 .

5. The recognition unit Switching recognition algorithms depending on specific environmental conditions The system of claim 1 .

6. The recognition unit Refer to the user's past recognition history to determine recognition priority The system of claim 1 .

7. The recognition unit Estimate the user's emotions and prioritize recognition targets based on the estimated user emotions. The system of claim 1 .

8. The recognition unit Filtering recognition targets based on the user's geographic location The system of claim 1 .

9. The recognition unit Analyze users' social media activities and prioritize relevant recognition targets The system of claim 1 .

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