System

Abstract

To provide a system for providing a visually handicapped person with supports such as guiding directions and reading a book for him / her when going out.SOLUTION: A system comprises an acquisition part, a generation part, a provision part, a reception part, a response generation part, a response provision part, a book reception part, and a reading provision part. The acquisition part grasps a current location. The generation part calculates a route to a destination on the basis of information acquired by the acquisition part. The provision part guides directions on the basis of the route generated by the generation part. The reception part receives input by a user. The response generation part generates a response on the basis of the information received by the reception part. The response provision part provides the response generated by the response generation part. The book reception part receives the title of a book to be read. The reading provision part reads contents on the basis of the information received by the book reception part.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] With conventional technology, the means by which visually impaired people can receive support when they go out, such as directions or having someone read a book to them, are limited, and there is room for improvement.

[0005] The system according to the embodiment is intended to provide support to visually impaired people when they go out, such as directions or reading books to them. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response providing unit, this reception unit, and a read-aloud providing unit. The acquisition unit grasps the current location. The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. The provision unit provides directions based on the route generated by the generation unit. The reception unit accepts user input. The response generation unit generates a response based on the information accepted by the reception unit. The response providing unit provides the response generated by the response generation unit. The reception unit accepts the title of a book to be read. The read-aloud providing unit reads out the contents based on the information accepted by the reception unit. Effect of the Invention

[0007] The system according to the embodiment enables visually impaired people to receive support when they go out, such as directions or having books read to them. [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 wearable device according to the embodiment of the present invention is a system that not only guides visually impaired people when they go out, but also serves as a conversation partner and reads books they want to read. When a visually impaired person goes out, this system grasps their current location and provides directions to their destination. When a user inputs a destination by voice, the generation AI calculates the optimal route and provides voice guidance. For example, specific instructions such as "Turn right at the next intersection" are provided. Next, the system has a function as a conversation partner. When a user speaks to the system, the generation AI responds appropriately and the conversation can continue. This allows visually impaired people to go out without feeling lonely and with peace of mind. Furthermore, the system has a function of reading books they want to read. When a user inputs the title of a book they want to read by voice, the generation AI reads out the contents of the book. This allows visually impaired people to enjoy reading freely. In this way, the wearable device of the present invention has multiple functions such as guidance for visually impaired people when they go out, a conversation partner, and reading books they want to read, and functions as a substitute or complement for a guide dog. This allows the wearable device to guide visually impaired people when they go out, be a conversation partner, and read books they want to read.

[0029] The wearable device according to the embodiment includes an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, the reception unit, and a reading-out provision unit. The acquisition unit grasps the current location when a visually impaired person goes out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. The generation unit calculates a route to a destination based on the information acquired by the acquisition unit. For example, a route considering the shortest distance, the shortest time, and traffic conditions can be calculated using a generation AI. The provision unit provides route guidance based on the route generated by the generation unit. For example, route guidance can be provided by a method such as voice guidance, vibration guidance, and visual guidance. The reception unit accepts user input. For example, user input can be accepted by a method such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information accepted by the reception unit. For example, a response can be generated by a method such as a voice response, a text response, and a vibration response using a generation AI. The response provision unit provides the response generated by the response generation unit. For example, a response can be provided by a method such as a voice response, a text response, and a vibration response. The reception unit receives the title of a book that the user wants to read. For example, the title of the book can be received by voice input, text input, or other methods. The read-aloud provider reads the contents based on the information received by the reception unit. For example, the read-aloud provider can use voice synthesis technology. This allows visually impaired people to be given directions, have someone to talk to, and read books for them when they go out.

[0030] The acquisition unit grasps the current location of the visually impaired person when they go out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. Specifically, GPS receives signals from satellites to identify the current location with high accuracy, and Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information identifies the rough location using signals from mobile phone base stations. By combining these technologies, highly accurate location information can be acquired both indoors and outdoors. Furthermore, the acquisition unit can collect not only location information but also surrounding environmental information. For example, environmental data such as surrounding sounds, vibrations, temperature, and humidity can be acquired through sensors to help the visually impaired person move safely. As a result, the acquisition unit can comprehensively collect information necessary for the visually impaired person when they go out and provide it in real time.

[0031] The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can be used to calculate a route that takes into account the shortest distance, the shortest time, and traffic conditions. Specifically, the generation AI analyzes map data between the current location and the destination to generate an optimal route. By taking into account traffic conditions, road congestion, construction information, etc., the generation AI can provide an individually optimized route by learning the past movement history and the user's preferences. For example, the generation AI can propose a safer and more comfortable route by taking into account routes frequently used by visually impaired people and places they want to avoid. Furthermore, the generation unit can recalculate the route based on information updated in real time. For example, if a sudden event such as a traffic accident or road closure occurs, the generation AI can instantly calculate a new route and notify the visually impaired person. This allows the generation unit to support the visually impaired person to always travel on the optimal route.

[0032] The providing unit performs route guidance based on the route generated by the generating unit. For example, route guidance can be performed using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, the voice guidance informs the visually impaired person of the direction and distance to go next, obstacles to be careful of, and the like by voice. The vibration guidance indicates the direction and distance by vibrating the device, and the visually impaired person can feel it with their hands or arms. When used by a visually impaired person, the visual guidance can provide information using a braille display or large character display. In this way, the providing unit supports the visually impaired person to reach the destination safely and reliably. In addition, the providing unit can customize the guidance method according to the user's preferences and circumstances. For example, for a user who is not good at voice guidance, vibration guidance can be given priority, and a combination of multiple guidance methods can be provided. In addition, the providing unit can modify the guidance content based on information updated in real time. For example, if a new obstacle occurs on the route, the providing unit immediately provides new guidance so that the visually impaired person can move safely. In this way, the providing unit can provide an environment in which the visually impaired person can go out with peace of mind.

[0033] The reception unit accepts user input. For example, the reception unit can accept user input by voice input, touch input, gesture input, and other methods. Specifically, the voice input is a method in which a visually impaired person operates the device by speaking to it, and the user's instructions are accurately understood using voice recognition technology. The touch input is a method in which an operation is performed by touching the device's touch screen with a finger, and is designed so that a visually impaired person can confirm the operation by touch. The gesture input is a method in which a camera or sensor in the device is used to detect the movement of the hand or arm to perform an operation, and a visually impaired person can operate the device with a natural movement. In this way, the reception unit supports a visually impaired person to operate the device intuitively and easily. In addition, the reception unit can quickly and accurately process the user's input and transmit the information to other departments. For example, when a destination is specified by voice input, the reception unit transmits the information to the generation unit and starts route calculation. In this way, the reception unit can provide an environment in which a visually impaired person can smoothly use the device.

[0034] The response generation unit generates a response based on the information received by the reception unit. For example, the generation AI can be used to generate responses by voice response, text response, vibration response, or other methods. Specifically, the generation AI analyzes the user's input content and generates an appropriate response. Voice response is a method of providing information to visually impaired people by voice, and uses voice synthesis technology to generate natural speech. Text response is a method that allows visually impaired people to check information using a braille display or screen reader, and the generation AI generates appropriate text. Vibration response is a method of conveying information by vibrating a device, and visually impaired people can receive information by touch. In this way, the response generation unit helps visually impaired people to receive the information they need quickly and accurately. In addition, the response generation unit can customize the response content according to the user's preferences and circumstances. For example, for users who are not good at voice response, text response can be prioritized and multiple response methods can be provided in combination. In addition, the response generation unit can modify the response content based on information updated in real time. In this way, the response generation unit can provide an environment in which visually impaired people can always receive the latest information.

[0035] The response providing unit provides the response generated by the response generating unit. For example, the response can be provided by a method such as a voice response, a text response, or a vibration response. Specifically, the voice response is a method of providing information to a visually impaired person by voice, and the generated voice is reproduced through a speaker or earphone. The text response is a method of enabling a visually impaired person to confirm information using a braille display or a screen reader, and displays the generated text. The vibration response is a method of conveying information by vibrating a device, and the visually impaired person can receive information by touch. In this way, the response providing unit supports the visually impaired person to receive the necessary information quickly and accurately. In addition, the response providing unit can customize the response method according to the user's preferences and circumstances. For example, for a user who is not good at voice response, a text response can be given priority, and a combination of multiple response methods can be provided. In addition, the response providing unit can modify the response content based on information updated in real time. In this way, the response providing unit can provide an environment in which a visually impaired person can always receive the latest information.

[0036] This reception unit accepts the title of a book to be read. For example, the title of the book can be accepted by voice input, text input, or other methods. Specifically, voice input is a method in which a visually impaired person specifies the title of a book by speaking to a device, and the user's instructions are accurately understood using voice recognition technology. Text input is a method in which a visually impaired person inputs the title of a book using a braille display or screen reader, and is designed so that a visually impaired person can confirm the operation by touch. In this way, this reception unit supports visually impaired people to intuitively and easily specify the title of a book they want to read. In addition, this reception unit can quickly and accurately process the user's input and transmit information to other departments. For example, when a book title is specified by voice input, this reception unit transmits the information to the reading-aloud providing unit and starts reading aloud. In this way, this reception unit can provide an environment in which a visually impaired person can smoothly specify a book they want to read.

