System

Abstract

To provide a system for supporting visually handicapped persons to spend their independent daily lives.SOLUTION: The system includes an acquisition part, an analysis part, and a provision part. The acquisition part acquires an image captured by an imaging part provided in a camera mounted on a user. The analysis part analyzes the image acquired by the acquisition part. The provision part provides a voice guide according to an analysis result by the analysis part to the user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not provide sufficient support for people with visual impairments to lead independent daily lives, and there is room for improvement.

[0005] The system according to the embodiment aims to support people with visual impairments in living an independent daily life. [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 an imaging unit provided in a camera worn by a user. The analysis unit analyzes the image acquired by the acquisition unit. The provision unit provides the user with an audio guide according to the analysis result by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can support people with visual impairments to lead independent daily lives. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is an interactive voice-guided glasses that utilizes AI to support the lives of people with visual impairments and enable them to live more independent daily lives. This system acquires an image captured by an imaging unit provided in a camera worn by a user, and analyzes the image to provide a voice guide to the user. For example, a user wears glasses, and the camera captures an image of the surroundings. This image is acquired by an acquisition unit and sent to an analysis unit. The analysis unit uses AI to analyze the acquired image and identify information required by the user. For example, information for the user to avoid obstacles while walking and directions to the destination are included. Next, based on the analysis result, the provision unit provides a voice guide to the user. The voice guide is provided with specific and easy-to-understand content so that the user can easily understand the current situation. For example, instructions such as "There is an obstacle ahead. Please turn right." are included. In addition, a reception unit that accepts the user's voice is also provided. When a user issues a question or instruction by voice, the reception unit accepts the voice, and the analysis unit analyzes the image according to the voice. For example, when a user asks "Where is the next intersection?", the analysis unit performs an analysis according to the question, and the provision unit provides an appropriate voice guide. The providing unit also includes a product guidance unit that provides product location guidance while shopping, and a transportation guidance unit that assists in the use of public transportation. This allows the user to receive support while shopping and using public transportation. This system enables people with visual impairments to live more independent daily lives. For example, being guided to the location of products while shopping allows the user to shop smoothly, and when using public transportation, the user can know the appropriate timing for transfers and getting off. This allows people with visual impairments to live more independent daily lives.

[0029] The visually impaired person support system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires an image captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture an image of the surroundings using a camera and acquire the image as digital data. The acquisition unit can also optimize the acquisition of the image using AI. The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit can, for example, recognize objects in the image using AI to identify information required by the user. The analysis unit can identify obstacles and destinations using object recognition technology. The analysis unit can also improve the accuracy of image analysis using AI. The provision unit provides the user with audio guidance according to the analysis result by the analysis unit. The provision unit can, for example, use voice synthesis technology to provide the user with specific and easy-to-understand audio guidance. The provision unit can also optimize the content of the audio guidance using AI. For example, the provision unit provides information for the user to avoid obstacles while walking and route guidance to the destination by voice. This allows the visually impaired person support system to support the user to move safely and efficiently. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping, and a traffic guidance unit that supports the use of public transportation. The product guidance unit, for example, identifies the location of products in a store and provides audio guidance to the user. The traffic guidance unit, for example, provides transfer guidance and operation information, and supports the user when using public transportation. In this way, the visually impaired person support system enables the user to live a more independent daily life. In this way, the visually impaired person support system according to the embodiment enables visually impaired people to live a more independent daily life.

[0030] The acquisition unit acquires images captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture images of the surroundings using a camera and acquire the images as digital data. Specifically, the camera is worn on the user's glasses or hat, or hung from the neck, and always captures the user's line of sight. The camera has a high resolution and a wide-angle lens, and can cover a wide range of vision around the user. The captured images are transmitted to the acquisition unit in real time and stored as digital data. The acquisition unit can also optimize image acquisition using AI. For example, AI extracts important information from the captured images and filters unnecessary data. This allows the acquisition unit to efficiently collect necessary information and transmit it to the analysis unit. In addition, the acquisition unit has a function to automatically adjust the imaging conditions according to changes in the environment. For example, it can automatically adjust brightness and contrast to capture clear images even at night or in backlit conditions. This allows the acquisition unit to provide high-quality images to the user in any environment and improve the performance of the entire system.

[0031] The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit, for example, uses AI to recognize objects in the image and identify information required by the user. Specifically, the AI ​​uses a deep learning algorithm to classify objects in the image and identify obstacles, destinations, signs, etc. For example, in order for a walking user to avoid obstacles ahead, the AI ​​detects obstacles in the image and identifies their positions and sizes. In addition, in order to provide route guidance on the way to the destination, the AI ​​recognizes the characteristics of road signs and buildings and provides the user with the necessary information. The analysis unit can identify obstacles and destinations using object recognition technology. For example, the AI ​​recognizes pedestrians, vehicles, traffic lights, etc. in the image in real time and guides the user to a safe route. Furthermore, the analysis unit can also improve the accuracy of image analysis using AI. For example, the AI ​​learns past data and improves the accuracy of object recognition in a specific environment or situation. This allows the analysis unit to provide accurate information to the user no matter what situation he or she is in, thereby increasing the reliability of the visually impaired support system.

[0032] The providing unit provides the user with a voice guide according to the analysis result by the analyzing unit. The providing unit provides the user with a specific and easy-to-understand voice guide, for example, by using a voice synthesis technology. Specifically, the providing unit conveys appropriate instructions to the user by voice based on the information sent from the analyzing unit. For example, if the user is walking and there is an obstacle ahead, the providing unit provides specific instructions by voice, such as "There is an obstacle ahead. Please move to the right to avoid it." In addition, when providing route guidance on the way to the destination, detailed guidance such as "Turn left at the next intersection" is provided. The providing unit can also optimize the content of the voice guide using AI. For example, the timing and content of the voice guide are adjusted according to the user's walking speed and the surrounding environment. This allows the user to receive the voice guide without stress. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping and a transportation guidance unit that supports the use of public transportation. The product guidance unit, for example, identifies the location of a product in a store and provides guidance to the user by voice. Specifically, it provides guidance such as "The product on the shelf on the right is the product you are looking for." The traffic guidance unit provides, for example, transfer guidance and operation information to support the user when using public transportation. Specifically, it provides detailed guidance such as "Get off at the next station and move to platform 3." In this way, the visually impaired support system enables the user to live a more independent daily life, and people with visual impairments can live a more independent daily life.

[0033] The visually impaired support system includes a reception unit that receives the voice of a user. The reception unit receives the voice of the user. The reception unit, for example, collects the user's voice using a microphone and analyzes the voice command using a voice recognition technology. The reception unit can also improve the accuracy of the voice recognition by using AI. For example, when a user asks "Where is the next intersection?", the reception unit recognizes the voice and transmits it to the analysis unit. In this way, the reception unit enables the user to ask questions or give instructions by voice. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's voice data to AI and causes AI to perform voice recognition. In this way, by receiving the user's voice, the visually impaired support system enables the user to ask questions or give instructions by voice.

[0034] The analysis unit can analyze the image according to the voice received by the reception unit. The analysis unit analyzes the image according to the voice received by the reception unit. For example, when a user asks "Where is the next intersection?", the analysis unit identifies an intersection in the image according to the question and transmits it to the provision unit. The analysis unit can also select an analysis target based on a voice command using AI. For example, the analysis unit can input a voice command to AI and cause the AI ​​to select an analysis target. This allows the analysis unit to perform image analysis according to the voice. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. This enables image analysis according to voice.

