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 Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the means for visually impaired persons to receive support such as route guidance and book reading assistance when going out are limited, and there is room for improvement.

[0005] The system according to the embodiment aims to enable visually impaired persons to receive support such as route guidance and book reading assistance when going out.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a main reception unit, and a read-aloud provision unit. The acquisition unit determines the current location. The generation unit calculates the 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 receives user input. The response generation unit generates a response based on the information received by the reception unit. The response provision unit provides the response generated by the response generation unit. The main reception unit receives the title of the book the user wants to read. The read-aloud provision unit reads the contents aloud based on the information received by the main reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows visually impaired individuals to receive support such as directions and having books read aloud to them when they go out. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The wearable device according to an embodiment of the present invention is a system that not only provides directions to visually impaired people when they go out, but also acts as a conversational partner and reads aloud books they wish to read. When a visually impaired person goes out, the system determines their current location and provides directions to their destination. When the user inputs the destination by voice, the generating AI calculates the optimal route and provides voice directions. For example, it provides specific instructions such as, "Turn right at the next intersection." Next, the system functions as a conversational partner. When the user speaks, the generating AI provides an appropriate response and can continue the conversation. This allows visually impaired people to go out with peace of mind without feeling lonely. Furthermore, the system has the function of reading aloud books they wish to read. When the user inputs the title of the book they wish to read by voice, the generating AI reads the contents of that book aloud. This allows visually impaired people to enjoy reading freely. Thus, the wearable device of the present invention has multiple functions such as providing directions, conversational partners, and reading aloud books for visually impaired people when they go out, and functions as a substitute or complement to a guide dog. As a result, the wearable device can provide directions, conversational partners, and read aloud books for visually impaired people when they go out.

[0029] The wearable device according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a reception unit, and a read-aloud provision unit. The acquisition unit determines 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 the destination based on the information acquired by the acquisition unit. For example, it can calculate the shortest distance, shortest time, and a route that takes traffic conditions into account using a generation AI. The provision unit provides directions based on the route generated by the generation unit. For example, directions can be provided by methods such as voice guidance, vibration guidance, and visual guidance. The reception unit receives user input. For example, it can receive user input by methods such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information received by the reception unit. For example, it can generate a response by methods such as voice response, text response, and vibration response using a generation AI. The response provision unit provides the response generated by the response generation unit. For example, it can provide a response by methods such as voice response, text response, and vibration response. This reception desk accepts the titles of books that users wish to read. For example, book titles can be entered via voice input or text input. The text-to-speech service provides a reading service based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the reading service. This allows visually impaired individuals to receive directions, have someone to talk to, and have books read aloud for them when they are out and about.

[0030] The location acquisition unit helps visually impaired individuals determine their current location when they go out. For example, it can acquire their current location using GPS, Wi-Fi location information, and cell tower information. Specifically, GPS receives signals from satellites to pinpoint the current location with high accuracy, while Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information uses signals from cell phone base stations to determine the approximate location. By combining these technologies, high-precision location information can be acquired both indoors and outdoors. Furthermore, the location acquisition unit can collect not only location information but also information about the surrounding environment. For example, it can acquire environmental data such as ambient sound, vibration, temperature, and humidity through sensors to support visually impaired individuals in moving safely. As a result, the location acquisition unit can comprehensively collect and provide in real time the information necessary for visually impaired individuals to go out.

[0031] The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can calculate the shortest distance, shortest time, and route that takes traffic conditions into consideration. Specifically, the generation AI analyzes map data between the current location and the destination and generates the optimal route. By taking into account traffic conditions, road congestion, and construction information, it enables visually impaired people to move smoothly. In addition, the generation AI can learn past travel history and user preferences to provide individually optimized routes. For example, it can suggest a safer and more comfortable route by considering routes frequently used by visually impaired people and places they want to avoid. Furthermore, the generation unit can recalculate the route based on information that is updated in real time. For example, if an unexpected event occurs, such as a traffic accident or road closure, the generation AI will immediately calculate a new route and notify the visually impaired person. In this way, the generation unit can support visually impaired people in always traveling on the optimal route.