[0037] The reading-out providing unit reads out the contents based on the information received by the receiving unit. For example, reading out can be performed using a voice synthesis technology. Specifically, the voice synthesis technology is a technology that analyzes text data and generates natural speech, and provides the contents of the book by voice to the visually impaired. The reading-out providing unit can adjust the reading speed and tone of the voice according to the user's preferences and circumstances. For example, the reading speed can be increased for a user who prefers a fast reading speed, and can be decreased for a user who prefers a slow reading speed. In addition, the reading-out providing unit can modify the reading out contents based on information updated in real time. For example, when the contents of the book are updated, the reading-out providing unit immediately reads out the new contents. In this way, the reading-out providing unit can provide an environment in which the visually impaired can always receive the latest information. In addition, the reading-out providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the reading out contents. In this way, the reading-out providing unit can provide an environment in which the visually impaired can comfortably enjoy books.

[0038] The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has a function of detecting and avoiding obstacles when a visually impaired person goes out. For example, an obstacle can be detected using an ultrasonic sensor, a camera, LIDAR, or the like. The detection unit can avoid obstacles by changing the direction of travel or adjusting the speed. For example, an ultrasonic sensor can be used to detect an obstacle ahead and change the direction of travel. In addition, a camera can be used to visually detect an obstacle and adjust the speed. Furthermore, LIDAR can be used to scan the surrounding environment in detail and avoid obstacles. This makes it easier for visually impaired people to avoid obstacles. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from an ultrasonic sensor or a camera to the generation AI and cause the generation AI to detect and avoid obstacles.

[0039] The wearable device includes a feedback unit that provides vibration or haptic feedback. The feedback unit has a function for visually impaired people to receive feedback when they go out. For example, the feedback can be provided by adjusting the intensity or pattern of vibration. The feedback unit can also provide feedback using a haptic device. For example, a vibration motor can be used to provide vibration in a specific pattern. The haptic device can also be used to make the visually impaired people feel changes in pressure or temperature. This makes it easier for visually impaired people to receive feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user input and environmental data to the generation AI and cause the generation AI to execute optimal feedback.

[0040] The wearable device has a function of reading out a specific chapter or page. The reading out function has a function that allows visually impaired people to select a specific chapter or page and have it read out. For example, a chapter number or a page number can be specified to read out. The reading out function can also select a specific chapter or page using a keyword search. For example, if a user inputs "Please read out Chapter 3" by voice, the specified chapter will be read out. Also, if a user inputs "Please read out a page that contains the keyword 'adventure'", the corresponding page can be read out. This allows visually impaired people to select a specific chapter or page and have it read out. Some or all of the above-mentioned processing in the reading out function may be performed, for example, using AI, or may be performed without using AI. For example, the reading out function can input a user's voice input to a generation AI and have the generation AI execute reading out a specified chapter or page.

[0041] The wearable device has a function for supporting multiple languages. The multiple language support function has a function for visually impaired people to obtain information in multiple languages. For example, it can support languages ​​such as English, Japanese, and Spanish. The multiple language support function can accept voice input and text input in multiple languages. For example, when a user inputs in English, a response in English is provided. Also, when a user inputs in Japanese, a response in Japanese can be provided. Furthermore, the multiple language support function can read out in multiple languages ​​using speech synthesis technology. For example, when reading out an English book, English speech synthesis is used, and when reading out a Japanese book, Japanese speech synthesis is used. This allows visually impaired people to obtain information in multiple languages. Part or all of the above-mentioned processing in the multiple language support function may be performed, for example, using AI, or may be performed without using AI. For example, the multiple language support function can input a user's input to a generation AI and have the generation AI execute responses and reading out in multiple languages.

[0042] The wearable device includes a providing unit that acquires the latest information online and provides it to the user. The providing unit has a function for visually impaired people to acquire the latest information in real time. For example, it can acquire real-time data through the Internet and provide it to the user. The providing unit can perform regular updates and always provide the latest information. For example, it acquires the latest information such as news, weather forecasts, and traffic information and provides it to the user. The providing unit can also acquire and provide specific information in response to a user's request. For example, when a user voice-inputs "Please tell me the latest news," the latest news is acquired and read out. This allows visually impaired people to acquire the latest information in real time. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data acquired from the Internet to a generating AI and cause the generating AI to execute information to be provided to the user.

[0043] The acquisition unit can analyze the user's past movement history and select the optimal current location acquisition method. The acquisition unit can analyze the user's past movement history using, for example, a GPS log or a record of a movement route. For example, the accuracy of current location acquisition can be adjusted based on places that the user has frequently visited in the past. In addition, the acquisition unit can analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify a place where the user got lost in the past and increase the frequency of current location acquisition in the vicinity. This can improve the accuracy of current location acquisition based on the past movement history. Part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input past movement data to the generation AI and cause the generation AI to select the optimal current location acquisition method.

[0044] The acquisition unit can improve the accuracy of acquiring the current location by considering the moving speed and direction of the user. The acquisition unit acquires the moving speed of the user using, for example, a GPS speed or an acceleration sensor. For example, when the user is moving at high speed, the acquisition accuracy of the current location is improved. In addition, the moving direction of the user can be acquired using a compass or a gyro sensor, and when the user frequently changes direction, the acquisition frequency of the current location can be increased. Furthermore, when the user is walking, the acquisition accuracy of the current location can be adjusted according to the moving speed. As a result, the accuracy of the current location acquisition is improved by considering the moving speed and direction. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input data on the moving speed and direction to the generation AI and cause the generation AI to execute the improvement of the accuracy of the current location acquisition.

[0045] When acquiring the current location, the acquisition unit can preferentially acquire highly relevant information by considering the geographical location information of the user. The acquisition unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, information on tourist spots can be preferentially acquired. In addition, when the user is in a commercial facility, store information can also be preferentially acquired. Furthermore, when the user is using public transportation, operation information can also be preferentially acquired. In this way, by considering the geographical location information, highly relevant information can be preferentially acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0046] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the current location. The acquisition unit, for example, analyzes the user's social media activity. For example, it acquires information on the location where the user checked in on social media. It can also acquire the current location based on the location information of the photo the user shared on social media. It can also preferentially acquire information on the location the user follows on social media. This makes it possible to acquire related information by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to acquire related information.

[0047] The generation unit can predict the optimal route by referring to past movement data when calculating the route. The generation unit can refer to past movement data using, for example, a GPS log or a record of a movement route. For example, the generation unit can predict the optimal route based on a route used by the user in the past. In addition, the generation unit can predict a route that avoids congestion from the user's past movement data. Furthermore, the generation unit can predict the most efficient route by analyzing the user's past movement data. In this way, the generation unit can predict the optimal route by referring to the past movement data. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past movement data to the generation AI and cause the generation AI to execute prediction of the optimal route.

[0048] The generation unit can generate an optimal route by considering traffic conditions and weather information when calculating a route. The generation unit generates an optimal route based on, for example, real-time traffic congestion information. For example, a route that avoids congestion is calculated using traffic information obtained from a traffic sensor or the Internet. The generation unit can also generate an optimal route by considering real-time weather information. For example, a route that avoids rain and snow is calculated based on weather data. Furthermore, the optimal route can be generated by considering the real-time operation status of public transportation. For example, the most efficient route is calculated based on operation information. In this way, the optimal route can be generated by considering traffic conditions and weather information. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input traffic information and weather information to the generation AI and cause the generation AI to generate an optimal route.

[0049] The generation unit can generate an optimal route by taking into account the geographical location information of the user when calculating the route. The generation unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, a route that passes through the tourist spots can be generated. In addition, when the user is in a commercial facility, a route that takes into account store information can also be generated. Furthermore, when the user is using public transportation, a route that takes into account operation information can also be generated. In this way, an optimal route can be generated by taking into account the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input geographical location information to the generation AI and cause the generation AI to generate an optimal route.

[0050] The generation unit can analyze the user's social media activity and generate a related route when calculating the route. The generation unit, for example, analyzes the user's social media activity. For example, the generation unit generates a route that passes through places where the user has checked in on social media. The generation unit can also generate a route based on location information of photos shared by the user on social media. Furthermore, the generation unit can also generate a route that passes through places the user follows on social media. In this way, the related route can be generated by analyzing the social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input social media data to the generation AI and cause the generation AI to generate a related route.

[0051] The providing unit can select the optimal guidance method by referring to the user's past movement history when providing route guidance. The providing unit, for example, refers to the user's past movement history using a GPS log or a record of the movement route. For example, the optimal guidance method is selected based on the route used by the user in the past. In addition, a guidance method that avoids congestion can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed and the most efficient guidance method can be selected. As a result, the optimal guidance method can be selected by referring to the past movement history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past movement data to the generating AI and cause the generating AI to select the optimal guidance method.