[0035] The providing unit may include a product guidance unit that provides guidance on the location of a product during shopping. The providing unit includes a product guidance unit that provides guidance on the location of a product during shopping. The product guidance unit, for example, identifies the location of a product in a store and provides audio guidance to the user. The product guidance unit can also identify the location of a product using AI. For example, the product guidance unit can input location information of a product in a store to AI and cause AI to execute processing to identify the location of the product. This allows the providing unit to provide guidance on the location of a product during shopping. A part or all of the above-mentioned processing in the product guidance unit may be performed using AI, or may be performed without using AI. This allows guidance on the location of a product during shopping.

[0036] The providing unit may include a traffic guidance unit that supports the use of public transportation. The providing unit includes a traffic guidance unit that supports the use of public transportation. The traffic guidance unit provides, for example, transfer guidance and operation information, and supports the user when using public transportation. The traffic guidance unit can also analyze operation information using AI and provide optimal transfer guidance. For example, the traffic guidance unit can input operation information to AI and cause AI to execute optimal transfer guidance. This allows the providing unit to support the use of public transportation. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. This allows the use of public transportation to be supported.

[0037] The acquisition unit can select an appropriate imaging angle by referring to the user's past behavior history when capturing an image. The acquisition unit selects an appropriate imaging angle by referring to the user's past behavior history when capturing an image. For example, if the user has preferred imaging at a particular angle in the past, the acquisition unit prioritizes that angle for imaging. It is also possible to capture an image while avoiding angles that the user has avoided in the past. Furthermore, it is also possible to select an optimal angle in a particular situation from the user's past behavior history. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past behavior history data into AI and cause AI to execute a process of selecting an optimal imaging angle. This makes it possible to select an optimal imaging angle based on the user's past behavior history.

[0038] The acquisition unit can analyze the surrounding environmental sound at the time of imaging, and automatically perform imaging when an important sound occurs. The acquisition unit can analyze the surrounding environmental sound at the time of imaging, and automatically perform imaging when an important sound occurs. For example, when a car horn sounds in the vicinity, the acquisition unit automatically performs imaging. Also, when a human voice is heard in the vicinity, the acquisition unit can also image the direction. Furthermore, when an alarm sound is sounded in the vicinity, the acquisition unit can also image the direction. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input surrounding environmental sound data to AI and cause AI to execute processing to detect important sounds. As a result, imaging can be automatically performed when an important sound occurs based on the surrounding environmental sound.

[0039] The acquisition unit can preferentially acquire related images in consideration of the geographical location information of the user when capturing an image. The acquisition unit preferentially acquires related images in consideration of the geographical location information of the user when capturing an image. For example, when the user is in a specific location, the acquisition unit preferentially captures information related to that location. In addition, when the user is moving, the acquisition unit can preferentially capture information related to the current location. Furthermore, when the user approaches the destination, the acquisition unit can preferentially capture information about the surrounding area. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute a process of preferentially acquiring related images. This makes it possible to preferentially acquire images that are highly related based on the geographical location information of the user.

[0040] The acquisition unit can analyze the user's social media activity at the time of imaging and acquire related images. The acquisition unit can analyze the user's social media activity at the time of imaging and acquire related images. For example, if the user mentions a specific place on social media, the acquisition unit preferentially acquires images of the place. Also, if the user participates in a specific event on social media, the acquisition unit can acquire images related to the event. Furthermore, the content of the user's social media posts can be analyzed to acquire related information. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into AI and cause AI to execute a process of acquiring related images. This makes it possible to acquire related images based on the user's social media activity.

[0041] The analysis unit can adjust the analysis algorithm by referring to past analysis data during analysis. The analysis unit can adjust the analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit selects an optimal algorithm based on past analysis data. Also, an optimal analysis method for a specific situation can be derived from past analysis data. Furthermore, the accuracy of the algorithm can be improved by analyzing past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to AI and cause AI to execute a process of adjusting the analysis algorithm. This makes it possible to optimize the analysis algorithm based on past analysis data.

[0042] The analysis unit can determine the priority of the analysis based on the user's current activity status during the analysis. The analysis unit determines the priority of the analysis based on the user's current activity status during the analysis. For example, when the user is walking, the analysis unit prioritizes the analysis of obstacles. Also, when the user is shopping, the analysis unit can prioritize the analysis of product location information. Furthermore, when the user is using public transportation, the analysis unit can prioritize the analysis of transfer information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's current activity status data into AI and cause AI to execute a process of determining the priority of the analysis. This makes it possible to determine the priority of the analysis based on the user's current activity status.

[0043] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. For example, when the user is in a specific location, the analysis unit prioritizes analysis of information related to that location. In addition, when the user is moving, the analysis unit can prioritize analysis of information related to the current location. Furthermore, when the user approaches the destination, the analysis unit can prioritize analysis of information in the surrounding area. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing to improve the accuracy of the analysis. This can improve the accuracy of the analysis based on the geographical location information of the user.

[0044] The analysis unit can analyze the user's social media activity and analyze the related information at the time of analysis. The analysis unit can analyze the user's social media activity and analyze the related information at the time of analysis. For example, if the user mentions a specific place on social media, the analysis unit prioritizes and analyzes information about the place. Also, if the user participates in a specific event on social media, the analysis unit can analyze information related to the event. Furthermore, the content of the user's social media posts can be analyzed and the related information can be analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and cause AI to execute a process of analyzing the related information. This makes it possible to analyze the related information based on the user's social media activity.

[0045] The providing unit can select optimal guide contents by referring to the user's past behavior history when providing the voice guide. The providing unit selects optimal guide contents by referring to the user's past behavior history when providing the voice guide. For example, the providing unit selects optimal guide contents based on information on places the user has visited in the past. Also, optimal guide contents for a specific situation can be selected from the user's past behavior history. Furthermore, the user's past behavior history can be analyzed to select the most efficient guide contents. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavior history data into AI and cause AI to execute a process of selecting optimal guide contents. This makes it possible to select optimal guide contents based on the user's past behavior history.

[0046] The providing unit can determine the priority of the guide based on the user's current activity status when providing the voice guide. The providing unit determines the priority of the guide based on the user's current activity status when providing the voice guide. For example, when the user is walking, the providing unit provides guidance by prioritizing information about obstacles. In addition, when the user is shopping, the providing unit can provide guidance by prioritizing product location information. Furthermore, when the user is using public transportation, the providing unit can provide guidance by prioritizing transfer information. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's current activity status data into AI and cause AI to execute processing for determining the priority of the guide. This makes it possible to determine the priority of the guide based on the user's current activity status.

[0047] The providing unit can provide optimal guide contents in consideration of the geographical location information of the user when providing the voice guide. The providing unit can provide optimal guide contents in consideration of the geographical location information of the user when providing the voice guide. For example, when the user is in a specific location, the providing unit provides guidance by giving priority to information related to that location. In addition, when the user is moving, the providing unit can provide guidance by giving priority to information related to the current location. Furthermore, when the user approaches the destination, the providing unit can provide guidance by giving priority to information about the surrounding area. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing for providing optimal guide contents. This makes it possible to provide optimal guide contents based on the geographical location information of the user.