[0032] The service provider provides directions based on the route generated by the generation unit. For example, directions can be provided using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, voice guidance informs visually impaired users of the direction and distance to proceed, as well as obstacles to be aware of, through voice. Vibration guidance indicates direction and distance by vibrating the device, which visually impaired users can feel with their hands and arms. Visual guidance can provide information using braille displays or large print displays when used by visually impaired users. In this way, the service provider helps visually impaired people reach their destination safely and reliably. Furthermore, the service provider can customize the guidance method according to the user's preferences and circumstances. For example, vibration guidance can be prioritized for users who are uncomfortable with voice guidance, and multiple guidance methods can be combined. In addition, the service provider can modify the guidance content based on information that is updated in real time. For example, if a new obstacle appears on the route, the service provider will immediately provide new guidance so that visually impaired people can move safely. In this way, the service provider can provide an environment in which visually impaired people can go out with peace of mind.

[0033] The reception desk receives user input. For example, it can accept user input through methods such as voice input, touch input, and gesture input. Specifically, voice input is a method in which visually impaired users operate the device by speaking to it, and voice recognition technology is used to accurately understand the user's instructions. Touch input is a method in which users operate the device by touching the device's touchscreen with their fingers, and is designed so that visually impaired users can confirm the operation through touch. Gesture input is a method in which users operate the device by detecting hand and arm movements using the device's camera and sensors, and visually impaired users can operate it with natural movements. In this way, the reception desk supports visually impaired users in operating the device intuitively and easily. In addition, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a destination is specified by voice input, the reception desk will send that information to the generation unit and start route calculation. In this way, the reception desk can provide an environment in which visually impaired users can use the device smoothly.

[0034] The response generation unit generates a response based on the information received by the reception unit. For example, it can generate responses using methods such as voice responses, text responses, and vibration responses using a generation AI. Specifically, the generation AI analyzes the user's input and generates an appropriate response. Voice responses are a method of providing information to visually impaired individuals through voice, using speech synthesis technology to generate natural speech. Text responses are a method of enabling visually impaired individuals to check information using braille displays or screen readers, with the generation AI generating appropriate text. Vibration responses are a method of conveying information through device vibration, allowing visually impaired individuals to receive information through touch. In this way, the response generation unit helps visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response generation unit can customize the response content according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. In addition, the response generation unit can modify the response content based on information that is updated in real time. In this way, the response generation unit can provide an environment in which visually impaired individuals always receive the latest information.

[0035] The response provider unit provides responses generated by the response generation unit. For example, responses can be provided in the form of voice responses, text responses, or vibration responses. Specifically, voice responses are a method of providing information to visually impaired individuals through voice, playing the generated audio through speakers or earphones. Text responses allow visually impaired individuals to access information using braille displays or screen readers, displaying the generated text. Vibration responses convey information through device vibrations, allowing visually impaired individuals to receive information through touch. In this way, the response provider unit helps visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response provider unit can customize the response method according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. In addition, the response provider unit can modify the response content based on information updated in real time. In this way, the response provider unit can provide an environment where visually impaired individuals can always receive the latest information.

[0036] This reception desk accepts the titles of books that users wish to read. For example, it can accept book titles via voice input or text input. Specifically, voice input allows visually impaired users to specify book titles by speaking into the device, using voice recognition technology to accurately understand user instructions. Text input allows visually impaired users to enter book titles using a braille display or screen reader, with features designed to allow them to confirm operation through touch. This enables the reception desk to help visually impaired users intuitively and easily specify the titles of books they wish to read. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a book title is specified via voice input, the reception desk sends that information to the text-to-speech service, which then begins reading the title aloud. This allows the reception desk to provide an environment where visually impaired users can smoothly specify the books they wish to read.

[0037] The text-to-speech service provides the content based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the content. Specifically, speech synthesis technology analyzes text data and generates natural speech, providing the content of books to visually impaired individuals in audio format. The text-to-speech service can adjust the reading speed and tone of voice according to the user's preferences and circumstances. For example, it can increase the speed for users who prefer a faster reading pace and decrease it for users who prefer a slower reading pace. Furthermore, the text-to-speech service can modify the content based on information updated in real time. For example, if the content of a book is updated, the text-to-speech service will immediately read the new content. This allows the text-to-speech service to provide an environment where visually impaired individuals can always receive the latest information. In addition, the text-to-speech service can collect user feedback and continuously improve the accuracy and effectiveness of the content. This allows the text-to-speech service to provide an environment where visually impaired individuals can comfortably enjoy books.