[0052] The providing unit can adjust the timing of guidance in consideration of the user's current moving speed and direction when providing route guidance. The providing unit acquires the user's moving speed using, for example, a GPS speed or an acceleration sensor. For example, when the user is moving at high speed, the timing of guidance is advanced. In addition, the moving direction of the user is acquired using a compass or a gyro sensor, and when the user frequently changes direction, the timing of guidance can be shortened. Furthermore, when the user is walking, the timing of guidance can be adjusted according to the moving speed. As a result, the timing of guidance can be optimized by considering the moving speed and direction. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data on the moving speed and direction to the generating AI and cause the generating AI to adjust the timing of guidance.

[0053] The providing unit can select the optimal guidance method in consideration of the geographical location information of the user when providing route guidance. The providing unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, the providing unit selects a guidance method that passes through the tourist spot. In addition, when the user is in a commercial facility, the providing unit can select a guidance method that takes store information into consideration. Furthermore, when the user is using public transportation, the providing unit can select a guidance method that takes operation information into consideration. In this way, the optimal guidance method can be selected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input geographical location information to the generating AI and cause the generating AI to select the optimal guidance method.

[0054] The providing unit can analyze the social media activity of the user and provide related guidance information during route guidance. The providing unit, for example, analyzes the social media activity of the user. For example, it provides information on the place where the user checked in on social media. It can also provide guidance information based on the location information of the photo shared by the user on social media. It can also provide information on the place that the user follows on social media. In this way, it is possible to provide related guidance information by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data to the generating AI and cause the generating AI to provide related guidance information.

[0055] The reception unit can select the optimal reception method by referring to the user's past input history when receiving input. The reception unit, for example, refers to the user's past input history using an input log or a record of input contents. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. In addition, it can provide predictive input based on the contents that the user has input in the past. Furthermore, it can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, it is possible to select the optimal reception method by referring to the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input data to a generation AI and cause the generation AI to select the optimal reception method.

[0056] The reception unit can improve the accuracy of reception by considering the current situation and environment of the user when receiving input. The reception unit acquires the current situation and environment of the user using, for example, ambient sound, light intensity, temperature, etc. For example, when the user is in a noisy environment, the accuracy of voice input is improved. In addition, when the user is moving, the timing of input reception can be adjusted. Furthermore, when the user is in a quiet environment, detailed input options can be provided. This improves the accuracy of reception by considering the current situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input environmental data to the generation AI and cause the generation AI to execute the improvement of the accuracy of reception.

[0057] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving the input. The reception unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, when the user is in a tourist spot, tourist information can be preferentially received. In addition, when the user is in a commercial facility, store information can also be preferentially received. Furthermore, when the user is using public transportation, operation information can also be preferentially received. In this way, the optimal reception method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal reception method.

[0058] The reception unit can analyze the social media activity of the user and receive related input information when receiving the input. The reception unit, for example, analyzes the social media activity of the user. For example, it receives information on the location where the user checked in on social media. It can also receive input information based on location information of photos shared by the user on social media. It can also receive information on the locations the user follows on social media. In this way, it is possible to receive related input information by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data to the generation AI and cause the generation AI to execute the reception of related input information.

[0059] When detecting an obstacle, the detection unit can select the optimal detection method by referring to the user's past movement history. The detection unit, for example, refers to the user's past movement history using a GPS log or a record of the movement route. For example, the optimal detection method is selected based on the route that the user frequently used in the past. In addition, a detection method that avoids congestion can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed and the most efficient detection method can be selected. In this way, the optimal detection method can be selected by referring to the past movement history. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past movement data to the generation AI and cause the generation AI to select the optimal detection method.

[0060] The detection unit can select the optimal detection method in consideration of the geographical location information of the user when detecting an obstacle. The detection unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, the detection unit preferentially detects obstacles around the tourist spot. Also, when the user is in a commercial facility, the detection unit can preferentially detect obstacles around the store. Furthermore, when the user is using public transportation, the detection unit can preferentially detect obstacles around the station or bus stop. This allows the optimal detection method to be selected by considering the geographical location information. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal detection method.

[0061] The feedback unit can select the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback unit can refer to the user's past feedback history by using, for example, a feedback log or a record of the feedback content. For example, the optimal feedback is provided based on the feedback method that the user preferred in the past. In addition, the feedback method for a specific situation can be selected from the user's past feedback history. Furthermore, the user's past feedback history can be analyzed and the most effective feedback method can be selected. In this way, the optimal feedback method can be selected by referring to the past feedback history. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input past feedback data to the generation AI and cause the generation AI to select the optimal feedback method.

[0062] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. The feedback unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, feedback around the tourist spot can be provided preferentially. In addition, when the user is in a commercial facility, feedback around the store can be provided preferentially. Furthermore, when the user is using public transportation, feedback around the station or bus stop can be provided preferentially. In this way, the optimal feedback method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0063] The providing unit can select the optimal reading method by referring to the user's past reading history when reading aloud. The providing unit can refer to the user's past reading history by using, for example, a reading log or a record of the reading content. For example, the optimal reading is provided based on the reading method that the user preferred in the past. In addition, the reading method of a specific genre or author can be selected from the user's past reading history. Furthermore, the user's past reading history can be analyzed and the most effective reading method can be selected. In this way, the optimal reading method can be selected by referring to the past reading history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input past reading data to the generating AI and cause the generating AI to select the optimal reading method.

[0064] The providing unit can select the optimal reading method in consideration of the geographical location information of the user when reading out. The providing unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, information related to the tourist spot can be read out preferentially. In addition, when the user is in a commercial facility, store information can be read out preferentially. Furthermore, when the user is using public transportation, operation information can be read out preferentially. In this way, the optimal reading method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information to the generating AI and cause the generating AI to select the optimal reading method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The wearable device may also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health condition in real time. For example, if the heart rate is abnormally high, an alert is provided to encourage the user to take a break. Also, if the blood pressure is high, a guide to relax can be provided. Furthermore, if the body temperature is rising, a warning can be given of the risk of heatstroke. This makes it easier for visually impaired people to manage their health even when they are out and about.

[0067] The wearable device may also include a diet management unit that supports the user's diet management. The diet management unit records the contents of the meals the user has eaten and analyzes the nutritional balance. For example, when the user inputs the contents of the meal by voice, information on calories and nutrients is provided. Also, if the user needs to consume a specific nutrient, ingredients and recipes containing that nutrient can be suggested. Furthermore, advice on healthy eating can be provided based on the meal history. This makes it easier for visually impaired people to maintain a healthy diet.

[0068] The wearable device may also include an exercise management unit that supports the user's exercise. The exercise management unit records the amount and type of exercise of the user and analyzes the effect of the exercise. For example, when the user walks or runs, the exercise management unit calculates the distance and calories burned. In addition, when the user sets a specific exercise goal, the exercise management unit can monitor the progress of the goal and report the progress. Furthermore, the exercise management unit can provide advice on the form and posture when the user exercises. This makes it easier for visually impaired people to exercise effectively.

[0069] The wearable device may also include a sleep management unit that supports the user's sleep. The sleep management unit records the user's sleep patterns and analyzes the quality of sleep. For example, it may provide music or a guide to help the user relax before going to bed. It may also provide an alarm that encourages the user to wake up naturally when waking up. Furthermore, it may provide advice to improve the quality of sleep based on the user's sleep data. This may make it easier for visually impaired people to get high-quality sleep.

[0070] The wearable device may also be equipped with a learning support unit that supports the user's learning. When the user inputs the content they want to learn by voice, the learning support unit provides information related to that content. For example, if the user wants to learn about a specific topic, the learning support unit will provide articles and audio materials on that topic. Also, if the user wants to learn a language, the learning support unit can provide pronunciation practice and word meanings. Furthermore, the learning progress of the user can be recorded and suggestions on what to learn next can be made. This makes it easier for visually impaired people to study effectively even when they are out and about.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The acquisition unit grasps the current location of a visually impaired person when he or she goes out. For example, the acquisition unit acquires the current location using GPS, Wi-Fi location information, cell tower information, etc. Step 2: The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, a generation AI is used to calculate a route that takes into account the shortest distance, shortest time, and traffic conditions. Step 3: The providing unit provides route guidance based on the route generated by the generating unit, for example, by using a method such as voice guidance, vibration guidance, or visual guidance. Step 4: The reception unit receives an input from the user, for example, by a method such as voice input, touch input, or gesture input. Step 5: The response generation unit generates a response based on the information received by the reception unit. For example, the response is generated by using a generation AI in a manner such as a voice response, a text response, or a vibration response. Step 6: The response providing unit provides the response generated by the response generating unit, for example, by a voice response, a text response, a vibration response, or the like. Step 7: The reception unit receives the title of the book to be read, for example, by voice input, text input, or the like. Step 8: The reading out providing unit reads out the contents based on the information received by the receiving unit, for example, using a voice synthesis technique.