[0048] The providing unit can analyze the user's social media activities and provide related guide content when providing the audio guide. The providing unit can analyze the user's social media activities and provide related guide content when providing the audio guide. For example, if the user mentions a specific place on social media, the providing unit provides priority guidance on information about that place. In addition, if the user is participating in a specific event on social media, the providing unit can provide information related to that event. Furthermore, the content posted on the user's social media can be analyzed and related information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause AI to execute processing to provide related guide content. This makes it possible to provide related guide content based on the user's social media activity.

[0049] The reception unit can select the optimal reception method by referring to the user's past voice command history when receiving voice. The reception unit selects the optimal reception method by referring to the user's past voice command history when receiving voice. For example, the reception unit preferentially recognizes voice commands that the user has used frequently in the past. In addition, the reception unit can select the optimal reception method in a specific situation from the user's past voice command history. Furthermore, the reception unit can analyze the user's past voice command history and select the most efficient reception method. A part or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's past voice command history data into AI and cause AI to execute a process of selecting the optimal reception method. In this way, the reception unit can select the optimal reception method based on the user's past voice command history.

[0050] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving the voice. The reception unit selects the optimal reception method in consideration of the geographical location information of the user when receiving the voice. For example, when the user is in a specific location, the reception unit preferentially receives voice commands related to the location. In addition, when the user is moving, the reception unit can preferentially receive voice commands related to the current location. Furthermore, when the user approaches the destination, the reception unit can preferentially receive voice commands related to information about the surrounding area. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause AI to execute processing to select the optimal reception method. In this way, the optimal reception method can be selected based on the geographical location information of the user.

[0051] The product information unit can select optimal information contents by referring to the user's past purchase history when providing product information. The product information unit selects optimal information contents by referring to the user's past purchase history when providing product information. For example, information related to products purchased by the user in the past is given priority. In addition, optimal information contents for a specific situation can be selected from the user's past purchase history. Furthermore, the user's past purchase history can be analyzed to select the most efficient information contents. A part or all of the above-mentioned processing in the product information unit may be performed using AI, or may be performed without using AI. For example, the product information unit can input the user's past purchase history data into AI and cause AI to execute a process of selecting optimal information contents. This makes it possible to select optimal information contents based on the user's past purchase history.

[0052] The product information unit can provide optimal guidance content in consideration of the user's geographical location information when providing product information. The product information unit can provide optimal guidance content in consideration of the user's geographical location information when providing product information. For example, when the user is in a specific location, the product information unit provides product information related to that location with priority. In addition, when the user is moving, the product information unit can provide product information related to the current location with priority. Furthermore, when the user approaches the destination, the product information unit can provide product information related to the surrounding information with priority. A part or all of the above-mentioned processing in the product information unit may be performed using AI, or may be performed without using AI. For example, the product information unit can input the user's geographical location information into AI and cause AI to execute processing to provide optimal guidance content. This makes it possible to provide optimal guidance content based on the user's geographical location information.

[0053] The traffic guidance unit can select optimal guidance content by referring to the user's past movement history when providing traffic guidance. The traffic guidance unit selects optimal guidance content by referring to the user's past movement history when providing traffic guidance. For example, the traffic guidance unit selects optimal guidance content based on the route used by the user in the past. In addition, optimal guidance content in a specific situation can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed to select the most efficient guidance content. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. For example, the traffic guidance unit can input the user's past movement history data into AI and cause AI to execute a process of selecting optimal guidance content. This makes it possible to select optimal guidance content based on the user's past movement history.

[0054] The traffic guidance unit can provide optimal guidance contents in consideration of the geographical location information of the user when providing traffic guidance. The traffic guidance unit provides optimal guidance contents in consideration of the geographical location information of the user when providing traffic guidance. For example, when the user is in a specific location, the traffic guidance unit provides traffic information related to that location with priority. In addition, when the user is moving, the traffic guidance unit can provide traffic information related to the current location with priority. Furthermore, when the user approaches the destination, the traffic guidance unit can provide traffic information related to the surrounding information with priority. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. For example, the traffic guidance unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing to provide optimal guidance contents. This makes it possible to provide optimal guidance contents based on the geographical location information of the user.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The visually impaired person support system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric information such as the user's heart rate and blood pressure, and transmits it to the analysis unit. The analysis unit analyzes the biometric information and evaluates the user's health condition. For example, if the user's heart rate is abnormally high, the analysis unit may provide audio guidance encouraging the user to take a break. Also, if the user's blood pressure is high, the analysis unit may provide audio guidance encouraging the user to visit a medical institution. In this way, the visually impaired person support system supports the user's health management, enabling them to live a safer daily life.

[0057] The visually impaired person support system may further include a schedule management unit that manages the user's schedule. The schedule management unit acquires the user's plans and transmits them to the analysis unit. The analysis unit provides audio guidance at appropriate times based on the user's schedule. For example, if the user is on his / her way to a meeting, the analysis unit may provide audio guidance that guides the user along the optimal route to arrive in time for the start of the meeting. The analysis unit may also provide guidance on the location of necessary products based on the user's planned shopping list. In this way, the visually impaired person support the user's schedule management and enable the user to live an efficient daily life.

[0058] The visually impaired person support system may further include a diet management unit that manages the user's diet. The diet management unit acquires the user's dietary content and transmits it to the analysis unit. The analysis unit analyzes the user's dietary content and evaluates the nutritional balance. For example, if the user is consuming too much of a particular nutrient, the analysis unit may provide audio guidance suggesting a balanced diet. It may also provide guidance on ingredients that can supplement the nutrient that the user is lacking. In this way, the visually impaired person support system supports the user's healthy eating habits and enables them to live a better life.

[0059] The visually impaired support system may further include an exercise management unit that manages the user's exercise. The exercise management unit acquires the amount and content of the user's exercise and transmits it to the analysis unit. The analysis unit analyzes the user's exercise data and proposes an appropriate exercise plan. For example, if the user is not getting enough exercise, the analysis unit may provide audio guidance encouraging moderate exercise. Also, if the user is exercising too much, the analysis unit may provide audio guidance recommending rest. In this way, the visually impaired support system supports the user's healthy exercise habits and enables them to live a better life.

[0060] The visually impaired support system may further include a hobby information providing unit that provides personalized information based on the user's hobbies and interests. The hobby information providing unit acquires the user's hobbies and interests and transmits them to the analysis unit. The analysis unit analyzes this information and provides information suitable for the user. For example, if the user is interested in a particular sport, the hobby information providing unit can provide the latest news and event information related to that sport. Also, if the user is interested in a particular music genre, the hobby information providing unit can provide new songs and artist information in that genre. In this way, the visually impaired support system can provide information according to the user's hobbies and interests, and support a more fulfilling daily life.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The acquisition unit acquires an image captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture an image of the surroundings using a camera and acquire the image as digital data. The acquisition unit can also optimize image acquisition using AI. Step 2: The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit, for example, recognizes objects in the image using AI and identifies information required by the user. The analysis unit can identify obstacles and destinations using object recognition technology. The analysis unit can also improve the accuracy of image analysis using AI. Step 3: The providing unit provides the user with an audio guide according to the analysis result by the analyzing unit. The providing unit uses, for example, voice synthesis technology to provide the user with a specific and easy-to-understand audio guide. The providing unit can also optimize the content of the audio guide using AI. For example, the providing unit provides the user with information to help them avoid obstacles while walking and directions to their destination by voice. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping, and a transportation guidance unit that supports the use of public transportation.