[0038] The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has the function of detecting and avoiding obstacles when a visually impaired person is outdoors. For example, obstacles can be detected using ultrasonic sensors, cameras, or LiDAR. The detection unit can avoid obstacles by changing direction or adjusting speed. For example, it can use an ultrasonic sensor to detect an obstacle ahead and change direction. It can also use a camera to visually detect obstacles and adjust speed. Furthermore, it can use LiDAR 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 processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from ultrasonic sensors or cameras into a generating AI and have the generating AI perform obstacle detection and avoidance.

[0039] The wearable device includes a feedback unit that provides vibration or tactile feedback. The feedback unit has a function to allow visually impaired people to receive feedback when they are out and about. For example, it can provide feedback by adjusting the intensity and pattern of vibrations. The feedback unit can also provide feedback using a tactile device. For example, it can provide vibrations in a specific pattern using a vibration motor. It can also make the user feel changes in pressure or temperature using a tactile device. This makes it easier for visually impaired people to receive feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user input and environmental data into a generating AI and have the generating AI execute the optimal feedback.

[0040] Wearable devices can have a function to read aloud specific chapters or pages. This function allows visually impaired individuals to select specific chapters or pages and have them read aloud. For example, it is possible to specify a chapter number or page number for reading. The reading function can also select specific chapters or pages using keyword search. For example, if a user voice-inputs "Please read chapter 3," the specified chapter will be read aloud. Also, if a user inputs "Please read aloud pages containing the keyword 'adventure'," the corresponding pages will be read aloud. This allows visually impaired individuals to select specific chapters or pages and have them read aloud. Some or all of the above-described processes in the reading function may be performed using AI, for example, or not using AI. For example, the reading function can input the user's voice input into a generating AI and have the generating AI perform the reading of the specified chapter or page.

[0041] Wearable devices are equipped with multilingual capabilities. Multilingual capabilities allow visually impaired individuals to access information in multiple languages. For example, they can support languages ​​such as English, Japanese, and Spanish. Multilingual capabilities can accept voice and text input in multiple languages. For example, if a user inputs in English, a response will be provided in English. Similarly, if a user inputs in Japanese, a response will be provided in Japanese. Furthermore, multilingual capabilities can also perform text-to-speech in multiple languages ​​using speech synthesis technology. For example, when reading an English book, English speech synthesis is used, and when reading a Japanese book, Japanese speech synthesis is used. This allows visually impaired individuals to access information in multiple languages. Some or all of the above-described processes in multilingual capabilities may be performed using AI, or not. For example, multilingual capabilities can input user input into a generating AI, which can then perform multilingual responses and text-to-speech.

[0042] The wearable device includes a service provider that retrieves the latest information online and provides it to the user. This service provider has the functionality to enable visually impaired individuals to obtain the latest information in real time. For example, it can retrieve real-time data via the internet and provide it to the user. The service provider can also perform regular updates to always provide the latest information. For example, it can retrieve and provide the latest information such as news, weather forecasts, and traffic information. Furthermore, the service provider can retrieve and provide specific information in response to user requests. For example, if a user voice-inputs "Please tell me the latest news," the service provider can retrieve and read aloud the latest news. This allows visually impaired individuals to obtain the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data retrieved from the internet into a generating AI and have the generating AI generate information to provide to the user.

[0043] The acquisition unit can analyze the user's past movement history and select the optimal method for acquiring the current location. The acquisition unit analyzes the user's past movement history using, for example, GPS logs and records of movement routes. For example, it can adjust the accuracy of current location acquisition based on places the user has frequently visited in the past. It can also analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify places where the user has gotten lost in the past and increase the frequency of current location acquisition in those areas. This improves the accuracy of current location acquisition based on past movement history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past movement data into a generating AI and have the generating AI select the optimal method for acquiring the current location.

[0044] The acquisition unit can improve the accuracy of acquiring the current location by considering the user's movement speed and direction. For example, the acquisition unit can acquire the user's movement speed using GPS speed sensors or acceleration sensors. For example, if the user is moving at high speed, the accuracy of acquiring the current location can be increased. It can also acquire the user's movement direction using a compass or gyro sensor, and increase the frequency of acquiring the current location if the user frequently changes direction. Furthermore, if the user is walking, the accuracy of acquiring the current location can be adjusted according to the movement speed. In this way, the accuracy of acquiring the current location is improved by considering the movement speed and direction. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input movement speed and direction data into a generating AI and have the generating AI perform the task of improving the accuracy of acquiring the current location.