[0073] (Example 2) The wearable device according to the embodiment of the present invention is a system that not only guides visually impaired people when they go out, but also serves as a conversation partner and reads books they want to read. When a visually impaired person goes out, this system grasps their current location and provides directions to their destination. When a user inputs a destination by voice, the generation AI calculates the optimal route and provides voice guidance. For example, specific instructions such as "Turn right at the next intersection" are provided. Next, the system has a function as a conversation partner. When a user speaks to the system, the generation AI responds appropriately and the conversation can continue. This allows visually impaired people to go out without feeling lonely and with peace of mind. Furthermore, the system has a function of reading books they want to read. When a user inputs the title of a book they want to read by voice, the generation AI reads out the contents of the book. This allows visually impaired people to enjoy reading freely. In this way, the wearable device of the present invention has multiple functions such as guidance for visually impaired people when they go out, a conversation partner, and reading books they want to read, and functions as a substitute or complement for a guide dog. This allows the wearable device to guide visually impaired people when they go out, be a conversation partner, and read books they want to read.

[0074] The wearable device according to the embodiment includes an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, the reception unit, and a reading-out provision unit. The acquisition unit grasps the current location when a visually impaired person goes out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. The generation unit calculates a route to a destination based on the information acquired by the acquisition unit. For example, a route considering the shortest distance, the shortest time, and traffic conditions can be calculated using a generation AI. The provision unit provides route guidance based on the route generated by the generation unit. For example, route guidance can be provided by a method such as voice guidance, vibration guidance, and visual guidance. The reception unit accepts user input. For example, user input can be accepted by a method such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information accepted by the reception unit. For example, a response can be generated by a method such as a voice response, a text response, and a vibration response using a generation AI. The response provision unit provides the response generated by the response generation unit. For example, a response can be provided by a method such as a voice response, a text response, and a vibration response. The reception unit receives the title of a book that the user wants to read. For example, the title of the book can be received by voice input, text input, or other methods. The read-aloud provider reads the contents based on the information received by the reception unit. For example, the read-aloud provider can use voice synthesis technology. This allows visually impaired people to be given directions, have someone to talk to, and read books for them when they go out.

[0075] The acquisition unit grasps the current location of the visually impaired person when they go out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. Specifically, GPS receives signals from satellites to identify the current location with high accuracy, and Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information identifies the rough location using signals from mobile phone base stations. By combining these technologies, highly accurate location information can be acquired both indoors and outdoors. Furthermore, the acquisition unit can collect not only location information but also surrounding environmental information. For example, environmental data such as surrounding sounds, vibrations, temperature, and humidity can be acquired through sensors to help the visually impaired person move safely. As a result, the acquisition unit can comprehensively collect information necessary for the visually impaired person when they go out and provide it in real time.

[0076] The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can be used to calculate a route that takes into account the shortest distance, the shortest time, and traffic conditions. Specifically, the generation AI analyzes map data between the current location and the destination to generate an optimal route. By taking into account traffic conditions, road congestion, construction information, etc., the generation AI can provide an individually optimized route by learning the past movement history and the user's preferences. For example, the generation AI can propose a safer and more comfortable route by taking into account routes frequently used by visually impaired people and places they want to avoid. Furthermore, the generation unit can recalculate the route based on information updated in real time. For example, if a sudden event such as a traffic accident or road closure occurs, the generation AI can instantly calculate a new route and notify the visually impaired person. This allows the generation unit to support the visually impaired person to always travel on the optimal route.

[0077] The providing unit performs route guidance based on the route generated by the generating unit. For example, route guidance can be performed using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, the voice guidance informs the visually impaired person of the direction and distance to go next, obstacles to be careful of, and the like by voice. The vibration guidance indicates the direction and distance by vibrating the device, and the visually impaired person can feel it with their hands or arms. When used by a visually impaired person, the visual guidance can provide information using a braille display or large character display. In this way, the providing unit supports the visually impaired person to reach the destination safely and reliably. In addition, the providing unit can customize the guidance method according to the user's preferences and circumstances. For example, for a user who is not good at voice guidance, vibration guidance can be given priority, and a combination of multiple guidance methods can be provided. In addition, the providing unit can modify the guidance content based on information updated in real time. For example, if a new obstacle occurs on the route, the providing unit immediately provides new guidance so that the visually impaired person can move safely. In this way, the providing unit can provide an environment in which the visually impaired person can go out with peace of mind.

[0078] The reception unit accepts user input. For example, the reception unit can accept user input by voice input, touch input, gesture input, and other methods. Specifically, the voice input is a method in which a visually impaired person operates the device by speaking to it, and the user's instructions are accurately understood using voice recognition technology. The touch input is a method in which an operation is performed by touching the device's touch screen with a finger, and is designed so that a visually impaired person can confirm the operation by touch. The gesture input is a method in which a camera or sensor in the device is used to detect the movement of the hand or arm to perform an operation, and a visually impaired person can operate the device with a natural movement. In this way, the reception unit supports a visually impaired person to operate the device intuitively and easily. In addition, the reception unit can quickly and accurately process the user's input and transmit the information to other departments. For example, when a destination is specified by voice input, the reception unit transmits the information to the generation unit and starts route calculation. In this way, the reception unit can provide an environment in which a visually impaired person can smoothly use the device.

[0079] The response generation unit generates a response based on the information received by the reception unit. For example, the generation AI can be used to generate responses by voice response, text response, vibration response, or other methods. Specifically, the generation AI analyzes the user's input content and generates an appropriate response. Voice response is a method of providing information to visually impaired people by voice, and uses voice synthesis technology to generate natural speech. Text response is a method that allows visually impaired people to check information using a braille display or screen reader, and the generation AI generates appropriate text. Vibration response is a method of conveying information by vibrating a device, and visually impaired people can receive information by touch. In this way, the response generation unit helps visually impaired people to receive the information they need quickly and accurately. In addition, the response generation unit can customize the response content according to the user's preferences and circumstances. For example, for users who are not good at voice response, text response can be prioritized and multiple response methods can be provided in combination. In addition, the response generation unit can modify the response content based on information updated in real time. In this way, the response generation unit can provide an environment in which visually impaired people can always receive the latest information.

[0080] The response providing unit provides the response generated by the response generating unit. For example, the response can be provided by a method such as a voice response, a text response, or a vibration response. Specifically, the voice response is a method of providing information to a visually impaired person by voice, and the generated voice is reproduced through a speaker or earphone. The text response is a method of enabling a visually impaired person to confirm information using a braille display or a screen reader, and displays the generated text. The vibration response is a method of conveying information by vibrating a device, and the visually impaired person can receive information by touch. In this way, the response providing unit supports the visually impaired person to receive the necessary information quickly and accurately. In addition, the response providing unit can customize the response method according to the user's preferences and circumstances. For example, for a user who is not good at voice response, a text response can be given priority, and a combination of multiple response methods can be provided. In addition, the response providing unit can modify the response content based on information updated in real time. In this way, the response providing unit can provide an environment in which a visually impaired person can always receive the latest information.

[0081] This reception unit accepts the title of a book to be read. For example, the title of the book can be accepted by voice input, text input, or other methods. Specifically, voice input is a method in which a visually impaired person specifies the title of a book by speaking to a device, and the user's instructions are accurately understood using voice recognition technology. Text input is a method in which a visually impaired person inputs the title of a book using a braille display or screen reader, and is designed so that a visually impaired person can confirm the operation by touch. In this way, this reception unit supports visually impaired people to intuitively and easily specify the title of a book they want to read. In addition, this reception unit can quickly and accurately process the user's input and transmit information to other departments. For example, when a book title is specified by voice input, this reception unit transmits the information to the reading-aloud providing unit and starts reading aloud. In this way, this reception unit can provide an environment in which a visually impaired person can smoothly specify a book they want to read.

[0082] The reading-out providing unit reads out the contents based on the information received by the receiving unit. For example, reading out can be performed using a voice synthesis technology. Specifically, the voice synthesis technology is a technology that analyzes text data and generates natural speech, and provides the contents of the book by voice to the visually impaired. The reading-out providing unit can adjust the reading speed and tone of the voice according to the user's preferences and circumstances. For example, the reading speed can be increased for a user who prefers a fast reading speed, and can be decreased for a user who prefers a slow reading speed. In addition, the reading-out providing unit can modify the reading out contents based on information updated in real time. For example, when the contents of the book are updated, the reading-out providing unit immediately reads out the new contents. In this way, the reading-out providing unit can provide an environment in which the visually impaired can always receive the latest information. In addition, the reading-out providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the reading out contents. In this way, the reading-out providing unit can provide an environment in which the visually impaired can comfortably enjoy books.