[0063] (Example 2) The system according to the embodiment of the present invention is an interactive voice-guided glasses that utilizes AI to support the lives of people with visual impairments and enable them to live more independent daily lives. This system acquires an image captured by an imaging unit provided in a camera worn by a user, and analyzes the image to provide a voice guide to the user. For example, a user wears glasses, and the camera captures an image of the surroundings. This image is acquired by an acquisition unit and sent to an analysis unit. The analysis unit uses AI to analyze the acquired image and identify information required by the user. For example, information for the user to avoid obstacles while walking and directions to the destination are included. Next, based on the analysis result, the provision unit provides a voice guide to the user. The voice guide is provided with specific and easy-to-understand content so that the user can easily understand the current situation. For example, instructions such as "There is an obstacle ahead. Please turn right." are included. In addition, a reception unit that accepts the user's voice is also provided. When a user issues a question or instruction by voice, the reception unit accepts the voice, and the analysis unit analyzes the image according to the voice. For example, when a user asks "Where is the next intersection?", the analysis unit performs an analysis according to the question, and the provision unit provides an appropriate voice guide. The providing unit also includes a product guidance unit that provides product location guidance while shopping, and a transportation guidance unit that assists in the use of public transportation. This allows the user to receive support while shopping and using public transportation. This system enables people with visual impairments to live more independent daily lives. For example, being guided to the location of products while shopping allows the user to shop smoothly, and when using public transportation, the user can know the appropriate timing for transfers and getting off. This allows people with visual impairments to live more independent daily lives.

[0064] The visually impaired person support system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires an image captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture an image of the surroundings using a camera and acquire the image as digital data. The acquisition unit can also optimize the acquisition of the image using AI. The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit can, for example, recognize objects in the image using AI to identify information required by the user. The analysis unit can identify obstacles and destinations using object recognition technology. The analysis unit can also improve the accuracy of image analysis using AI. The provision unit provides the user with audio guidance according to the analysis result by the analysis unit. The provision unit can, for example, use voice synthesis technology to provide the user with specific and easy-to-understand audio guidance. The provision unit can also optimize the content of the audio guidance using AI. For example, the provision unit provides information for the user to avoid obstacles while walking and route guidance to the destination by voice. This allows the visually impaired person support system to support the user to move safely and efficiently. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping, and a traffic guidance unit that supports the use of public transportation. The product guidance unit, for example, identifies the location of products in a store and provides audio guidance to the user. The traffic guidance unit, for example, provides transfer guidance and operation information, and supports the user when using public transportation. In this way, the visually impaired person support system enables the user to live a more independent daily life. In this way, the visually impaired person support system according to the embodiment enables visually impaired people to live a more independent daily life.

[0065] The acquisition unit acquires images captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture images of the surroundings using a camera and acquire the images as digital data. Specifically, the camera is worn on the user's glasses or hat, or hung from the neck, and always captures the user's line of sight. The camera has a high resolution and a wide-angle lens, and can cover a wide range of vision around the user. The captured images are transmitted to the acquisition unit in real time and stored as digital data. The acquisition unit can also optimize image acquisition using AI. For example, AI extracts important information from the captured images and filters unnecessary data. This allows the acquisition unit to efficiently collect necessary information and transmit it to the analysis unit. In addition, the acquisition unit has a function to automatically adjust the imaging conditions according to changes in the environment. For example, it can automatically adjust brightness and contrast to capture clear images even at night or in backlit conditions. This allows the acquisition unit to provide high-quality images to the user in any environment and improve the performance of the entire system.

[0066] The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit, for example, uses AI to recognize objects in the image and identify information required by the user. Specifically, the AI ​​uses a deep learning algorithm to classify objects in the image and identify obstacles, destinations, signs, etc. For example, in order for a walking user to avoid obstacles ahead, the AI ​​detects obstacles in the image and identifies their positions and sizes. In addition, in order to provide route guidance on the way to the destination, the AI ​​recognizes the characteristics of road signs and buildings and provides the user with the necessary information. The analysis unit can identify obstacles and destinations using object recognition technology. For example, the AI ​​recognizes pedestrians, vehicles, traffic lights, etc. in the image in real time and guides the user to a safe route. Furthermore, the analysis unit can also improve the accuracy of image analysis using AI. For example, the AI ​​learns past data and improves the accuracy of object recognition in a specific environment or situation. This allows the analysis unit to provide accurate information to the user no matter what situation he or she is in, thereby increasing the reliability of the visually impaired support system.

[0067] The providing unit provides the user with a voice guide according to the analysis result by the analyzing unit. The providing unit provides the user with a specific and easy-to-understand voice guide, for example, by using a voice synthesis technology. Specifically, the providing unit conveys appropriate instructions to the user by voice based on the information sent from the analyzing unit. For example, if the user is walking and there is an obstacle ahead, the providing unit provides specific instructions by voice, such as "There is an obstacle ahead. Please move to the right to avoid it." In addition, when providing route guidance on the way to the destination, detailed guidance such as "Turn left at the next intersection" is provided. The providing unit can also optimize the content of the voice guide using AI. For example, the timing and content of the voice guide are adjusted according to the user's walking speed and the surrounding environment. This allows the user to receive the voice guide without stress. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping and a transportation guidance unit that supports the use of public transportation. The product guidance unit, for example, identifies the location of a product in a store and provides guidance to the user by voice. Specifically, it provides guidance such as "The product on the shelf on the right is the product you are looking for." The traffic guidance unit provides, for example, transfer guidance and operation information to support the user when using public transportation. Specifically, it provides detailed guidance such as "Get off at the next station and move to platform 3." In this way, the visually impaired support system enables the user to live a more independent daily life, and people with visual impairments can live a more independent daily life.

[0068] The visually impaired support system includes a reception unit that receives the voice of a user. The reception unit receives the voice of the user. The reception unit, for example, collects the user's voice using a microphone and analyzes the voice command using a voice recognition technology. The reception unit can also improve the accuracy of the voice recognition by using AI. For example, when a user asks "Where is the next intersection?", the reception unit recognizes the voice and transmits it to the analysis unit. In this way, the reception unit enables the user to ask questions or give instructions by voice. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's voice data to AI and causes AI to perform voice recognition. In this way, by receiving the user's voice, the visually impaired support system enables the user to ask questions or give instructions by voice.

[0069] The analysis unit can analyze the image according to the voice received by the reception unit. The analysis unit analyzes the image according to the voice received by the reception unit. For example, when a user asks "Where is the next intersection?", the analysis unit identifies an intersection in the image according to the question and transmits it to the provision unit. The analysis unit can also select an analysis target based on a voice command using AI. For example, the analysis unit can input a voice command to AI and cause the AI ​​to select an analysis target. This allows the analysis unit to perform image analysis according to the voice. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. This enables image analysis according to voice.

[0070] The providing unit may include a product guidance unit that provides guidance on the location of a product during shopping. The providing unit includes a product guidance unit that provides guidance on the location of a product during shopping. The product guidance unit, for example, identifies the location of a product in a store and provides audio guidance to the user. The product guidance unit can also identify the location of a product using AI. For example, the product guidance unit can input location information of a product in a store to AI and cause AI to execute processing to identify the location of the product. This allows the providing unit to provide guidance on the location of a product during shopping. A part or all of the above-mentioned processing in the product guidance unit may be performed using AI, or may be performed without using AI. This allows guidance on the location of a product during shopping.

