System

Abstract

The system according to the embodiment aims to quickly and accurately acquire detailed information on an object.SOLUTION: A system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires an image captured by a camera. The analysis unit analyzes the image acquired by the acquisition unit and specifies the object. The providing unit acquires, from the database, information related to the target identified by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately obtain detailed information about an object.

[0005] The system according to the embodiment aims to quickly and accurately obtain detailed information about an object. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires an image captured by a camera. The analysis unit analyzes the image acquired by the acquisition unit and identifies an object. The provision unit acquires information about the object identified by the analysis unit from a database. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately obtain detailed information about an object. [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) An information provision system according to an embodiment of the present invention allows a user to learn detailed information about an object of interest simply by pointing a device at it. When a user points a device at an object, the device's camera captures the object. AI analyzes the image to identify the object, retrieves related information from a database, and provides the information to the user. For example, when a user points the device at a plant, information such as the plant's name, how to grow it, and environmental conditions required is displayed. Similarly, when a user points the device at a building, information such as the building's history, designer, and year of construction is displayed. This system can also be used as a guide at tourist spots or as educational material. For example, when a user points a device at an object, the device's camera captures the object. AI analyzes the image and identifies the object. The AI ​​uses an image recognition algorithm to identify the object and retrieves related information from a database. This allows the information provision system to learn detailed information about an object of interest simply by pointing the device at it. For example, by using it as a guide at a tourist spot or as teaching material in an educational setting, users can quickly and easily acquire knowledge about the subject.

[0029] An information provision system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. When a user points a device toward an object, the acquisition unit captures an image of the object using the camera. For example, the acquisition unit acquires an image of the object using the device's camera. The acquisition unit can also automatically start capturing an image when the user points the device toward the object at a specific angle or distance. The acquisition unit can also start capturing an image based on a voice command from the user. The analysis unit analyzes the image acquired by the acquisition unit and identifies the object. For example, the analysis unit identifies the object using an image recognition algorithm. Image recognition algorithms include, for example, a convolutional neural network (CNN) or a support vector machine (SVM). The analysis unit can also extract features of the object and compare them with known data in a database. The analysis unit can also improve the accuracy of image analysis using AI. The provision unit acquires information about the object identified by the analysis unit from a database and provides it to the user. For example, the provision unit acquires text information and image information about the object from the database. The provision unit can also display the acquired information on the user's device. Furthermore, the providing unit can read out information based on a voice command from the user. As a result, the information providing system according to the embodiment allows the user to learn detailed information about an object of interest simply by pointing the device at the object.

[0030] The acquisition unit can capture an image of an object with the camera when the user points the device at the object. For example, the acquisition unit captures an image of the object with the camera when the user points the device at the object. For example, the acquisition unit can acquire an image of the object using the camera of the device. The acquisition unit can also automatically start capturing an image when the user points the device at the object at a specific angle or distance. Furthermore, the acquisition unit can start capturing an image based on a voice command from the user. This allows the camera to capture an image of the object when the user points the device at the object.

[0031] The analysis unit can identify the object using an image recognition algorithm. The analysis unit can identify the object using, for example, an image recognition algorithm. Image recognition algorithms include, for example, a convolutional neural network (CNN) and a support vector machine (SVM). The analysis unit can also extract features of the object and compare them with known data in a database. Furthermore, the analysis unit can use AI to improve the accuracy of image analysis. This makes it possible to identify the object using the image recognition algorithm.

[0032] The providing unit can obtain related information from the database and provide it to the user. The providing unit, for example, obtains related information from the database and provides it to the user. For example, the providing unit obtains text information or image information about an object from the database. The providing unit can also display the obtained information on the user's device. Furthermore, the providing unit can read out information based on a voice instruction from the user. In this way, related information can be obtained from the database and provided to the user.

[0033] The providing unit can display detailed information about the object. The providing unit displays, for example, detailed information about the object. For example, the providing unit displays information acquired from a database on the user's device. The providing unit can also display the acquired information in a visually easy-to-understand format. Furthermore, the providing unit can read out information based on a voice instruction from the user. This makes it possible to display detailed information about the object.