[0083] The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has a function of detecting and avoiding obstacles when a visually impaired person goes out. For example, an obstacle can be detected using an ultrasonic sensor, a camera, LIDAR, or the like. The detection unit can avoid obstacles by changing the direction of travel or adjusting the speed. For example, an ultrasonic sensor can be used to detect an obstacle ahead and change the direction of travel. In addition, a camera can be used to visually detect an obstacle and adjust the speed. Furthermore, LIDAR can be used to scan the surrounding environment in detail and avoid obstacles. This makes it easier for visually impaired people to avoid obstacles. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from an ultrasonic sensor or a camera to the generation AI and cause the generation AI to detect and avoid obstacles.

[0084] The wearable device includes a feedback unit that provides vibration or haptic feedback. The feedback unit has a function for visually impaired people to receive feedback when they go out. For example, the feedback can be provided by adjusting the intensity or pattern of vibration. The feedback unit can also provide feedback using a haptic device. For example, a vibration motor can be used to provide vibration in a specific pattern. The haptic device can also be used to make the visually impaired people feel changes in pressure or temperature. This makes it easier for visually impaired people to receive feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user input and environmental data to the generation AI and cause the generation AI to execute optimal feedback.

[0085] The wearable device has a function of reading out a specific chapter or page. The reading out function has a function that allows visually impaired people to select a specific chapter or page and have it read out. For example, a chapter number or a page number can be specified to read out. The reading out function can also select a specific chapter or page using a keyword search. For example, if a user inputs "Please read out Chapter 3" by voice, the specified chapter will be read out. Also, if a user inputs "Please read out a page that contains the keyword 'adventure'", the corresponding page can be read out. This allows visually impaired people to select a specific chapter or page and have it read out. Some or all of the above-mentioned processing in the reading out function may be performed, for example, using AI, or may be performed without using AI. For example, the reading out function can input a user's voice input to a generation AI and have the generation AI execute reading out a specified chapter or page.

[0086] The wearable device has a function for supporting multiple languages. The multiple language support function has a function for visually impaired people to obtain information in multiple languages. For example, it can support languages ​​such as English, Japanese, and Spanish. The multiple language support function can accept voice input and text input in multiple languages. For example, when a user inputs in English, a response in English is provided. Also, when a user inputs in Japanese, a response in Japanese can be provided. Furthermore, the multiple language support function can read out in multiple languages ​​using speech synthesis technology. For example, when reading out an English book, English speech synthesis is used, and when reading out a Japanese book, Japanese speech synthesis is used. This allows visually impaired people to obtain information in multiple languages. Part or all of the above-mentioned processing in the multiple language support function may be performed, for example, using AI, or may be performed without using AI. For example, the multiple language support function can input a user's input to a generation AI and have the generation AI execute responses and reading out in multiple languages.

[0087] The wearable device includes a providing unit that acquires the latest information online and provides it to the user. The providing unit has a function for visually impaired people to acquire the latest information in real time. For example, it can acquire real-time data through the Internet and provide it to the user. The providing unit can perform regular updates and always provide the latest information. For example, it acquires the latest information such as news, weather forecasts, and traffic information and provides it to the user. The providing unit can also acquire and provide specific information in response to a user's request. For example, when a user voice-inputs "Please tell me the latest news," the latest news is acquired and read out. This allows visually impaired people to acquire the latest information in real time. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data acquired from the Internet to a generating AI and cause the generating AI to execute information to be provided to the user.

[0088] The acquisition unit can estimate the user's emotion and adjust the acquisition frequency of the current location based on the estimated user's emotion. The acquisition unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, the acquisition frequency of the current location can be increased to provide more detailed navigation information. In addition, if the user is relaxed, the acquisition frequency of the current location can be reduced to reduce battery consumption. Furthermore, if the user is in a hurry, the acquisition frequency of the current location can be increased to provide quick navigation. This enables more appropriate navigation by adjusting the acquisition frequency of the current location according to the user's emotion. 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, 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 acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's voice data and facial expression data to the generation AI, and cause the generation AI to execute the estimation of the emotion and the adjustment of the acquisition frequency of the current location.

[0089] The acquisition unit can analyze the user's past movement history and select the optimal current location acquisition method. The acquisition unit can analyze the user's past movement history using, for example, a GPS log or a record of a movement route. For example, the accuracy of current location acquisition can be adjusted based on places that the user has frequently visited in the past. In addition, the acquisition unit can analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify a place where the user got lost in the past and increase the frequency of current location acquisition in the vicinity. This can improve the accuracy of current location acquisition based on the past movement history. Part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input past movement data to the generation AI and cause the generation AI to select the optimal current location acquisition method.

[0090] The acquisition unit can improve the accuracy of acquiring the current location by considering the moving speed and direction of the user. The acquisition unit acquires the moving speed of the user using, for example, a GPS speed or an acceleration sensor. For example, when the user is moving at high speed, the acquisition accuracy of the current location is improved. In addition, the moving direction of the user can be acquired using a compass or a gyro sensor, and when the user frequently changes direction, the acquisition frequency of the current location can be increased. Furthermore, when the user is walking, the acquisition accuracy of the current location can be adjusted according to the moving speed. As a result, the accuracy of the current location acquisition is improved by considering the moving speed and direction. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input data on the moving speed and direction to the generation AI and cause the generation AI to execute the improvement of the accuracy of the current location acquisition.

[0091] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the current location based on the estimated emotion of the user. The acquisition unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, when the user is nervous, the timing of acquiring the current location can be shortened to provide a sense of security. In addition, when the user is relaxed, the timing of acquiring the current location can be lengthened to reduce battery consumption. Furthermore, when the user is in a hurry, the timing of acquiring the current location can be shortened to provide quick navigation. This enables more appropriate navigation by adjusting the timing of acquiring the current location according to the user's emotion. 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, 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 acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's voice data and facial expression data to the generation AI, and cause the generation AI to execute the estimation of the emotion and the adjustment of the timing of acquiring the current location.

[0092] When acquiring the current location, the acquisition unit can preferentially acquire highly relevant information by considering the geographical location information of the user. The acquisition unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, information on tourist spots can be preferentially acquired. In addition, when the user is in a commercial facility, store information can also be preferentially acquired. Furthermore, when the user is using public transportation, operation information can also be preferentially acquired. In this way, by considering the geographical location information, highly relevant information can be preferentially acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0093] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the current location. The acquisition unit, for example, analyzes the user's social media activity. For example, it acquires information on the location where the user checked in on social media. It can also acquire the current location based on the location information of the photo the user shared on social media. It can also preferentially acquire information on the location the user follows on social media. This makes it possible to acquire related information by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to acquire related information.

[0094] The generation unit can estimate the user's emotion and adjust the route calculation algorithm based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, an algorithm that prioritizes the safest route is used. Also, if the user is relaxed, an algorithm that prioritizes a scenic route can be used. Furthermore, if the user is in a hurry, an algorithm that prioritizes a route that can be reached in the shortest time can be used. In this way, a more appropriate route can be provided by adjusting the route calculation algorithm according to the user's emotion. The emotion estimation 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to estimate the emotion and adjust the route calculation algorithm.

[0095] The generation unit can predict the optimal route by referring to past movement data when calculating the route. The generation unit can refer to past movement data using, for example, a GPS log or a record of a movement route. For example, the generation unit can predict the optimal route based on a route used by the user in the past. In addition, the generation unit can predict a route that avoids congestion from the user's past movement data. Furthermore, the generation unit can predict the most efficient route by analyzing the user's past movement data. In this way, the generation unit can predict the optimal route by referring to the past movement data. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past movement data to the generation AI and cause the generation AI to execute prediction of the optimal route.

[0096] The generation unit can generate an optimal route by considering traffic conditions and weather information when calculating a route. The generation unit generates an optimal route based on, for example, real-time traffic congestion information. For example, a route that avoids congestion is calculated using traffic information obtained from a traffic sensor or the Internet. The generation unit can also generate an optimal route by considering real-time weather information. For example, a route that avoids rain and snow is calculated based on weather data. Furthermore, the optimal route can be generated by considering the real-time operation status of public transportation. For example, the most efficient route is calculated based on operation information. In this way, the optimal route can be generated by considering traffic conditions and weather information. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input traffic information and weather information to the generation AI and cause the generation AI to generate an optimal route.

[0097] The generation unit can estimate the user's emotions and determine the priority of the route based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, a safe route can be prioritized. Also, if the user is relaxed, a route with a good view can be prioritized. Furthermore, if the user is in a hurry, a route that can be reached in the shortest time can be prioritized. In this way, a more appropriate route can be provided by determining the priority of the route according to the user's emotions. 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, 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and route priority determination.

[0098] The generation unit can generate an optimal route by taking into account the geographical location information of the user when calculating the route. The generation unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, a route that passes through the tourist spots can be generated. In addition, when the user is in a commercial facility, a route that takes into account store information can also be generated. Furthermore, when the user is using public transportation, a route that takes into account operation information can also be generated. In this way, an optimal route can be generated by taking into account the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input geographical location information to the generation AI and cause the generation AI to generate an optimal route.