[0071] The providing unit may include a traffic guidance unit that supports the use of public transportation. The providing unit includes a traffic guidance unit that supports the use of public transportation. The traffic guidance unit provides, for example, transfer guidance and operation information, and supports the user when using public transportation. The traffic guidance unit can also analyze operation information using AI and provide optimal transfer guidance. For example, the traffic guidance unit can input operation information to AI and cause AI to execute optimal transfer guidance. This allows the providing unit to support the use of public transportation. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. This allows the use of public transportation to be supported.

[0072] The acquisition unit can estimate the user's emotion and adjust the timing of imaging based on the estimated user's emotion. The acquisition unit estimates the user's emotion and adjusts the timing of imaging based on the estimated user's emotion. For example, when the user is nervous, the acquisition unit frequently captures images and provides detailed information. Also, when the user is relaxed, the acquisition unit can reduce the frequency of imaging and provide only necessary information. Furthermore, when the user is in a hurry, the acquisition unit prioritizes imaging and provides important information quickly. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a process of adjusting the timing of imaging. This makes it possible to adjust the timing of imaging according to the user's emotion.

[0073] The acquisition unit can select an appropriate imaging angle by referring to the user's past behavior history when capturing an image. The acquisition unit selects an appropriate imaging angle by referring to the user's past behavior history when capturing an image. For example, if the user has preferred imaging at a particular angle in the past, the acquisition unit prioritizes that angle for imaging. It is also possible to capture an image while avoiding angles that the user has avoided in the past. Furthermore, it is also possible to select an optimal angle in a particular situation from the user's past behavior history. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past behavior history data into AI and cause AI to execute a process of selecting an optimal imaging angle. This makes it possible to select an optimal imaging angle based on the user's past behavior history.

[0074] The acquisition unit can analyze the surrounding environmental sound at the time of imaging, and automatically perform imaging when an important sound occurs. The acquisition unit can analyze the surrounding environmental sound at the time of imaging, and automatically perform imaging when an important sound occurs. For example, when a car horn sounds in the vicinity, the acquisition unit automatically performs imaging. Also, when a human voice is heard in the vicinity, the acquisition unit can also image the direction. Furthermore, when an alarm sound is sounded in the vicinity, the acquisition unit can also image the direction. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input surrounding environmental sound data to AI and cause AI to execute processing to detect important sounds. As a result, imaging can be automatically performed when an important sound occurs based on the surrounding environmental sound.

[0075] The acquisition unit can estimate the user's emotion and determine the priority of the images to be captured based on the estimated user's emotion. The acquisition unit estimates the user's emotion and determines the priority of the images to be captured based on the estimated user's emotion. For example, when the user is nervous, the acquisition unit prioritizes capturing important information. Also, when the user is relaxed, the acquisition unit can prioritize capturing detailed information. Furthermore, when the user is in a hurry, the acquisition unit prioritizes capturing information to be provided quickly. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a process of determining the priority of the images to be captured. This makes it possible to determine the priority of the images to be captured according to the user's emotion.

[0076] The acquisition unit can preferentially acquire related images in consideration of the geographical location information of the user when capturing an image. The acquisition unit preferentially acquires related images in consideration of the geographical location information of the user when capturing an image. For example, when the user is in a specific location, the acquisition unit preferentially captures information related to that location. In addition, when the user is moving, the acquisition unit can preferentially capture information related to the current location. Furthermore, when the user approaches the destination, the acquisition unit can preferentially capture information about the surrounding area. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute a process of preferentially acquiring related images. This makes it possible to preferentially acquire images that are highly related based on the geographical location information of the user.

[0077] The acquisition unit can analyze the user's social media activity at the time of imaging and acquire related images. The acquisition unit can analyze the user's social media activity at the time of imaging and acquire related images. For example, if the user mentions a specific place on social media, the acquisition unit preferentially acquires images of the place. Also, if the user participates in a specific event on social media, the acquisition unit can acquire images related to the event. Furthermore, the content of the user's social media posts can be analyzed to acquire related information. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into AI and cause AI to execute a process of acquiring related images. This makes it possible to acquire related images based on the user's social media activity.

[0078] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the accuracy of the analysis based on the estimated user's emotion. For example, if the user is nervous, the analysis unit performs a detailed analysis and provides accurate information. Also, if the user is relaxed, the analysis unit can perform a simplified analysis and provide only necessary information. Furthermore, if the user is in a hurry, the analysis unit performs a quick analysis and provides important information with priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the accuracy of the analysis. This makes it possible to adjust the accuracy of the analysis according to the user's emotion.

[0079] The analysis unit can adjust the analysis algorithm by referring to past analysis data during analysis. The analysis unit can adjust the analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit selects an optimal algorithm based on past analysis data. Also, an optimal analysis method for a specific situation can be derived from past analysis data. Furthermore, the accuracy of the algorithm can be improved by analyzing past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to AI and cause AI to execute a process of adjusting the analysis algorithm. This makes it possible to optimize the analysis algorithm based on past analysis data.

[0080] The analysis unit can determine the priority of the analysis based on the user's current activity status during the analysis. The analysis unit determines the priority of the analysis based on the user's current activity status during the analysis. For example, when the user is walking, the analysis unit prioritizes the analysis of obstacles. Also, when the user is shopping, the analysis unit can prioritize the analysis of product location information. Furthermore, when the user is using public transportation, the analysis unit can prioritize the analysis of transfer information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's current activity status data into AI and cause AI to execute a process of determining the priority of the analysis. This makes it possible to determine the priority of the analysis based on the user's current activity status.

[0081] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. Also, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the display method of the analysis result. This makes it possible to adjust the display method of the analysis result according to the user's emotion.

[0082] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. For example, when the user is in a specific location, the analysis unit prioritizes analysis of information related to that location. In addition, when the user is moving, the analysis unit can prioritize analysis of information related to the current location. Furthermore, when the user approaches the destination, the analysis unit can prioritize analysis of information in the surrounding area. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing to improve the accuracy of the analysis. This can improve the accuracy of the analysis based on the geographical location information of the user.

[0083] The analysis unit can analyze the user's social media activity and analyze the related information at the time of analysis. The analysis unit can analyze the user's social media activity and analyze the related information at the time of analysis. For example, if the user mentions a specific place on social media, the analysis unit prioritizes and analyzes information about the place. Also, if the user participates in a specific event on social media, the analysis unit can analyze information related to the event. Furthermore, the content of the user's social media posts can be analyzed and the related information can be analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and cause AI to execute a process of analyzing the related information. This makes it possible to analyze the related information based on the user's social media activity.

[0084] The providing unit can estimate the user's emotion and adjust the expression method of the voice guide based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the expression method of the voice guide based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide guidance in a calm voice. Also, if the user is relaxed, the providing unit can provide guidance in a bright voice. Furthermore, if the user is in a hurry, the providing unit can provide quick and concise voice guidance. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the expression method of the voice guide. This makes it possible to adjust the expression method of the voice guide according to the user's emotion.

[0085] The providing unit can select optimal guide contents by referring to the user's past behavior history when providing the voice guide. The providing unit selects optimal guide contents by referring to the user's past behavior history when providing the voice guide. For example, the providing unit selects optimal guide contents based on information on places the user has visited in the past. Also, optimal guide contents for a specific situation can be selected from the user's past behavior history. Furthermore, the user's past behavior history can be analyzed to select the most efficient guide contents. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavior history data into AI and cause AI to execute a process of selecting optimal guide contents. This makes it possible to select optimal guide contents based on the user's past behavior history.