[0045] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information when acquiring the user's current location. The acquisition unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can prioritize the acquisition of information about tourist spots. If the user is in a commercial facility, it can also prioritize the acquisition of store information. Furthermore, if the user is using public transportation, it can also prioritize the acquisition of service information. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI execute the acquisition of highly relevant information.

[0046] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring the current location. For example, the acquisition unit can analyze the user's social media activity. For example, it can acquire information about places where the user has checked in on social media. It can also acquire the current location based on the location information of photos shared by the user on social media. Furthermore, it can prioritize acquiring information about places that the user follows on social media. In this way, relevant information can be obtained by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0047] The generation unit can predict the optimal route by referring to past travel data during route calculation. The generation unit refers to past travel data, for example, using GPS logs or records of travel routes. For example, it predicts the optimal route based on routes previously used by the user. It can also predict routes that avoid congestion from the user's past travel data. Furthermore, it can analyze the user's past travel data and predict the most efficient route. In this way, the optimal route can be predicted by referring to past travel data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the prediction of the optimal route.

[0048] The generation unit can generate the optimal route by considering traffic conditions and weather information during route calculation. For example, the generation unit can generate the optimal route based on real-time traffic congestion information. For example, it can calculate a route that avoids congestion using traffic information obtained from traffic sensors or the internet. It can also generate the optimal route by considering real-time weather information. For example, it can calculate a route that avoids rain or snow based on weather data. Furthermore, it can generate the optimal route by considering the real-time operation status of public transportation. For example, it can calculate the most efficient route based on operation information. In this way, the optimal route can be generated by considering traffic conditions and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input traffic information and weather information into a generation AI and have the generation AI execute the generation of the optimal route.

[0049] The generation unit can generate the optimal route by considering the user's geographical location information during route calculation. The generation unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can generate a route that passes through tourist spots. If the user is in a commercial facility, it can also generate a route that takes store information into account. Furthermore, if the user is using public transportation, it can also generate a route that takes service information into account. In this way, the optimal route can be generated by considering geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform the generation of the optimal route.

[0050] The generation unit can analyze a user's social media activity and generate relevant routes when calculating routes. For example, the generation unit can analyze a user's social media activity. For example, it can generate a route that passes through places the user has checked in to on social media. It can also generate a route based on the location information of photos the user has shared on social media. Furthermore, it can generate a route that passes through places the user follows on social media. In this way, relevant routes can be generated by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input social media data into a generation AI and have the generation AI perform the generation of relevant routes.

[0051] The service provider can select the optimal guidance method by referring to the user's past travel history when providing directions. The service provider can refer to the user's past travel history, for example, by using GPS logs or records of travel routes. For example, it can select the optimal guidance method based on routes the user has used in the past. It can also select a guidance method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past travel data into a generating AI and have the generating AI perform the selection of the optimal guidance method.

[0052] The service provider can adjust the timing of directions by considering the user's current speed and direction. For example, the service provider can obtain the user's speed using GPS speed sensors or acceleration sensors. For instance, if the user is moving at high speed, the service provider can advance the timing of directions. It can also obtain the user's direction of movement using a compass or gyroscope sensor, and shorten the timing of directions if the user frequently changes direction. Furthermore, if the user is walking, the service provider can adjust the timing of directions according to their speed. In this way, the timing of directions can be optimized by considering the speed and direction of movement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input speed and direction data into a generating AI and have the generating AI adjust the timing of directions.

[0053] The service provider can select the optimal guidance method when providing directions, taking into account the user's geographical location information. The service provider can acquire the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can select a guidance method that goes through tourist spots. If the user is in a commercial facility, it can also select a guidance method that takes store information into account. Furthermore, if the user is using public transportation, it can also select a guidance method that takes service information into account. In this way, the optimal guidance method can be selected by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal guidance method.