[0034] The providing unit can be used as a guide at a tourist spot or as teaching material in an educational setting. The providing unit can be used, for example, as a guide at a tourist spot or as teaching material in an educational setting. For example, the providing unit provides information about an object as a guide at a tourist spot. The providing unit can also provide information about an object as teaching material in an educational setting. Furthermore, the providing unit can read out information based on a voice instruction from the user. This allows the device to be used as a guide at a tourist spot or as teaching material in an educational setting.

[0035] The acquisition unit can analyze the user's past shooting history and select the optimal shooting method. The acquisition unit, for example, analyzes the user's past shooting history and selects the optimal shooting method. For example, the acquisition unit automatically selects a shooting mode (e.g., portrait, landscape) that the user has used favorably in the past. The acquisition unit can also analyze the tendencies of images taken by the user in the past and automatically adjust the optimal exposure settings. The acquisition unit can also suggest optimal shooting conditions by taking into account the location and time of day when the user took photos in the past. In this way, the user's past shooting history can be analyzed and the optimal shooting method can be selected.

[0036] The acquisition unit can perform filtering based on the user's current location information and environmental conditions when capturing an image. For example, the acquisition unit performs filtering based on the user's current location information and environmental conditions when capturing an image. For example, when the user is outdoors, the acquisition unit performs filtering taking natural light into consideration. Furthermore, when the user is indoors, the acquisition unit can also perform filtering taking artificial light into consideration. Furthermore, the acquisition unit can perform region-specific filtering based on the user's current location information. This allows filtering to be performed based on the user's current location information and environmental conditions.

[0037] The acquisition unit can select an appropriate imaging means according to the user's input method when capturing an image. For example, the acquisition unit selects an appropriate imaging means according to the user's input method when capturing an image. For example, when the user gives a voice command such as "take a picture," the acquisition unit starts capturing an image based on the voice input. The acquisition unit can also start capturing an image based on gesture input when the user waves their hand toward the camera with a gesture. The acquisition unit can also start capturing an image based on touch input when the user taps the touchscreen. This makes it possible to select an appropriate imaging means according to the user's input method.

[0038] The acquisition unit can prioritize photographing highly relevant objects in consideration of the user's geographical location information when photographing. For example, the acquisition unit prioritizes photographing highly relevant objects in consideration of the user's geographical location information when photographing. For example, when the user is in a tourist spot, the acquisition unit prioritizes photographing tourist attractions. Furthermore, when the user is in a museum, the acquisition unit can also prioritize photographing exhibits. Furthermore, when the user is in a park, the acquisition unit can also prioritize photographing natural scenery. This makes it possible to prioritize photographing highly relevant objects in consideration of the user's geographical location information.

[0039] The acquisition unit can analyze the user's social media activity and capture related objects when capturing images. For example, the acquisition unit analyzes the user's social media activity and captures related objects when capturing images. For example, the acquisition unit captures objects related to themes that the user often posts about on social media. The acquisition unit can also prioritize capturing images of places that the user's friends have shared on social media. The acquisition unit can also capture images of objects recommended by accounts that the user follows on social media. This makes it possible to analyze the user's social media activity and capture related objects.

[0040] The acquisition unit can customize the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit customizes the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit automatically sets a shooting angle that the user previously preferred. The acquisition unit can also automatically apply a filter that the user previously rated. The acquisition unit can also adjust the shooting method by reflecting points for improvement that the user previously pointed out. In this way, the shooting method can be customized by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the object during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the object during analysis. For example, the analysis unit can display detailed analysis results for important objects. The analysis unit can also display concise analysis results for general objects. The analysis unit can also display detailed analysis results including additional information for objects in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the object.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the object during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the object during analysis. For example, in the case of a plant, the analysis unit applies an analysis algorithm specific to the plant. In addition, in the case of a building, the analysis unit can also apply an analysis algorithm specific to the building. In addition, in the case of an animal, the analysis unit can also apply an analysis algorithm specific to the animal. In this way, different analysis algorithms can be applied depending on the category of the object.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on analysis results previously evaluated by the user. The analysis unit can also improve the accuracy of the analysis by reflecting errors previously pointed out by the user. The analysis unit can also analyze the user's past analysis history and suggest the optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on when the object was photographed during analysis. For example, the analysis unit determines the analysis priority based on when the object was photographed during analysis. For example, the analysis unit prioritizes analyzing objects that were photographed recently. The analysis unit can also prioritize analyzing objects that the user photographed at a specific event. The analysis unit can also prioritize analyzing objects that the user photographed in the past but that have not yet been analyzed. This makes it possible to determine the analysis priority based on when the object was photographed.