[0086] The providing unit can determine the priority of the guide based on the user's current activity status when providing the voice guide. The providing unit determines the priority of the guide based on the user's current activity status when providing the voice guide. For example, when the user is walking, the providing unit provides guidance by prioritizing information about obstacles. In addition, when the user is shopping, the providing unit can provide guidance by prioritizing product location information. Furthermore, when the user is using public transportation, the providing unit can provide guidance by prioritizing transfer information. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's current activity status data into AI and cause AI to execute processing for determining the priority of the guide. This makes it possible to determine the priority of the guide based on the user's current activity status.

[0087] The providing unit can estimate the user's emotion and adjust the length of the audio guide based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the length of the audio guide based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides a short and to the point audio guide. Also, if the user is relaxed, the providing unit can provide a longer audio guide including detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a quick and concise audio guide. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the length of the audio guide. This makes it possible to adjust the length of the audio guide according to the user's emotion.

[0088] The providing unit can provide optimal guide contents in consideration of the geographical location information of the user when providing the voice guide. The providing unit can provide optimal guide contents in consideration of the geographical location information of the user when providing the voice guide. For example, when the user is in a specific location, the providing unit provides guidance by giving priority to information related to that location. In addition, when the user is moving, the providing unit can provide guidance by giving priority to information related to the current location. Furthermore, when the user approaches the destination, the providing unit can provide guidance by giving priority to information about the surrounding area. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing for providing optimal guide contents. This makes it possible to provide optimal guide contents based on the geographical location information of the user.

[0089] The providing unit can analyze the user's social media activities and provide related guide content when providing the audio guide. The providing unit can analyze the user's social media activities and provide related guide content when providing the audio guide. For example, if the user mentions a specific place on social media, the providing unit provides priority guidance on information about that place. In addition, if the user is participating in a specific event on social media, the providing unit can provide information related to that event. Furthermore, the content posted on the user's social media can be analyzed and related information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause AI to execute processing to provide related guide content. This makes it possible to provide related guide content based on the user's social media activity.

[0090] The reception unit can estimate the user's emotion and adjust the accuracy of voice reception based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the accuracy of voice reception based on the estimated user's emotion. For example, when the user is nervous, the reception unit increases the accuracy of voice recognition and reduces erroneous recognition. Also, when the user is relaxed, the reception unit can keep the accuracy of voice recognition normal. Furthermore, when the user is in a hurry, the reception unit can quickly recognize the voice and respond immediately. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a process of adjusting the accuracy of voice reception. This makes it possible to adjust the accuracy of voice reception according to the user's emotion.

[0091] The reception unit can select the optimal reception method by referring to the user's past voice command history when receiving voice. The reception unit selects the optimal reception method by referring to the user's past voice command history when receiving voice. For example, the reception unit preferentially recognizes voice commands that the user has used frequently in the past. In addition, the reception unit can select the optimal reception method in a specific situation from the user's past voice command history. Furthermore, the reception unit can analyze the user's past voice command history and select the most efficient reception method. A part or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's past voice command history data into AI and cause AI to execute a process of selecting the optimal reception method. In this way, the reception unit can select the optimal reception method based on the user's past voice command history.

[0092] The reception unit can estimate the user's emotion and determine the priority of voice reception based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of voice reception based on the estimated user's emotion. For example, when the user is nervous, the reception unit preferentially receives important voice commands. Also, when the user is relaxed, the reception unit can preferentially receive detailed voice commands. Furthermore, when the user is in a hurry, the reception unit can preferentially receive voice commands for quick response. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a process of determining the priority of voice reception. This makes it possible to determine the priority of voice reception according to the user's emotion.

[0093] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving the voice. The reception unit selects the optimal reception method in consideration of the geographical location information of the user when receiving the voice. For example, when the user is in a specific location, the reception unit preferentially receives voice commands related to the location. In addition, when the user is moving, the reception unit can preferentially receive voice commands related to the current location. Furthermore, when the user approaches the destination, the reception unit can preferentially receive voice commands related to information about the surrounding area. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause AI to execute processing to select the optimal reception method. In this way, the optimal reception method can be selected based on the geographical location information of the user.

[0094] The product information unit can estimate the user's emotions and adjust the product information method based on the estimated user's emotions. The product information unit can estimate the user's emotions and adjust the product information method based on the estimated user's emotions. For example, if the user is nervous, the product information unit provides a simple and highly visible information method. Also, if the user is relaxed, the product information unit can provide a information method including detailed information. Furthermore, if the user is in a hurry, the product information unit can provide a information method that focuses on the main points. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the product information unit may be performed using an AI, or may be performed without using an AI. For example, the product information unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the product information method. This makes it possible to adjust the product information method according to the user's emotions.

[0095] The product information unit can select optimal information contents by referring to the user's past purchase history when providing product information. The product information unit selects optimal information contents by referring to the user's past purchase history when providing product information. For example, information related to products purchased by the user in the past is given priority. In addition, optimal information contents for a specific situation can be selected from the user's past purchase history. Furthermore, the user's past purchase history can be analyzed to select the most efficient information contents. A part or all of the above-mentioned processing in the product information unit may be performed using AI, or may be performed without using AI. For example, the product information unit can input the user's past purchase history data into AI and cause AI to execute a process of selecting optimal information contents. This makes it possible to select optimal information contents based on the user's past purchase history.

[0096] The product information unit can estimate the user's emotion and determine the product information priority based on the estimated user's emotion. The product information unit can estimate the user's emotion and determine the product information priority based on the estimated user's emotion. For example, if the user is nervous, the product information unit can prioritize important product information. Also, if the user is relaxed, the product information unit can prioritize detailed product information. Furthermore, if the user is in a hurry, the product information unit can prioritize information for quick provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the product information unit may be performed using an AI, or may be performed without using an AI. For example, the product information unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process for determining the product information priority. This makes it possible to determine the product information priority according to the user's emotion.

[0097] The product information unit can provide optimal guidance content in consideration of the user's geographical location information when providing product information. The product information unit can provide optimal guidance content in consideration of the user's geographical location information when providing product information. For example, when the user is in a specific location, the product information unit provides product information related to that location with priority. In addition, when the user is moving, the product information unit can provide product information related to the current location with priority. Furthermore, when the user approaches the destination, the product information unit can provide product information related to the surrounding information with priority. A part or all of the above-mentioned processing in the product information unit may be performed using AI, or may be performed without using AI. For example, the product information unit can input the user's geographical location information into AI and cause AI to execute processing to provide optimal guidance content. This makes it possible to provide optimal guidance content based on the user's geographical location information.

[0098] The traffic guidance unit can estimate the user's emotion and adjust the traffic guidance method based on the estimated user's emotion. The traffic guidance unit can estimate the user's emotion and adjust the traffic guidance method based on the estimated user's emotion. For example, if the user is nervous, the traffic guidance unit provides a simple and highly visible guidance method. Also, if the user is relaxed, the traffic guidance unit can provide a guidance method including detailed information. Furthermore, if the user is in a hurry, the traffic guidance unit can provide a guidance method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using an AI, or may be performed without using an AI. For example, the traffic guidance unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute a process of adjusting the traffic guidance method. This makes it possible to adjust the traffic guidance method according to the user's emotion.

[0099] The traffic guidance unit can select optimal guidance content by referring to the user's past movement history when providing traffic guidance. The traffic guidance unit selects optimal guidance content by referring to the user's past movement history when providing traffic guidance. For example, the traffic guidance unit selects optimal guidance content based on the route used by the user in the past. In addition, optimal guidance content in a specific situation can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed to select the most efficient guidance content. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. For example, the traffic guidance unit can input the user's past movement history data into AI and cause AI to execute a process of selecting optimal guidance content. This makes it possible to select optimal guidance content based on the user's past movement history.