[0054] The service provider can analyze the user's social media activity and provide relevant guidance information when providing directions. For example, the service provider can analyze the user's social media activity. For example, it can provide information about places the user has checked in to on social media. It can also provide guidance information based on the location information of photos the user has shared on social media. Furthermore, it can provide information about places the user follows on social media. In this way, relevant guidance information can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the task of providing relevant 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 can refer to the user's past input history using, for example, input logs and records of input content. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also provide predictive input based on the content the user has entered in the past. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by referring to past input history. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input past input data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0056] The reception unit can improve the accuracy of its reception process by considering the user's current situation and environment when receiving input. For example, the reception unit can acquire information about the user's current situation and environment using ambient noise, light intensity, temperature, etc. For instance, if the user is in a noisy environment, it can improve the accuracy of voice input. It can also adjust the timing of input reception if the user is on the move. Furthermore, if the user is in a quiet environment, it can provide detailed input options. This improves the accuracy of reception by considering the current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input environmental data into a generating AI and have the generating AI perform the necessary adjustments to improve reception accuracy.

[0057] The reception unit can select the optimal reception method by considering the user's geographical location information when receiving input. The reception unit can obtain the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize receiving tourist information. Similarly, if the user is in a commercial facility, it can prioritize receiving store information. Furthermore, if the user is using public transportation, it can prioritize receiving service information. In this way, the optimal reception method can be selected by considering the geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select the optimal reception method.

[0058] The reception unit can analyze the user's social media activity and receive relevant input information when receiving input. For example, the reception unit can analyze the user's social media activity. For example, it can receive information about places the user has checked in to on social media. It can also receive input information based on the location information of photos the user has shared on social media. Furthermore, it can receive information about places the user follows on social media. In this way, relevant input information can be received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input social media data into a generating AI and have the generating AI perform the reception of relevant input information.

[0059] The detection unit can select the optimal detection method by referring to the user's past movement history when an obstacle is detected. The detection unit refers to the user's past movement history, for example, using GPS logs or records of movement routes. For example, it can select the optimal detection method based on routes that the user has frequently used in the past. It can also select a detection method that avoids congestion based on the user's past movement history. Furthermore, it can analyze the user's past movement history and select the most efficient detection method. In this way, the optimal detection method can be selected by referring to past movement history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past movement data into a generating AI and have the generating AI perform the selection of the optimal detection method.

[0060] The detection unit can select the optimal detection method when detecting an obstacle, taking into account the user's geographical location information. The detection unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize detecting obstacles around tourist spots. If the user is in a commercial facility, it can also prioritize detecting obstacles around stores. Furthermore, if the user is using public transportation, it can also prioritize detecting obstacles around stations and bus stops. This allows the detection unit to select the optimal detection method by considering geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI select the optimal detection method.

[0061] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. The feedback unit can refer to the user's past feedback history, for example, by using feedback logs or records of feedback content. For example, it can provide optimal feedback based on the feedback methods the user has preferred in the past. It can also select a feedback method for a specific situation from the user's past feedback history. Furthermore, it can analyze the user's past feedback history and select the most effective feedback method. In this way, the optimal feedback method can be selected by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI perform the selection of the optimal feedback method.

[0062] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. The feedback unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can prioritize providing feedback around tourist spots. If the user is in a commercial facility, it can also prioritize providing feedback around stores. Furthermore, if the user is using public transportation, it can also prioritize providing feedback around stations and bus stops. In this way, the optimal feedback method can be selected by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0063] The service provider can select the optimal reading method by referring to the user's past reading history during reading. The service provider can refer to the user's past reading history, for example, by using reading logs or records of reading content. For example, it can provide the optimal reading method based on the reading method the user has preferred in the past. It can also select a reading method for a specific genre or author from the user's past reading history. Furthermore, it can analyze the user's past reading history and select the most effective reading method. In this way, the optimal reading method can be selected by referring to the past reading history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past reading data into a generating AI and have the generating AI select the optimal reading method.

[0064] The service provider can select the optimal reading method by considering the user's geographical location information during reading. The service provider can acquire the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize reading information related to tourist spots. If the user is in a commercial facility, it can also prioritize reading store information. Furthermore, if the user is using public transportation, it can also prioritize reading service information. In this way, the service provider can select the optimal reading method by considering the geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal reading method.

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

[0066] Wearable devices can also be equipped with a health management unit that monitors the user's health status. This unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if the heart rate is abnormally high, it can provide an alert prompting the user to take a break. It can also provide guidance on relaxation if blood pressure is high. Furthermore, if body temperature is elevated, it can warn of the risk of heatstroke. This makes it easier for visually impaired individuals to manage their health while outdoors.