[0045] The analysis unit can adjust the order of analysis based on the relevance of objects during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of objects during analysis. For example, the analysis unit prioritizes analyzing objects in a category in which the user is interested. The analysis unit can also adjust the order of analysis based on related keywords searched for by the user in the past. The analysis unit can also prioritize analyzing objects with high relevance based on the user's current location information. This makes it possible to adjust the order of analysis based on the relevance of objects.

[0046] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user is an expert, the analysis unit can display the analysis results using detailed technical terminology. If the user is a beginner, the analysis unit can also display the analysis results in simple language. The analysis unit can also suggest the optimal level of technical terminology based on the user's past analysis history. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise.

[0047] The providing unit can adjust the level of detail of the information to be provided based on the importance of the object when providing the information. For example, the providing unit adjusts the level of detail of the information to be provided based on the importance of the object when providing the information. For example, the providing unit provides detailed information for an important object. The providing unit can also provide concise information for a general object. The providing unit can also provide detailed information including additional information for an object in which the user is particularly interested. In this way, the level of detail of the information to be provided can be adjusted based on the importance of the object.

[0048] The providing unit can apply different information provision algorithms depending on the category of the object when providing the information. For example, the providing unit applies different information provision algorithms depending on the category of the object when providing the information. For example, in the case of a plant, the providing unit applies an information provision algorithm specific to the plant. In addition, in the case of a building, the providing unit can also apply an information provision algorithm specific to the building. In addition, in the case of an animal, the providing unit can also apply an information provision algorithm specific to the animal. In this way, different information provision algorithms can be applied depending on the category of the object.

[0049] The providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit adjusts the information provision algorithm based on the information provision results previously evaluated by the user. The providing unit can also improve the accuracy of information provision by reflecting errors previously pointed out by the user. The providing unit can also analyze the user's past information provision history and suggest an optimal information provision method. This makes it possible to improve the accuracy of information provision by referring to the user's past information provision results.

[0050] The providing unit can determine the priority of information to be provided based on the time when the object was photographed at the time of providing. The providing unit determines the priority of information to be provided based on, for example, the time when the object was photographed. For example, the providing unit can provide information about objects that were photographed recently with priority. The providing unit can also provide information about objects that the user photographed at a specific event with priority. The providing unit can also provide information that the user photographed in the past but has not yet provided with priority. This makes it possible to determine the priority of information to be provided based on the time when the object was photographed.

[0051] The providing unit can adjust the order of information to be provided based on the relevance of the object at the time of providing. For example, the providing unit adjusts the order of information to be provided based on the relevance of the object at the time of providing. For example, the providing unit preferentially provides information about objects in a category in which the user is interested. The providing unit can also adjust the order of information based on related keywords searched for by the user in the past. The providing unit can also preferentially provide information about objects that are highly relevant based on the user's current location information. This makes it possible to adjust the order of information to be provided based on the relevance of the object.

[0052] The providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user is an expert, the providing unit can provide information using detailed technical terms. Also, if the user is a beginner, the providing unit can provide information in simple language. Furthermore, the providing unit can suggest an optimal level of technical terms based on the user's past information provision history. In this way, the use of technical terms in the information to be provided can be adjusted according to the user's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The acquisition unit acquires the user's biometric information, and the analysis unit can estimate the user's health condition based on the biometric information. For example, the acquisition unit measures the user's heart rate and body temperature using a sensor. The acquisition unit can also measure the electrical resistance of the user's skin to estimate the stress level. The analysis unit analyzes this biometric information and estimates the user's health condition. This makes it possible to provide appropriate information based on the user's health condition.

[0055] The providing unit can analyze the user's past behavior history and select the optimal information provision method. For example, the providing unit automatically selects the format of information (text, image, video) that the user has previously preferred to view. The providing unit can also analyze the trends in information the user has previously viewed and suggest the optimal information. Furthermore, the providing unit can adjust the optimal information provision method based on information that the user has previously evaluated. In this way, the user's past behavior history can be analyzed and the optimal information provision method can be selected.