[0100] The traffic guidance unit can estimate the user's emotion and determine the priority of traffic guidance based on the estimated user's emotion. The traffic guidance unit can estimate the user's emotion and determine the priority of traffic guidance based on the estimated user's emotion. For example, if the user is nervous, the traffic guidance unit can prioritize important traffic information. Also, if the user is relaxed, the traffic guidance unit can prioritize detailed traffic information. Furthermore, if the user is in a hurry, the traffic guidance unit can prioritize information for quick provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using an AI, or may be performed without using an AI. For example, the traffic guidance unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a process of determining the priority of traffic guidance. This makes it possible to determine the priority of traffic guidance according to the user's emotion.

[0101] The traffic guidance unit can provide optimal guidance contents in consideration of the geographical location information of the user when providing traffic guidance. The traffic guidance unit provides optimal guidance contents in consideration of the geographical location information of the user when providing traffic guidance. For example, when the user is in a specific location, the traffic guidance unit provides traffic information related to that location with priority. In addition, when the user is moving, the traffic guidance unit can provide traffic information related to the current location with priority. Furthermore, when the user approaches the destination, the traffic guidance unit can provide traffic information related to the surrounding information with priority. A part or all of the above-mentioned processing in the traffic guidance unit may be performed using AI, or may be performed without using AI. For example, the traffic guidance unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute processing to provide optimal guidance contents. This makes it possible to provide optimal guidance contents based on the geographical location information of the user.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The visually impaired person support system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric information such as the user's heart rate and blood pressure, and transmits it to the analysis unit. The analysis unit analyzes the biometric information and evaluates the user's health condition. For example, if the user's heart rate is abnormally high, the analysis unit may provide audio guidance encouraging the user to take a break. Also, if the user's blood pressure is high, the analysis unit may provide audio guidance encouraging the user to visit a medical institution. In this way, the visually impaired person support system supports the user's health management, enabling them to live a safer daily life.

[0104] The visually impaired person support system may further include a schedule management unit that manages the user's schedule. The schedule management unit acquires the user's plans and transmits them to the analysis unit. The analysis unit provides audio guidance at appropriate times based on the user's schedule. For example, if the user is on his / her way to a meeting, the analysis unit may provide audio guidance that guides the user along the optimal route to arrive in time for the start of the meeting. The analysis unit may also provide guidance on the location of necessary products based on the user's planned shopping list. In this way, the visually impaired person support the user's schedule management and enable the user to live an efficient daily life.

[0105] The visually impaired person support system may further include a music providing unit that estimates the user's emotions and provides music based on the estimated emotions. The music providing unit receives the user's emotions from the analysis unit, and selects and provides appropriate music. For example, if the user is nervous, the music providing unit may provide music that has a relaxing effect. Also, if the user is relaxed, the music providing unit may provide music that lifts the mood. Furthermore, if the user is sad, the music providing unit may provide music that soothes the mood. In this way, the visually impaired person support system can provide music that corresponds to the user's emotions and create a comfortable environment.

[0106] The visually impaired person support system may further include a diet management unit that manages the user's diet. The diet management unit acquires the user's dietary content and transmits it to the analysis unit. The analysis unit analyzes the user's dietary content and evaluates the nutritional balance. For example, if the user is consuming too much of a particular nutrient, the analysis unit may provide audio guidance suggesting a balanced diet. It may also provide guidance on ingredients that can supplement the nutrient that the user is lacking. In this way, the visually impaired person support system supports the user's healthy eating habits and enables them to live a better life.

[0107] The visually impaired support system may further include an exercise management unit that manages the user's exercise. The exercise management unit acquires the amount and content of the user's exercise and transmits it to the analysis unit. The analysis unit analyzes the user's exercise data and proposes an appropriate exercise plan. For example, if the user is not getting enough exercise, the analysis unit may provide audio guidance encouraging moderate exercise. Also, if the user is exercising too much, the analysis unit may provide audio guidance recommending rest. In this way, the visually impaired support system supports the user's healthy exercise habits and enables them to live a better life.

[0108] The visually impaired support system may further include a relaxation suggestion unit that estimates the user's emotions and suggests a relaxation method based on the estimated emotions. The relaxation suggestion unit receives the user's emotions from the analysis unit and suggests an appropriate relaxation method. For example, if the user is tense, the relaxation suggestion unit may suggest deep breathing or meditation. Also, if the user is relaxed, the relaxation suggestion unit may suggest light stretching. Furthermore, if the user is tired, the relaxation suggestion unit may suggest a short break. In this way, the visually impaired support system can suggest a relaxation method according to the user's emotions and support mental and physical health.

[0109] The visually impaired support system may further include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on the estimated emotions. The communication adjustment unit receives the user's emotions from the analysis unit and selects an appropriate communication method. For example, if the user is nervous, the communication adjustment unit can speak to the user in a calm tone. Also, if the user is relaxed, the communication adjustment unit can speak to the user in a bright tone. Furthermore, if the user is angry, the communication adjustment unit can speak to the user in a calm tone. In this way, the visually impaired support system can provide a communication method according to the user's emotions and support smooth dialogue.

[0110] The visually impaired support system may further include an entertainment providing unit that estimates the user's emotions and provides entertainment based on the estimated emotions. The entertainment providing unit receives the user's emotions from the analysis unit, and selects and provides appropriate entertainment. For example, if the user is sad, the entertainment providing unit may provide movies or music that soothe the mood. Also, if the user is relaxed, the entertainment providing unit may provide fun games or activities. Furthermore, if the user is excited, the entertainment providing unit may provide content that has a relaxing effect. In this way, the visually impaired support system provides entertainment according to the user's emotions, allowing the user to spend a comfortable time.

[0111] The visually impaired support system may further include a learning support unit that estimates the user's emotions and adjusts the learning content based on the estimated emotions. The learning support unit receives the user's emotions from the analysis unit, and selects and provides appropriate learning content. For example, if the user is nervous, the learning support unit may suggest a learning method that has a relaxing effect. Also, if the user is relaxed, the learning support unit may suggest a learning method that improves concentration. Furthermore, if the user is tired, the learning support unit may suggest a learning method that includes short breaks. In this way, the visually impaired support system may provide learning support according to the user's emotions and support effective learning.