[0067] Wearable devices can also be equipped with a meal management unit to support users in managing their diet. This unit records the contents of meals consumed by the user and analyzes nutritional balance. For example, if the user inputs the contents of their meal by voice, it can provide information on calories and nutrients. It can also suggest ingredients and recipes containing specific nutrients if the user needs to consume them. Furthermore, it can provide healthy eating advice based on the user's meal history. This makes it easier for visually impaired individuals to maintain a healthy diet.

[0068] Wearable devices can also be equipped with an exercise management unit to support the user's physical activity. This unit records the user's exercise volume and type, and analyzes the effects of the exercise. For example, if the user walks or runs, it calculates the distance and calories burned. Furthermore, if the user sets specific exercise goals, it can monitor their progress and report on their achievement. It can also provide advice on the user's form and posture while exercising. This makes it easier for visually impaired individuals to exercise effectively.

[0069] Wearable devices can also be equipped with a sleep management unit to support the user's sleep. This unit records the user's sleep patterns and analyzes sleep quality. For example, it can provide music or guidance to help the user relax before going to sleep. It can also provide an alarm to encourage natural waking. Furthermore, it can offer advice to improve sleep quality based on the user's sleep data. This can make it easier for visually impaired individuals to achieve high-quality sleep.

[0070] Wearable devices can also be equipped with a learning support unit to assist users in their learning. This unit provides information related to what the user wants to learn, based on voice input. For example, if a user wants to learn about a specific topic, it can provide articles and audio materials on that topic. If a user wants to learn a language, it can also provide pronunciation practice and teach word meanings. Furthermore, it can record the user's learning progress and suggest what to learn next. This makes it easier for visually impaired individuals to learn effectively even when they are out and about.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The acquisition unit determines the current location of a visually impaired person when they go out. For example, it acquires the current location using GPS, Wi-Fi location information, cell tower information, etc. Step 2: The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, it uses a generation AI to calculate the shortest distance, shortest time, and a route that takes traffic conditions into consideration. Step 3: The providing unit provides directions based on the route generated by the generating unit. For example, directions are provided using methods such as voice guidance, vibration guidance, and visual guidance. Step 4: The reception desk receives user input. For example, it accepts user input through methods 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, it generates a response using a generation AI in the form of voice response, text response, vibration response, etc. Step 6: The response provider unit provides the response generated by the response generator unit. For example, the response is provided by methods such as voice response, text response, or vibration response. Step 7: This reception desk accepts the title of the book you want to read. For example, it accepts the book title via voice input, text input, etc. Step 8: The text-to-speech service unit reads the content aloud based on the information received by the reception unit. For example, it may use speech synthesis technology to perform the reading.

[0073] (Example of form 2) The wearable device according to an embodiment of the present invention is a system that not only provides directions to visually impaired people when they go out, but also acts as a conversational partner and reads aloud books they wish to read. When a visually impaired person goes out, the system determines their current location and provides directions to their destination. When the user inputs the destination by voice, the generating AI calculates the optimal route and provides voice directions. For example, it provides specific instructions such as, "Turn right at the next intersection." Next, the system functions as a conversational partner. When the user speaks, the generating AI provides an appropriate response and can continue the conversation. This allows visually impaired people to go out with peace of mind without feeling lonely. Furthermore, the system has the function of reading aloud books they wish to read. When the user inputs the title of the book they wish to read by voice, the generating AI reads the contents of that book aloud. This allows visually impaired people to enjoy reading freely. Thus, the wearable device of the present invention has multiple functions such as providing directions, conversational partners, and reading aloud books for visually impaired people when they go out, and functions as a substitute or complement to a guide dog. As a result, the wearable device can provide directions, conversational partners, and read aloud books for visually impaired people when they go out.

[0074] The wearable device according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a reception unit, and a read-aloud provision unit. The acquisition unit determines 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 the destination based on the information acquired by the acquisition unit. For example, it can calculate the shortest distance, shortest time, and a route that takes traffic conditions into account using a generation AI. The provision unit provides directions based on the route generated by the generation unit. For example, directions can be provided by methods such as voice guidance, vibration guidance, and visual guidance. The reception unit receives user input. For example, it can receive user input by methods such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information received by the reception unit. For example, it can generate a response by methods such as voice response, text response, and vibration response using a generation AI. The response provision unit provides the response generated by the response generation unit. For example, it can provide a response by methods such as voice response, text response, and vibration response. This reception desk accepts the titles of books that users wish to read. For example, book titles can be entered via voice input or text input. The text-to-speech service provides a reading service based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the reading service. This allows visually impaired individuals to receive directions, have someone to talk to, and have books read aloud for them when they are out and about.