[0056] The providing unit can detect the user's current activity status and provide information corresponding to the status. For example, if the user is exercising, the providing unit can provide information related to exercise with priority. Also, if the user is taking a break, the providing unit can provide information related to relaxation. Furthermore, if the user is working, the providing unit can provide information related to work. In this way, information corresponding to the user's current activity status can be provided.

[0057] The providing unit can analyze the user's social media activities and provide related information. For example, the providing unit can provide information related to themes that the user frequently posts on social media. The providing unit can also provide information shared by the user's friends on social media with priority. Furthermore, the providing unit can provide information recommended by accounts that the user follows on social media. In this way, the user's social media activities can be analyzed and related information can be provided.

[0058] The providing unit can improve the accuracy of information provision by reflecting the user's past feedback. For example, the providing unit adjusts the information provision algorithm based on the information provision results previously evaluated by the user. The providing unit can also improve the accuracy of information provision by reflecting errors previously pointed out by the user. Furthermore, the providing unit can analyze the user's past information provision history and suggest an optimal information provision method. In this way, the accuracy of information provision can be improved by reflecting the user's past feedback.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: When a user points the device at an object, the acquisition unit captures the object with the camera. For example, the acquisition unit captures an image of the object using the device's camera. It can also automatically start capturing images when the user points the device at a specific angle or distance toward the object. It can also start capturing images based on a user's voice command. Step 2: The analysis unit analyzes the images acquired by the acquisition unit and identifies the object. For example, it uses an image recognition algorithm to identify the object. Image recognition algorithms include convolutional neural networks (CNNs) and support vector machines (SVMs). It can also extract the object's features and compare them with known data in a database. Furthermore, it can use AI to improve the accuracy of image analysis. Step 3: The providing unit retrieves information about the object identified by the analyzing unit from the database and provides it to the user. For example, it retrieves text information or image information about the object from the database. It can also display the retrieved information on the user's device. It can also read out the information based on the user's voice instructions.

[0061] (Example 2) An information provision system according to an embodiment of the present invention allows a user to learn detailed information about an object of interest simply by pointing a device at it. When a user points a device at an object, the device's camera captures the object. AI analyzes the image to identify the object, retrieves related information from a database, and provides the information to the user. For example, when a user points the device at a plant, information such as the plant's name, how to grow it, and environmental conditions required is displayed. Similarly, when a user points the device at a building, information such as the building's history, designer, and year of construction is displayed. This system can also be used as a guide at tourist spots or as educational material. For example, when a user points a device at an object, the device's camera captures the object. AI analyzes the image and identifies the object. The AI ​​uses an image recognition algorithm to identify the object and retrieves related information from a database. This allows the information provision system to learn detailed information about an object of interest simply by pointing the device at it. For example, by using it as a guide at a tourist spot or as teaching material in an educational setting, users can quickly and easily acquire knowledge about the subject.

[0062] An information provision system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. When a user points a device toward an object, the acquisition unit captures an image of the object using the camera. For example, the acquisition unit acquires an image of the object using the device's camera. The acquisition unit can also automatically start capturing an image when the user points the device toward the object at a specific angle or distance. The acquisition unit can also start capturing an image based on a voice command from the user. The analysis unit analyzes the image acquired by the acquisition unit and identifies the object. For example, the analysis unit identifies the object using an image recognition algorithm. Image recognition algorithms include, for example, a convolutional neural network (CNN) or a support vector machine (SVM). The analysis unit can also extract features of the object and compare them with known data in a database. The analysis unit can also improve the accuracy of image analysis using AI. The provision unit acquires information about the object identified by the analysis unit from a database and provides it to the user. For example, the provision unit acquires text information and image information about the object from the database. The provision unit can also display the acquired information on the user's device. Furthermore, the providing unit can read out information based on a voice command from the user. As a result, the information providing system according to the embodiment allows the user to learn detailed information about an object of interest simply by pointing the device at the object.

[0063] The acquisition unit can capture an image of an object with the camera when the user points the device at the object. For example, the acquisition unit captures an image of the object with the camera when the user points the device at the object. For example, the acquisition unit can acquire an image of the object using the camera of the device. The acquisition unit can also automatically start capturing an image when the user points the device at the object at a specific angle or distance. Furthermore, the acquisition unit can start capturing an image based on a voice command from the user. This allows the camera to capture an image of the object when the user points the device at the object.