[0112] The visually impaired support system may further include a hobby information providing unit that provides personalized information based on the user's hobbies and interests. The hobby information providing unit acquires the user's hobbies and interests and transmits them to the analysis unit. The analysis unit analyzes this information and provides information suitable for the user. For example, if the user is interested in a particular sport, the hobby information providing unit can provide the latest news and event information related to that sport. Also, if the user is interested in a particular music genre, the hobby information providing unit can provide new songs and artist information in that genre. In this way, the visually impaired support system can provide information according to the user's hobbies and interests, and support a more fulfilling daily life.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The acquisition unit acquires an image captured by an imaging unit provided in a camera worn by a user. The acquisition unit can, for example, capture an image of the surroundings using a camera and acquire the image as digital data. The acquisition unit can also optimize image acquisition using AI. Step 2: The analysis unit analyzes the image acquired by the acquisition unit. The analysis unit, for example, recognizes objects in the image using AI and identifies information required by the user. The analysis unit can identify obstacles and destinations using object recognition technology. The analysis unit can also improve the accuracy of image analysis using AI. Step 3: The providing unit provides the user with an audio guide according to the analysis result by the analyzing unit. The providing unit uses, for example, voice synthesis technology to provide the user with a specific and easy-to-understand audio guide. The providing unit can also optimize the content of the audio guide using AI. For example, the providing unit provides the user with information to help them avoid obstacles while walking and directions to their destination by voice. Furthermore, the providing unit includes a product guidance unit that provides guidance on the location of products while shopping, and a transportation guidance unit that supports the use of public transportation.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0116] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] For example, the acquisition unit is realized by the camera 42 of the smart device 14 and acquires an image captured by a camera worn by the user. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the acquired image and identifies information required by the user. The provision unit is realized by, for example, the control unit 46A of the smart device 14 and provides audio guidance based on the analysis result. The reception unit is realized by, for example, the microphone 38B of the smart device 14 and accepts the user's voice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] Fig. 4 shows an example of 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.

[0127] 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.

[0128] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the acquisition unit is realized by the camera 42 of the smart glasses 214 and acquires an image captured by a camera worn by the user. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the acquired image and identifies information required by the user. The provision unit is realized by, for example, the control unit 46A of the smart glasses 214 and provides audio guidance based on the analysis result. The acceptance unit is realized by, for example, the microphone 238 of the smart glasses 214 and accepts the user's voice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] 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 performed in a secure state.

[0142] 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.

[0143] 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.

[0144] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 voice indicating a user input for 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the acquisition unit is realized by the camera 42 of the headset type terminal 314, and acquires an image captured by a camera worn by the user. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the acquired image and identifies information required by the user. The provision unit is realized by, for example, the control unit 46A of the headset type terminal 314, and provides audio guidance based on the analysis results. The acceptance unit is realized by, for example, the microphone 238 of the headset type terminal 314, and accepts the user's voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0154] 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.

[0155] 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 the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] 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 performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0159] 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.

[0160] 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.

[0161] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0163] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] 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 a voice indicating a user input for 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.

[0165] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0167] For example, the acquisition unit is realized by the camera 42 of the robot 414, and acquires an image captured by a camera worn by the user. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the acquired image and identifies information required by the user. The provision unit is realized by, for example, the control unit 46A of the robot 414, and provides audio guidance based on the analysis results. The reception unit is realized by, for example, the microphone 238 of the robot 414, and accepts the user's voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0175] 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.

[0176] 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 Universal Serial Bus (USB) 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.

[0177] In addition, 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 upon request from the data processing device 12.

[0178] 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.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0181] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) an acquisition unit that acquires an image captured by an imaging unit provided in a camera worn by a user; an analysis unit that analyzes the image acquired by the acquisition unit; a providing unit that provides the user with a voice guide according to the analysis result by the analyzing unit. A system characterized by: (Appendix 2) A reception unit for receiving the user's voice 2. The system of claim 1, (Appendix 3) The analysis unit is The image is analyzed according to the voice received by the reception unit. 2. The system of claim 1, (Appendix 4) The providing unit is Equipped with a product guide section that guides the location of products while shopping 2. The system of claim 1, (Appendix 5) The providing unit is Equipping a transportation information section to assist with the use of public transportation 2. The system of claim 1, (Appendix 6) The acquisition unit is To estimate a user's emotion and adjust the timing of image capture based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The acquisition unit is When capturing an image, the system selects the appropriate capture angle by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 8) The acquisition unit is When capturing an image, the camera analyzes the surrounding environmental sounds and automatically captures an image when an important sound occurs. 2. The system of claim 1, (Appendix 9) The acquisition unit is A user's emotion is estimated, and a priority order of images to be captured is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The acquisition unit is When capturing images, the system takes into account the user's geographic location information to preferentially obtain relevant images. 2. The system of claim 1, (Appendix 11) The acquisition unit is When capturing images, the app analyzes the user's social media activity to retrieve relevant images. 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the analysis algorithm is adjusted by referring to past analysis data. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on the user's current activity 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the geographic location of the user is taken into account to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, analyze users' social media activity and parse related information 2. The system of claim 1, (Appendix 18) The providing unit is Estimating the user's emotions and adjusting the way audio guide is expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The providing unit is When providing audio guidance, the system selects the most appropriate guidance content by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 20) The providing unit is When providing audio guidance, prioritize guidance based on the user's current activity 2. The system of claim 1, (Appendix 21) The providing unit is Estimate the user's emotion and adjust the length of the audio guide based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The providing unit is When providing audio guidance, the system provides optimal guidance content by taking into account the user's geographical location information. 2. The system of claim 1, (Appendix 23) The providing unit is When providing audio guides, analyze users' social media activity to provide relevant guide content 2. The system of claim 1, (Appendix 24) The reception unit is To estimate a user's emotion and adjust the accuracy of voice reception based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The reception unit is When accepting a voice call, the system selects the optimal method of acceptance by referring to the user's past voice command history. 2. The system of claim 1, (Appendix 26) The reception unit is To estimate a user's emotion and determine a priority of voice reception based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The reception unit is When accepting a voice call, the optimal reception method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 28) The product information section includes: To estimate a user's emotion and adjust a method of introducing a product based on the estimated user's emotion. 4. The system of claim 3. (Appendix 29) The product information section includes: When providing product information, select the most appropriate information by referring to the user's past purchase history 4. The system of claim 3. (Appendix 30) The product information section includes: To estimate a user's emotion and determine a priority order for product information based on the estimated user's emotion. 4. The system of claim 3. (Appendix 31) The product information section includes: When providing product information, provide optimal information by considering the user's geographic location information 4. The system of claim 3. (Appendix 32) The traffic information section includes: To estimate a user's emotion and adjust a method of providing traffic guidance based on the estimated user's emotion. 5. The system of claim 4. (Appendix 33) The traffic information section includes: When providing traffic guidance, the system selects the most appropriate guidance content by referring to the user's past travel history. 5. The system of claim 4. (Appendix 34) The traffic information section includes: Estimating user emotions and prioritizing traffic guidance based on the estimated user emotions 5. The system of claim 4. (Appendix 35) The traffic information section includes: When providing traffic guidance, the system provides optimal guidance by taking into account the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0187] 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 an imaging unit provided in a camera worn by a user; an analysis unit that analyzes the image acquired by the acquisition unit; a providing unit that provides the user with a voice guide according to the analysis result by the analyzing unit. A system characterized by:

2. A reception unit for receiving the user's voice 2. The system of claim 1 .

3. The analysis unit is The image is analyzed according to the voice received by the reception unit.

3. The system of claim 2.

4. The providing unit is Equipped with a product guide section that guides the location of products while shopping 2. The system of claim 1 .

5. The providing unit is Equipping a transportation information section to assist with the use of public transportation 2. The system of claim 1 .

6. The acquisition unit is Estimating the user's emotion and adjusting timing of image capture based on the estimated user's emotion.

2. The system of claim 1 .

7. The acquisition unit is When capturing an image, an appropriate capturing angle is selected by referring to the user's past behavior history.

2. The system of claim 1 .

8. The acquisition unit is When capturing an image, the camera analyzes the surrounding environmental sounds and automatically captures an image when an important sound occurs.

2. The system of claim 1 .

9. The acquisition unit is Estimating the emotion of the user and determining a priority order of images to be captured based on the estimated emotion of the user.

2. The system of claim 1 .

Images