[0075] The location acquisition unit helps visually impaired individuals determine their current location when they go out. For example, it can acquire their current location using GPS, Wi-Fi location information, and cell tower information. Specifically, GPS receives signals from satellites to pinpoint the current location with high accuracy, while Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information uses signals from cell phone base stations to determine the approximate location. By combining these technologies, high-precision location information can be acquired both indoors and outdoors. Furthermore, the location acquisition unit can collect not only location information but also information about the surrounding environment. For example, it can acquire environmental data such as ambient sound, vibration, temperature, and humidity through sensors to support visually impaired individuals in moving safely. As a result, the location acquisition unit can comprehensively collect and provide in real time the information necessary for visually impaired individuals to go out.

[0076] The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can calculate the shortest distance, shortest time, and route that takes traffic conditions into consideration. Specifically, the generation AI analyzes map data between the current location and the destination and generates the optimal route. By taking into account traffic conditions, road congestion, and construction information, it enables visually impaired people to move smoothly. In addition, the generation AI can learn past travel history and user preferences to provide individually optimized routes. For example, it can suggest a safer and more comfortable route by considering routes frequently used by visually impaired people and places they want to avoid. Furthermore, the generation unit can recalculate the route based on information that is updated in real time. For example, if an unexpected event occurs, such as a traffic accident or road closure, the generation AI will immediately calculate a new route and notify the visually impaired person. In this way, the generation unit can support visually impaired people in always traveling on the optimal route.

[0077] The service provider provides directions based on the route generated by the generation unit. For example, directions can be provided using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, voice guidance informs visually impaired users of the direction and distance to proceed, as well as obstacles to be aware of, through voice. Vibration guidance indicates direction and distance by vibrating the device, which visually impaired users can feel with their hands and arms. Visual guidance can provide information using braille displays or large print displays when used by visually impaired users. In this way, the service provider helps visually impaired people reach their destination safely and reliably. Furthermore, the service provider can customize the guidance method according to the user's preferences and circumstances. For example, vibration guidance can be prioritized for users who are uncomfortable with voice guidance, and multiple guidance methods can be combined. In addition, the service provider can modify the guidance content based on information that is updated in real time. For example, if a new obstacle appears on the route, the service provider will immediately provide new guidance so that visually impaired people can move safely. In this way, the service provider can provide an environment in which visually impaired people can go out with peace of mind.

[0078] The reception desk receives user input. For example, it can accept user input through methods such as voice input, touch input, and gesture input. Specifically, voice input is a method in which visually impaired users operate the device by speaking to it, and voice recognition technology is used to accurately understand the user's instructions. Touch input is a method in which users operate the device by touching the device's touchscreen with their fingers, and is designed so that visually impaired users can confirm the operation through touch. Gesture input is a method in which users operate the device by detecting hand and arm movements using the device's camera and sensors, and visually impaired users can operate it with natural movements. In this way, the reception desk supports visually impaired users in operating the device intuitively and easily. In addition, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a destination is specified by voice input, the reception desk will send that information to the generation unit and start route calculation. In this way, the reception desk can provide an environment in which visually impaired users can use the device smoothly.

[0079] The response generation unit generates a response based on the information received by the reception unit. For example, it can generate responses using methods such as voice responses, text responses, and vibration responses using a generation AI. Specifically, the generation AI analyzes the user's input and generates an appropriate response. Voice responses are a method of providing information to visually impaired individuals through voice, using speech synthesis technology to generate natural speech. Text responses are a method of enabling visually impaired individuals to check information using braille displays or screen readers, with the generation AI generating appropriate text. Vibration responses are a method of conveying information through device vibration, allowing visually impaired individuals to receive information through touch. In this way, the response generation unit helps visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response generation unit can customize the response content according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. In addition, the response generation unit can modify the response content based on information that is updated in real time. In this way, the response generation unit can provide an environment in which visually impaired individuals always receive the latest information.