[0064] The analysis unit can identify the object using an image recognition algorithm. The analysis unit can identify the object using, for example, an image recognition algorithm. Image recognition algorithms include, for example, a convolutional neural network (CNN) and a support vector machine (SVM). The analysis unit can also extract features of the object and compare them with known data in a database. Furthermore, the analysis unit can use AI to improve the accuracy of image analysis. This makes it possible to identify the object using the image recognition algorithm.

[0065] The providing unit can obtain related information from the database and provide it to the user. The providing unit, for example, obtains related information from the database and provides it to the user. For example, the providing unit obtains text information or image information about an object from the database. The providing unit can also display the obtained information on the user's device. Furthermore, the providing unit can read out information based on a voice instruction from the user. In this way, related information can be obtained from the database and provided to the user.

[0066] The providing unit can display detailed information about the object. The providing unit displays, for example, detailed information about the object. For example, the providing unit displays information acquired from a database on the user's device. The providing unit can also display the acquired information in a visually easy-to-understand format. Furthermore, the providing unit can read out information based on a voice instruction from the user. This makes it possible to display detailed information about the object.

[0067] The providing unit can be used as a guide at a tourist spot or as teaching material in an educational setting. The providing unit can be used, for example, as a guide at a tourist spot or as teaching material in an educational setting. For example, the providing unit provides information about an object as a guide at a tourist spot. The providing unit can also provide information about an object as teaching material in an educational setting. Furthermore, the providing unit can read out information based on a voice instruction from the user. This allows the device to be used as a guide at a tourist spot or as teaching material in an educational setting.

[0068] The acquisition unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of capturing images based on the estimated user emotions. For example, if the user is excited, the acquisition unit can start capturing images immediately and quickly capture images. If the user is relaxed, the acquisition unit can wait a short time before starting capturing images to capture natural-looking images. If the user is feeling anxious, the acquisition unit can display a confirmation message before capturing images and start capturing images only after obtaining the user's consent. This makes it possible to adjust the timing of capturing images based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The acquisition unit can analyze the user's past shooting history and select the optimal shooting method. The acquisition unit, for example, analyzes the user's past shooting history and selects the optimal shooting method. For example, the acquisition unit automatically selects a shooting mode (e.g., portrait, landscape) that the user has used favorably in the past. The acquisition unit can also analyze the tendencies of images taken by the user in the past and automatically adjust the optimal exposure settings. The acquisition unit can also suggest optimal shooting conditions by taking into account the location and time of day when the user took photos in the past. In this way, the user's past shooting history can be analyzed and the optimal shooting method can be selected.

[0070] The acquisition unit can perform filtering based on the user's current location information and environmental conditions when capturing an image. For example, the acquisition unit performs filtering based on the user's current location information and environmental conditions when capturing an image. For example, when the user is outdoors, the acquisition unit performs filtering taking natural light into consideration. Furthermore, when the user is indoors, the acquisition unit can also perform filtering taking artificial light into consideration. Furthermore, the acquisition unit can perform region-specific filtering based on the user's current location information. This allows filtering to be performed based on the user's current location information and environmental conditions.

[0071] The acquisition unit can select an appropriate imaging means according to the user's input method when capturing an image. For example, the acquisition unit selects an appropriate imaging means according to the user's input method when capturing an image. For example, when the user gives a voice command such as "take a picture," the acquisition unit starts capturing an image based on the voice input. The acquisition unit can also start capturing an image based on gesture input when the user waves their hand toward the camera with a gesture. The acquisition unit can also start capturing an image based on touch input when the user taps the touchscreen. This makes it possible to select an appropriate imaging means according to the user's input method.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of objects to be photographed based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of objects to be photographed based on the estimated user emotions. For example, when the user is excited, the acquisition unit prioritizes photographing conspicuous objects. Furthermore, when the user is relaxed, the acquisition unit can prioritize photographing landscapes or natural objects. Furthermore, when the user is anxious, the acquisition unit can prioritize photographing objects that give a sense of security. This makes it possible to determine the priority of objects to be photographed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The acquisition unit can prioritize photographing highly relevant objects in consideration of the user's geographical location information when photographing. For example, the acquisition unit prioritizes photographing highly relevant objects in consideration of the user's geographical location information when photographing. For example, when the user is in a tourist spot, the acquisition unit prioritizes photographing tourist attractions. Furthermore, when the user is in a museum, the acquisition unit can also prioritize photographing exhibits. Furthermore, when the user is in a park, the acquisition unit can also prioritize photographing natural scenery. This makes it possible to prioritize photographing highly relevant objects in consideration of the user's geographical location information.