[0099] The generation unit can analyze the user's social media activity and generate a related route when calculating the route. The generation unit, for example, analyzes the user's social media activity. For example, the generation unit generates a route that passes through places where the user has checked in on social media. The generation unit can also generate a route based on location information of photos shared by the user on social media. Furthermore, the generation unit can also generate a route that passes through places the user follows on social media. In this way, the related route can be generated by analyzing the social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input social media data to the generation AI and cause the generation AI to generate a related route.

[0100] The providing unit can estimate the user's emotion and adjust the way of expressing the route guidance based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, when the user feels anxious, detailed route guidance is provided. Also, when the user is relaxed, concise route guidance can be provided. Furthermore, when the user is in a hurry, quick route guidance can be provided. This enables more appropriate guidance by adjusting the way of expressing the route guidance according to the user's emotion. 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, 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotion and the adjustment of the way of expressing the route guidance.

[0101] The providing unit can select the optimal guidance method by referring to the user's past movement history when providing route guidance. The providing unit, for example, refers to the user's past movement history using a GPS log or a record of the movement route. For example, the optimal guidance method is selected based on the route used by the user in the past. In addition, a guidance method that avoids congestion can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed and the most efficient guidance method can be selected. As a result, the optimal guidance method can be selected by referring to the past movement history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past movement data to the generating AI and cause the generating AI to select the optimal guidance method.

[0102] The providing unit can adjust the timing of guidance in consideration of the user's current moving speed and direction when providing route guidance. The providing unit acquires the user's moving speed using, for example, a GPS speed or an acceleration sensor. For example, when the user is moving at high speed, the timing of guidance is advanced. In addition, the moving direction of the user is acquired using a compass or a gyro sensor, and when the user frequently changes direction, the timing of guidance can be shortened. Furthermore, when the user is walking, the timing of guidance can be adjusted according to the moving speed. As a result, the timing of guidance can be optimized by considering the moving speed and direction. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data on the moving speed and direction to the generating AI and cause the generating AI to adjust the timing of guidance.

[0103] The providing unit can estimate the user's emotion and determine the priority of the route guidance based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, a safe route can be prioritized. Also, if the user is relaxed, a route with a good view can be prioritized. Furthermore, if the user is in a hurry, a route that can be reached in the shortest time can be prioritized. This enables more appropriate guidance by determining the priority of the route guidance according to the user's emotion. 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, 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotion and the determination of the priority of the route guidance.

[0104] The providing unit can select the optimal guidance method in consideration of the geographical location information of the user when providing route guidance. The providing unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, the providing unit selects a guidance method that passes through the tourist spot. In addition, when the user is in a commercial facility, the providing unit can select a guidance method that takes store information into consideration. Furthermore, when the user is using public transportation, the providing unit can select a guidance method that takes operation information into consideration. In this way, the optimal guidance method can be selected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input geographical location information to the generating AI and cause the generating AI to select the optimal guidance method.

[0105] The providing unit can analyze the social media activity of the user and provide related guidance information during route guidance. The providing unit, for example, analyzes the social media activity of the user. For example, it provides information on the place where the user checked in on social media. It can also provide guidance information based on the location information of the photo shared by the user on social media. It can also provide information on the place that the user follows on social media. In this way, it is possible to provide related guidance information by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data to the generating AI and cause the generating AI to provide related guidance information.

[0106] The reception unit can estimate the user's emotion and adjust the input reception method based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized and input can be quickly accepted. This allows more appropriate input by adjusting the input reception method according to the user's emotion. The emotion estimation 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, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and adjustment of the input reception method.

[0107] The reception unit can select the optimal reception method by referring to the user's past input history when receiving input. The reception unit, for example, refers to the user's past input history using an input log or a record of input contents. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. In addition, it can provide predictive input based on the contents that the user has input in the past. Furthermore, it can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, it is possible to select the optimal reception method by referring to the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input data to a generation AI and cause the generation AI to select the optimal reception method.

[0108] The reception unit can improve the accuracy of reception by considering the current situation and environment of the user when receiving input. The reception unit acquires the current situation and environment of the user using, for example, ambient sound, light intensity, temperature, etc. For example, when the user is in a noisy environment, the accuracy of voice input is improved. In addition, when the user is moving, the timing of input reception can be adjusted. Furthermore, when the user is in a quiet environment, detailed input options can be provided. This improves the accuracy of reception by considering the current situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input environmental data to the generation AI and cause the generation AI to execute the improvement of the accuracy of reception.

[0109] The reception unit can estimate the user's emotion and determine the priority of input reception based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, important inputs are preferentially received. Also, if the user is relaxed, detailed inputs can be preferentially received. Furthermore, if the user is in a hurry, quick inputs can be preferentially received. This allows more appropriate input by determining the priority of input reception according to the user's emotion. The emotion estimation 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, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and input reception priority determination.

[0110] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving the input. The reception unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, when the user is in a tourist spot, tourist information can be preferentially received. In addition, when the user is in a commercial facility, store information can also be preferentially received. Furthermore, when the user is using public transportation, operation information can also be preferentially received. In this way, the optimal reception method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal reception method.

[0111] The reception unit can analyze the social media activity of the user and receive related input information when receiving the input. The reception unit, for example, analyzes the social media activity of the user. For example, it receives information on the location where the user checked in on social media. It can also receive input information based on location information of photos shared by the user on social media. It can also receive information on the locations the user follows on social media. In this way, it is possible to receive related input information by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data to the generation AI and cause the generation AI to execute the reception of related input information.

[0112] The detection unit can estimate the user's emotion and adjust the sensitivity of the obstacle detection based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, the sensitivity of the obstacle detection can be increased. Also, if the user is relaxed, the sensitivity of the obstacle detection can be reduced. Furthermore, if the user is in a hurry, the sensitivity of the obstacle detection can be increased. This allows for more appropriate detection by adjusting the sensitivity of the obstacle detection according to the user's emotion. The emotion estimation 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to estimate the emotion and adjust the sensitivity of the obstacle detection.

[0113] When detecting an obstacle, the detection unit can select the optimal detection method by referring to the user's past movement history. The detection unit, for example, refers to the user's past movement history using a GPS log or a record of the movement route. For example, the optimal detection method is selected based on the route that the user frequently used in the past. In addition, a detection method that avoids congestion can be selected from the user's past movement history. Furthermore, the user's past movement history can be analyzed and the most efficient detection method can be selected. In this way, the optimal detection method can be selected by referring to the past movement history. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past movement data to the generation AI and cause the generation AI to select the optimal detection method.

[0114] The detection unit can estimate the user's emotion and determine the priority of obstacle detection based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, important obstacles are detected with priority. Also, if the user is relaxed, detailed obstacles can be detected with priority. Furthermore, if the user is in a hurry, quick obstacle detection can be prioritized. This enables more appropriate detection by determining the priority of obstacle detection according to the user's emotion. The emotion estimation 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and obstacle detection priority determination.

[0115] The detection unit can select the optimal detection method in consideration of the geographical location information of the user when detecting an obstacle. The detection unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, the detection unit preferentially detects obstacles around the tourist spot. Also, when the user is in a commercial facility, the detection unit can preferentially detect obstacles around the store. Furthermore, when the user is using public transportation, the detection unit can preferentially detect obstacles around the station or bus stop. This allows the optimal detection method to be selected by considering the geographical location information. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal detection method.

[0116] The feedback unit can estimate the user's emotion and adjust the strength of the feedback based on the estimated user's emotion. The feedback unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, strong feedback is provided. Also, if the user is relaxed, weak feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. This enables more appropriate feedback by adjusting the strength of the feedback according to the user's emotion. The emotion estimation 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and adjustment of the feedback strength.

[0117] The feedback unit can select the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback unit can refer to the user's past feedback history by using, for example, a feedback log or a record of the feedback content. For example, the optimal feedback is provided based on the feedback method that the user preferred in the past. In addition, the feedback method for a specific situation can be selected from the user's past feedback history. Furthermore, the user's past feedback history can be analyzed and the most effective feedback method can be selected. In this way, the optimal feedback method can be selected by referring to the past feedback history. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input past feedback data to the generation AI and cause the generation AI to select the optimal feedback method.

[0118] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, important feedback can be provided preferentially. Also, if the user is relaxed, detailed feedback can be provided preferentially. Furthermore, if the user is in a hurry, quick feedback can be prioritized. This enables more appropriate feedback by determining the priority of feedback according to the user's emotion. The emotion estimation 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and feedback priority determination.

[0119] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. The feedback unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, feedback around the tourist spot can be provided preferentially. In addition, when the user is in a commercial facility, feedback around the store can be provided preferentially. Furthermore, when the user is using public transportation, feedback around the station or bus stop can be provided preferentially. In this way, the optimal feedback method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0120] The providing unit can estimate the user's emotion and adjust the reading expression method based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, the reading is performed in a calm voice. Also, if the user is relaxed, the reading can be performed in a bright voice. Furthermore, if the user is in a hurry, the reading can be performed quickly and concisely. This allows for more appropriate reading by adjusting the reading expression method according to the user's emotion. The estimation of emotion is realized using, for example, an emotion estimation function using 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's voice data and facial expression data to the generation AI, and cause the generation AI to estimate the emotion and adjust the reading expression method.