[0074] The acquisition unit can analyze the user's social media activity and capture related objects when capturing images. For example, the acquisition unit analyzes the user's social media activity and captures related objects when capturing images. For example, the acquisition unit captures objects related to themes that the user often posts about on social media. The acquisition unit can also prioritize capturing images of places that the user's friends have shared on social media. The acquisition unit can also capture images of objects recommended by accounts that the user follows on social media. This makes it possible to analyze the user's social media activity and capture related objects.

[0075] The acquisition unit can customize the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit customizes the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit automatically sets a shooting angle that the user previously preferred. The acquisition unit can also automatically apply a filter that the user previously rated. The acquisition unit can also adjust the shooting method by reflecting points for improvement that the user previously pointed out. In this way, the shooting method can be customized by reflecting the user's past feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is excited, the analysis unit can display the analysis results with a visually stimulating effect. If the user is relaxed, the analysis unit can display the analysis results in subdued colors. If the user is anxious, the analysis unit can display simple, easy-to-understand analysis results. This makes it possible to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the object during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the object during analysis. For example, the analysis unit can display detailed analysis results for important objects. The analysis unit can also display concise analysis results for general objects. The analysis unit can also display detailed analysis results including additional information for objects in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the object.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the object during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the object during analysis. For example, in the case of a plant, the analysis unit applies an analysis algorithm specific to the plant. In addition, in the case of a building, the analysis unit can also apply an analysis algorithm specific to the building. In addition, in the case of an animal, the analysis unit can also apply an analysis algorithm specific to the animal. In this way, different analysis algorithms can be applied depending on the category of the object.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on analysis results previously evaluated by the user. The analysis unit can also improve the accuracy of the analysis by reflecting errors previously pointed out by the user. The analysis unit can also analyze the user's past analysis history and suggest the optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can display a short, to-the-point analysis result. If the user is relaxed, the analysis unit can display a longer analysis result with detailed explanations. If the user is excited, the analysis unit can display an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The analysis unit can determine the analysis priority based on when the object was photographed during analysis. For example, the analysis unit determines the analysis priority based on when the object was photographed during analysis. For example, the analysis unit prioritizes analyzing objects that were photographed recently. The analysis unit can also prioritize analyzing objects that the user photographed at a specific event. The analysis unit can also prioritize analyzing objects that the user photographed in the past but that have not yet been analyzed. This makes it possible to determine the analysis priority based on when the object was photographed.

[0082] The analysis unit can adjust the order of analysis based on the relevance of objects during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of objects during analysis. For example, the analysis unit prioritizes analyzing objects in a category in which the user is interested. The analysis unit can also adjust the order of analysis based on related keywords searched for by the user in the past. The analysis unit can also prioritize analyzing objects with high relevance based on the user's current location information. This makes it possible to adjust the order of analysis based on the relevance of objects.

[0083] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user is an expert, the analysis unit can display the analysis results using detailed technical terminology. If the user is a beginner, the analysis unit can also display the analysis results in simple language. The analysis unit can also suggest the optimal level of technical terminology based on the user's past analysis history. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise.

[0084] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. For example, if the user is excited, the providing unit can provide information with a visually stimulating effect. Furthermore, if the user is relaxed, the providing unit can provide information in subdued colors. Furthermore, if the user is feeling anxious, the providing unit can provide simple, easy-to-understand information. This makes it possible to adjust the presentation method of the information to be provided based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The providing unit can adjust the level of detail of the information to be provided based on the importance of the object when providing the information. For example, the providing unit adjusts the level of detail of the information to be provided based on the importance of the object when providing the information. For example, the providing unit provides detailed information for an important object. The providing unit can also provide concise information for a general object. The providing unit can also provide detailed information including additional information for an object in which the user is particularly interested. In this way, the level of detail of the information to be provided can be adjusted based on the importance of the object.

[0086] The providing unit can apply different information provision algorithms depending on the category of the object when providing the information. For example, the providing unit applies different information provision algorithms depending on the category of the object when providing the information. For example, in the case of a plant, the providing unit applies an information provision algorithm specific to the plant. In addition, in the case of a building, the providing unit can also apply an information provision algorithm specific to the building. In addition, in the case of an animal, the providing unit can also apply an information provision algorithm specific to the animal. In this way, different information provision algorithms can be applied depending on the category of the object.

[0087] The providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit adjusts the information provision algorithm based on the information provision results previously evaluated by the user. The providing unit can also improve the accuracy of information provision by reflecting errors previously pointed out by the user. The providing unit can also analyze the user's past information provision history and suggest an optimal information provision method. This makes it possible to improve the accuracy of information provision by referring to the user's past information provision results.

[0088] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can provide longer information including detailed explanations. If the user is excited, the providing unit can provide information with visually stimulating effects. This makes it possible to adjust the length of the information to be provided based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The providing unit can determine the priority of information to be provided based on the time when the object was photographed at the time of providing. The providing unit determines the priority of information to be provided based on, for example, the time when the object was photographed. For example, the providing unit can provide information about objects that were photographed recently with priority. The providing unit can also provide information about objects that the user photographed at a specific event with priority. The providing unit can also provide information that the user photographed in the past but has not yet provided with priority. This makes it possible to determine the priority of information to be provided based on the time when the object was photographed.

[0090] The providing unit can adjust the order of information to be provided based on the relevance of the object at the time of providing. For example, the providing unit adjusts the order of information to be provided based on the relevance of the object at the time of providing. For example, the providing unit preferentially provides information about objects in a category in which the user is interested. The providing unit can also adjust the order of information based on related keywords searched for by the user in the past. The providing unit can also preferentially provide information about objects that are highly relevant based on the user's current location information. This makes it possible to adjust the order of information to be provided based on the relevance of the object.

[0091] The providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user is an expert, the providing unit can provide information using detailed technical terms. Also, if the user is a beginner, the providing unit can provide information in simple language. Furthermore, the providing unit can suggest an optimal level of technical terms based on the user's past information provision history. In this way, the use of technical terms in the information to be provided can be adjusted according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, and provision 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 acquisition unit acquires an image of an object using the camera 42 of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the object using an image recognition algorithm. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and acquires information about the object from the database 24 and displays it on the display 40A of the smart device 14. The provision unit can also read out the information using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image of an object using the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the object using an image recognition algorithm. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and acquires information about the object from the database 24 and displays it on the display of the smart glasses 214. The provision unit can also read out the information using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires an image of an object using the camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the object using an image recognition algorithm. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and acquires information about the object from the database 24 and displays it on the display 343 of the headset type terminal 314. The provision unit can also read out the information using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires an image of an object using the camera 42 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the object using an image recognition algorithm. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and acquires information about the object from the database 24 and displays it on the display of the robot 414. The provision unit can also read out the information using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The acquisition unit acquires the user's biometric information, and the analysis unit can estimate the user's health condition based on the biometric information. For example, the acquisition unit measures the user's heart rate and body temperature using a sensor. The acquisition unit can also measure the electrical resistance of the user's skin to estimate the stress level. The analysis unit analyzes this biometric information and estimates the user's health condition. This makes it possible to provide appropriate information based on the user's health condition.

[0094] The analysis unit can estimate the user's emotions and determine the priority of information based on the estimated emotions. For example, if the user is excited, the analysis unit can provide entertainment-related information with priority. If the user is relaxed, the analysis unit can provide relaxation-related information with priority. Furthermore, if the user is feeling anxious, the analysis unit can provide reassuring information with priority. In this way, the priority of information can be determined based on the user's emotions.

[0095] The providing unit can analyze the user's past behavior history and select the optimal information provision method. For example, the providing unit automatically selects the format of information (text, image, video) that the user has previously preferred to view. The providing unit can also analyze the trends in information the user has previously viewed and suggest the optimal information. Furthermore, the providing unit can adjust the optimal information provision method based on information that the user has previously evaluated. In this way, the user's past behavior history can be analyzed and the optimal information provision method can be selected.