[0121] The providing unit can select the optimal reading method by referring to the user's past reading history when reading aloud. The providing unit can refer to the user's past reading history by using, for example, a reading log or a record of the reading content. For example, the optimal reading is provided based on the reading method that the user preferred in the past. In addition, the reading method of a specific genre or author can be selected from the user's past reading history. Furthermore, the user's past reading history can be analyzed and the most effective reading method can be selected. In this way, the optimal reading method can be selected by referring to the past reading history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input past reading data to the generating AI and cause the generating AI to select the optimal reading method.

[0122] The providing unit can estimate the user's emotion and determine the priority of reading out based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, heart rate, and the like. For example, if the user feels anxious, important information is read out preferentially. Also, if the user is relaxed, detailed information can be read out preferentially. Furthermore, if the user is in a hurry, quick information can be read out preferentially. This enables more appropriate reading out by determining the priority of reading out according to the user's emotion. 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's voice data and facial expression data to the generation AI and cause the generation AI to execute emotion estimation and reading out priority determination.

[0123] The providing unit can select the optimal reading method in consideration of the geographical location information of the user when reading out. The providing unit acquires the geographical location information of the user using, for example, GPS, Wi-Fi location information, cell tower information, and the like. For example, when the user is in a tourist spot, information related to the tourist spot can be read out preferentially. In addition, when the user is in a commercial facility, store information can be read out preferentially. Furthermore, when the user is using public transportation, operation information can be read out preferentially. In this way, the optimal reading method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information to the generating AI and cause the generating AI to select the optimal reading method.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The wearable device may also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health condition in real time. For example, if the heart rate is abnormally high, an alert is provided to encourage the user to take a break. Also, if the blood pressure is high, a guide to relax can be provided. Furthermore, if the body temperature is rising, a warning can be given of the risk of heatstroke. This makes it easier for visually impaired people to manage their health even when they are out and about.

[0126] The wearable device may also include a music providing unit that estimates the user's emotions and selects music based on the estimated emotions. For example, if the user is relaxed, calm music may be provided. Also, if the user is feeling stressed, music with a relaxing effect may be provided. Furthermore, if the user wants to cheer up, upbeat music may be provided. This allows visually impaired people to enjoy music that matches their emotions even when they are out and about.

[0127] The wearable device may also include a diet management unit that supports the user's diet management. The diet management unit records the contents of the meals the user has eaten and analyzes the nutritional balance. For example, when the user inputs the contents of the meal by voice, information on calories and nutrients is provided. Also, if the user needs to consume a specific nutrient, ingredients and recipes containing that nutrient can be suggested. Furthermore, advice on healthy eating can be provided based on the meal history. This makes it easier for visually impaired people to maintain a healthy diet.

[0128] The wearable device may also include a relaxation unit that estimates the user's emotions and provides a relaxation guide based on the estimated emotions. For example, if the user is feeling stressed, a guide to deep breathing or meditation may be provided. If the user is feeling relaxed, relaxation music or natural sounds may be provided. Furthermore, if the user is feeling anxious, a message that gives a sense of security may be provided. This makes it easier for visually impaired people to relax even when they are out and about.

[0129] The wearable device may also include an exercise management unit that supports the user's exercise. The exercise management unit records the amount and type of exercise of the user and analyzes the effect of the exercise. For example, when the user walks or runs, the exercise management unit calculates the distance and calories burned. In addition, when the user sets a specific exercise goal, the exercise management unit can monitor the progress of the goal and report the progress. Furthermore, the exercise management unit can provide advice on the form and posture when the user exercises. This makes it easier for visually impaired people to exercise effectively.

[0130] The wearable device may also include a communication unit that estimates the user's emotions and adjusts the method of communication based on the estimated emotions. For example, if the user feels lonely, a message encouraging contact with friends and family may be provided. If the user feels relaxed, light topics may be provided. Furthermore, if the user feels stressed, an encouraging message may be provided. This makes it easier for visually impaired people to communicate appropriately even when they are out and about.

[0131] The wearable device may also include a sleep management unit that supports the user's sleep. The sleep management unit records the user's sleep patterns and analyzes the quality of sleep. For example, it may provide music or a guide to help the user relax before going to bed. It may also provide an alarm that encourages the user to wake up naturally when waking up. Furthermore, it may provide advice to improve the quality of sleep based on the user's sleep data. This may make it easier for visually impaired people to get high-quality sleep.

[0132] The wearable device may also include an exercise unit that estimates the user's emotions and adjusts the exercise content based on the estimated emotions. For example, if the user is feeling stressed, the exercise unit may suggest an exercise that has a relaxing effect. Also, if the user wants to cheer up, the exercise unit may suggest an energetic exercise. Furthermore, if the user is relaxed, the exercise unit may suggest light stretching or yoga. This makes it easier for visually impaired people to do appropriate exercises even when they are out and about.

[0133] The wearable device may also be equipped with a learning support unit that supports the user's learning. When the user inputs the content they want to learn by voice, the learning support unit provides information related to that content. For example, if the user wants to learn about a specific topic, the learning support unit will provide articles and audio materials on that topic. Also, if the user wants to learn a language, the learning support unit can provide pronunciation practice and word meanings. Furthermore, the learning progress of the user can be recorded and suggestions on what to learn next can be made. This makes it easier for visually impaired people to study effectively even when they are out and about.