[0096] The acquisition unit can estimate the user's emotions and automatically apply a filter when taking a photo based on the estimated emotions. For example, if the user is excited, the acquisition unit can apply a filter with vivid colors. If the user is relaxed, the acquisition unit can also apply a filter with calm colors. Furthermore, if the user is feeling anxious, the acquisition unit can also apply a filter with soft colors. In this way, a filter when taking a photo can be automatically applied based on the user's emotions.

[0097] The providing unit can detect the user's current activity status and provide information corresponding to the status. For example, if the user is exercising, the providing unit can provide information related to exercise with priority. Also, if the user is taking a break, the providing unit can provide information related to relaxation. Furthermore, if the user is working, the providing unit can provide information related to work. In this way, information corresponding to the user's current activity status can be provided.

[0098] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can display the analysis results using visually stimulating graphics. If the user is relaxed, the analysis unit can display the analysis results in calm colors. Furthermore, if the user is feeling anxious, the analysis unit can display the analysis results in a simple, easy-to-understand format. In this way, the display format of the analysis results can be adjusted based on the user's emotions.

[0099] The providing unit can analyze the user's social media activities and provide related information. For example, the providing unit can provide information related to themes that the user frequently posts on social media. The providing unit can also provide information shared by the user's friends on social media with priority. Furthermore, the providing unit can provide information recommended by accounts that the user follows on social media. In this way, the user's social media activities can be analyzed and related information can be provided.

[0100] The acquisition unit can estimate the user's emotions and adjust the audio guidance during shooting based on the estimated emotions. For example, if the user is excited, the acquisition unit can provide audio guidance in a cheerful tone. If the user is relaxed, the acquisition unit can also provide audio guidance in a calm tone. Furthermore, if the user is feeling anxious, the acquisition unit can also provide audio guidance in a tone that gives a sense of security. In this way, the audio guidance during shooting can be adjusted based on the user's emotions.

[0101] The providing unit can improve the accuracy of information provision by reflecting the user's past feedback. For example, the providing unit adjusts the information provision algorithm based on the information provision results previously evaluated by the user. The providing unit can also improve the accuracy of information provision by reflecting errors previously pointed out by the user. Furthermore, the providing unit can analyze the user's past information provision history and suggest an optimal information provision method. In this way, the accuracy of information provision can be improved by reflecting the user's past feedback.

[0102] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize entertainment-related analysis. Also, if the user is relaxed, the analysis unit can prioritize relaxation-related analysis. Furthermore, if the user is feeling anxious, the analysis unit can prioritize analysis that provides a sense of security. In this way, the priority of analysis can be determined based on the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: When a user points the device at an object, the acquisition unit captures the object with the camera. For example, the acquisition unit captures an image of the object using the device's camera. It can also automatically start capturing images when the user points the device at a specific angle or distance toward the object. It can also start capturing images based on a user's voice command. Step 2: The analysis unit analyzes the images acquired by the acquisition unit and identifies the object. For example, it uses an image recognition algorithm to identify the object. Image recognition algorithms include convolutional neural networks (CNNs) and support vector machines (SVMs). It can also extract the object's features and compare them with known data in a database. Furthermore, it can use AI to improve the accuracy of image analysis. Step 3: The providing unit retrieves information about the object identified by the analyzing unit from the database and provides it to the user. For example, it retrieves text information or image information about the object from the database. It can also display the retrieved information on the user's device. It can also read out the information based on the user's voice instructions.

[0105] 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.

[0106] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[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 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.

[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 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.

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

[0139] 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.

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

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires an image captured by a camera; an analysis unit that analyzes the image acquired by the acquisition unit and identifies an object; a providing unit that acquires information about the object identified by the analyzing unit from a database; Equipped with A system characterized by:

2. The acquisition unit When the user points the device at an object, the camera takes a picture of the object.

2. The system of claim 1.

3. The analysis unit Identifying objects using image recognition algorithms 2. The system of claim 1.

4. The providing unit Retrieving relevant information from a database and providing it to the user 2. The system of claim 1.

5. The providing unit View detailed information about an object 2. The system of claim 1.

6. The providing unit It can be used as a guide at tourist spots and as teaching material in educational settings.

2. The system of claim 1.

7. The acquisition unit Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past photography history and select the appropriate photography method 2. The system of claim 1.

Images