[0134] The wearable device may also include a reminder unit that estimates the user's emotions and adjusts the contents of the reminder based on the estimated emotions. For example, if the user is stressed, the reminder contents may be simplified. If the user is relaxed, a detailed reminder may be provided. Furthermore, if the user is in a hurry, important reminders may be provided with priority. This makes it easier for visually impaired people to receive appropriate reminders even when they are out and about.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The acquisition unit grasps the current location of a visually impaired person when he or she goes out. For example, the acquisition unit acquires the current location using GPS, Wi-Fi location information, cell tower information, etc. Step 2: The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, a generation AI is used to calculate a route that takes into account the shortest distance, shortest time, and traffic conditions. Step 3: The providing unit provides route guidance based on the route generated by the generating unit, for example, by using a method such as voice guidance, vibration guidance, or visual guidance. Step 4: The reception unit receives an input from the user, for example, by a method such as voice input, touch input, or gesture input. Step 5: The response generation unit generates a response based on the information received by the reception unit. For example, the response is generated by using a generation AI in a manner such as a voice response, a text response, or a vibration response. Step 6: The response providing unit provides the response generated by the response generating unit, for example, by a voice response, a text response, a vibration response, or the like. Step 7: The reception unit receives the title of the book to be read, for example, by voice input, text input, or the like. Step 8: The reading out providing unit reads out the contents based on the information received by the receiving unit, for example, using a voice synthesis technique.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the above-mentioned elements including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, this reception unit, reading-out provision unit, detection unit, feedback unit, reading-out function, multiple language support function, provision unit, and emotion estimation function is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires a current location using GPS or Wi-Fi location information of the smart device 14. The generation unit calculates an optimal route by the specific processing unit 290 of the data processing device 12. The provision unit performs voice guidance by the control unit 46A of the smart device 14. The reception unit accepts user input using the touch panel 38A or microphone 38B of the smart device 14. The response generation unit generates a response by the specific processing unit 290 of the data processing device 12. The response provision unit provides a response using the speaker 40B of the smart device 14. The reception unit accepts the title of a book using the microphone 38B of the smart device 14. The reading out provision unit reads out the contents by the specific processing unit 290 of the data processing device 12. The detection unit detects an obstacle by using the camera 42 or ultrasonic sensor of the smart device 14. The feedback unit provides feedback by using the vibration motor of the smart device 14. The reading out function reads out a specific chapter or page by the specific processing unit 290 of the data processing device 12. The multiple language support function responds or reads out in multiple languages ​​by the specific processing unit 290 of the data processing device 12. The provision unit obtains the latest information by the specific processing unit 290 of the data processing device 12 and provides it using the speaker 40B of the smart device 14. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the frequency of obtaining the current location. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] 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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the above-mentioned elements including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, reading-out provision unit, detection unit, feedback unit, reading-out function, multiple language support function, provision unit, and emotion estimation function is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires a current location using GPS or Wi-Fi location information of the smart glasses 214. The generation unit calculates an optimal route by the specific processing unit 290 of the data processing device 12. The provision unit performs voice guidance by the control unit 46A of the smart glasses 214. The reception unit accepts user input using the microphone 238 of the smart glasses 214. The response generation unit generates a response by the specific processing unit 290 of the data processing device 12. The response provision unit provides a response using the speaker 240 of the smart glasses 214. The main reception unit accepts the title of a book using the microphone 238 of the smart glasses 214. The reading out providing unit reads out the contents by the specific processing unit 290 of the data processing device 12. The detection unit detects an obstacle using the camera 42 or ultrasonic sensor of the smart glasses 214. The feedback unit provides feedback using the vibration motor of the smart glasses 214. The reading out function reads out a specific chapter or page by the specific processing unit 290 of the data processing device 12. The multiple language support function responds or reads out in multiple languages ​​by the specific processing unit 290 of the data processing device 12. The providing unit obtains the latest information by the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the smart glasses 214. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the frequency of obtaining the current location. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the above-mentioned elements including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, this reception unit, reading-out provision unit, detection unit, feedback unit, reading-out function, multiple language support function, provision unit, and emotion estimation function is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires a current location using GPS or Wi-Fi location information of the headset type terminal 314. The generation unit calculates an optimal route by the specific processing unit 290 of the data processing device 12. The provision unit performs voice guidance by the control unit 46A of the headset type terminal 314. The reception unit accepts user input using the microphone 238 of the headset type terminal 314. The response generation unit generates a response by the specific processing unit 290 of the data processing device 12. The response provision unit provides a response using the speaker 240 of the headset type terminal 314. The this reception unit accepts the title of a book using the microphone 238 of the headset type terminal 314. The reading out provision unit reads out the contents by the specific processing unit 290 of the data processing device 12. The detection unit detects an obstacle by using the camera 42 or ultrasonic sensor of the headset type terminal 314. The feedback unit provides feedback by using the vibration motor of the headset type terminal 314. The reading out function reads out a specific chapter or page by the specific processing unit 290 of the data processing device 12. The multiple language support function responds or reads out in multiple languages ​​by the specific processing unit 290 of the data processing device 12. The provision unit obtains the latest information by the specific processing unit 290 of the data processing device 12 and provides it by using the speaker 240 of the headset type terminal 314. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the frequency of obtaining the current location. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the above-mentioned elements including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, reading-out provision unit, detection unit, feedback unit, reading-out function, multiple language support function, provision unit, and emotion estimation function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the current location using GPS or Wi-Fi location information of the robot 414. The generation unit calculates the optimal route by the specific processing unit 290 of the data processing device 12. The provision unit performs voice guidance by the control unit 46A of the robot 414. The reception unit accepts user input using the microphone 238 of the robot 414. The response generation unit generates a response by the specific processing unit 290 of the data processing device 12. The response provision unit provides a response using the speaker 240 of the robot 414. The main reception unit accepts the title of the book using the microphone 238 of the robot 414. The reading-out provision unit reads out the contents by the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensors of the robot 414. The feedback unit provides feedback using a vibration motor of the robot 414. The reading function reads out a specific chapter or page by the specific processing unit 290 of the data processing device 12. The multiple language support function responds and reads out in multiple languages ​​by the specific processing unit 290 of the data processing device 12. The provision unit obtains the latest information by the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the robot 414. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the frequency of obtaining the current location. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Appendix 1) An acquisition unit that grasps the current location; A generation unit that calculates a route to a destination based on the information acquired by the acquisition unit; a providing unit that provides route guidance based on the route generated by the generating unit; A reception unit that receives input from a user; a response generating unit that generates a response based on the information received by the receiving unit; a response providing unit that provides the response generated by the response generating unit; A book reception desk that accepts the titles of books you want to read; a reading-to-speech providing unit that reads out the contents based on the information received by the receiving unit. A system characterized by: (Appendix 2) Equipped with a detection unit that detects and avoids obstacles 2. The system of claim 1, (Appendix 3) Equipped with a feedback unit that provides vibration or tactile feedback 2. The system of claim 1, (Appendix 4) Equipped with the ability to read out specific chapters or pages 2. The system of claim 1, (Appendix 5) Multilingual capabilities 2. The system of claim 1, (Appendix 6) Equipped with a provision section that obtains the latest information online and provides it to users 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a user's emotion and adjust the frequency of obtaining a current location based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze the user's past movement history and select the optimal method for obtaining the user's current location 2. The system of claim 1, (Appendix 9) The acquisition unit is When acquiring the current location, the accuracy is improved by taking into account the user's movement speed and direction. 2. The system of claim 1, (Appendix 10) The acquisition unit is To estimate a user's emotion and adjust the timing of obtaining the current location based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 11) The acquisition unit is When obtaining the current location, the system takes into account the user's geographic location information and prioritizes obtaining information that is more relevant to the user. 2. The system of claim 1, (Appendix 12) The acquisition unit is When obtaining your location, analyze your social media activity to obtain relevant information 2. The system of claim 1, (Appendix 13) The generation unit is Inferring user emotions and adjusting route calculation algorithms based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The generation unit is When calculating a route, past travel data is referenced to predict the optimal route 2. The system of claim 1, (Appendix 15) The generation unit is When calculating a route, the system generates the optimal route by taking into account traffic conditions and weather information. 2. The system of claim 1, (Appendix 16) The generation unit is Estimate user emotions and prioritize routes based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The generation unit is When calculating a route, the system takes into account the user's geographic location to generate the optimal route. 2. The system of claim 1, (Appendix 18) The generation unit is When calculating a route, the app analyzes the user's social media activity and generates relevant routes. 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and adjust the way directions are presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The providing unit is When providing directions, the system selects the optimal guidance method by referring to the user's past travel history. 2. The system of claim 1, (Appendix 21) The providing unit is When providing directions, the timing of guidance is adjusted taking into account the user's current moving speed and direction. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and prioritize directions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing directions, the system selects the optimal guidance method based on the user's geographic location information. 2. The system of claim 1, (Appendix 24) The providing unit is When giving directions, analyze users' social media activity to provide relevant directions 2. The system of claim 1, (Appendix 25) The reception unit is Estimate the user's emotions and adjust the input acceptance method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The reception unit is When accepting input, the optimal acceptance method is selected by referring to the user's past input history. 2. The system of claim 1, (Appendix 27) The reception unit is Improve accuracy of input reception by considering the user's current situation and environment. 2. The system of claim 1, (Appendix 28) The reception unit is A user's emotion is estimated, and input acceptance priority is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The reception unit is When accepting input, the optimal acceptance method is selected taking into account the user's geographical location information. 2. The system of claim 1, (Appendix 30) The reception unit is When receiving input, analyze the user's social media activity and receive relevant input information 2. The system of claim 1, (Appendix 31) The detection unit is Estimate the user's emotions and adjust the sensitivity of obstacle detection based on the estimated user's emotions. 3. The system of claim 2, (Appendix 32) The detection unit is When detecting an obstacle, the system selects the optimal detection method by referring to the user's past movement history. 3. The system of claim 2, (Appendix 33) The detection unit is Estimate the user's emotions and prioritize obstacle detection based on the estimated user's emotions. 3. The system of claim 2, (Appendix 34) The detection unit is When detecting obstacles, the optimal detection method is selected taking into account the user's geographical location information. 3. The system of claim 2, (Appendix 35) The feedback unit is Inferring a user's emotion and adjusting the strength of the feedback based on the estimated user's emotion 4. The system of claim 3. (Appendix 36) The feedback unit is When providing feedback, select the most appropriate feedback method by referring to the user's past feedback history. 4. The system of claim 3. (Appendix 37) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 4. The system of claim 3. (Appendix 38) The feedback unit is When providing feedback, the system selects the optimal feedback method by taking into account the user's geographic location information. 4. The system of claim 3. (Appendix 39) The providing unit is Estimate the user's emotions and adjust the speech expression based on the estimated user's emotions. 2. The system of claim 1, (Appendix 40) The providing unit is When reading aloud, the system selects the optimal reading method by referring to the user's past reading history. 2. The system of claim 1, (Appendix 41) The providing unit is Estimate the user's emotions and determine the priority of speech based on the estimated user's emotions. 2. The system of claim 1, (Appendix 42) The providing unit is When reading aloud, the optimal reading method is selected taking into account the user's geographic location information. 2. The system of claim 1, [Explanation of symbols]

[0209] 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 grasps the current location; A generation unit that calculates a route to a destination based on the information acquired by the acquisition unit; a providing unit that provides route guidance based on the route generated by the generating unit; A reception unit that receives input from a user; a response generating unit that generates a response based on the information received by the receiving unit; a response providing unit that provides the response generated by the response generating unit; A book reception desk that accepts the titles of books you want to read; a reading-to-speech providing unit that reads out the contents based on the information received by the receiving unit. A system characterized by:

2. Equipped with a detection unit that detects and avoids obstacles 2. The system of claim 1 .

3. Equipped with a feedback unit that provides vibration or tactile feedback 2. The system of claim 1 .

4. Equipped with the ability to read out specific chapters or pages 2. The system of claim 1 .

5. Multilingual capabilities 2. The system of claim 1 .

6. Equipped with a provision section that obtains the latest information online and provides it to users 2. The system of claim 1 .

7. The acquisition unit is To estimate a user's emotion and adjust the frequency of obtaining a current location based on the estimated user's emotion.

2. The system of claim 1 .

8. The acquisition unit is Analyze the user's past movement history and select the optimal method for obtaining the user's current location 2. The system of claim 1 .

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