[0080] The response provider unit provides responses generated by the response generation unit. For example, responses can be provided in the form of voice responses, text responses, or vibration responses. Specifically, voice responses are a method of providing information to visually impaired individuals through voice, playing the generated audio through speakers or earphones. Text responses allow visually impaired individuals to access information using braille displays or screen readers, displaying the generated text. Vibration responses convey information through device vibrations, allowing visually impaired individuals to receive information through touch. In this way, the response provider unit helps visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response provider unit can customize the response method according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. In addition, the response provider unit can modify the response content based on information updated in real time. In this way, the response provider unit can provide an environment where visually impaired individuals can always receive the latest information.

[0081] This reception desk accepts the titles of books that users wish to read. For example, it can accept book titles via voice input or text input. Specifically, voice input allows visually impaired users to specify book titles by speaking into the device, using voice recognition technology to accurately understand user instructions. Text input allows visually impaired users to enter book titles using a braille display or screen reader, with features designed to allow them to confirm operation through touch. This enables the reception desk to help visually impaired users intuitively and easily specify the titles of books they wish to read. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a book title is specified via voice input, the reception desk sends that information to the text-to-speech service, which then begins reading the title aloud. This allows the reception desk to provide an environment where visually impaired users can smoothly specify the books they wish to read.

[0082] The text-to-speech service provides the content based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the content. Specifically, speech synthesis technology analyzes text data and generates natural speech, providing the content of books to visually impaired individuals in audio format. The text-to-speech service can adjust the reading speed and tone of voice according to the user's preferences and circumstances. For example, it can increase the speed for users who prefer a faster reading pace and decrease it for users who prefer a slower reading pace. Furthermore, the text-to-speech service can modify the content based on information updated in real time. For example, if the content of a book is updated, the text-to-speech service will immediately read the new content. This allows the text-to-speech service to provide an environment where visually impaired individuals can always receive the latest information. In addition, the text-to-speech service can collect user feedback and continuously improve the accuracy and effectiveness of the content. This allows the text-to-speech service to provide an environment where visually impaired individuals can comfortably enjoy books.

[0083] The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has the function of detecting and avoiding obstacles when a visually impaired person is outdoors. For example, obstacles can be detected using ultrasonic sensors, cameras, or LiDAR. The detection unit can avoid obstacles by changing direction or adjusting speed. For example, it can use an ultrasonic sensor to detect an obstacle ahead and change direction. It can also use a camera to visually detect obstacles and adjust speed. Furthermore, it can use LiDAR 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 processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from ultrasonic sensors or cameras into a generating AI and have the generating AI perform obstacle detection and avoidance.

[0084] The wearable device includes a feedback unit that provides vibration or tactile feedback. The feedback unit has a function to allow visually impaired people to receive feedback when they are out and about. For example, it can provide feedback by adjusting the intensity and pattern of vibrations. The feedback unit can also provide feedback using a tactile device. For example, it can provide vibrations in a specific pattern using a vibration motor. It can also make the user feel changes in pressure or temperature using a tactile device. This makes it easier for visually impaired people to receive feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user input and environmental data into a generating AI and have the generating AI execute the optimal feedback.

[0085] Wearable devices can have a function to read aloud specific chapters or pages. This function allows visually impaired individuals to select specific chapters or pages and have them read aloud. For example, it is possible to specify a chapter number or page number for reading. The reading function can also select specific chapters or pages using keyword search. For example, if a user voice-inputs "Please read chapter 3," the specified chapter will be read aloud. Also, if a user inputs "Please read aloud pages containing the keyword 'adventure'," the corresponding pages will be read aloud. This allows visually impaired individuals to select specific chapters or pages and have them read aloud. Some or all of the above-described processes in the reading function may be performed using AI, for example, or not using AI. For example, the reading function can input the user's voice input into a generating AI and have the generating AI perform the reading of the specified chapter or page.

[0086] Wearable devices are equipped with multilingual capabilities. Multilingual capabilities allow visually impaired individuals to access information in multiple languages. For example, they can support languages ​​such as English, Japanese, and Spanish. Multilingual capabilities can accept voice and text input in multiple languages. For example, if a user inputs in English, a response will be provided in English. Similarly, if a user inputs in Japanese, a response will be provided in Japanese. Furthermore, multilingual capabilities can also perform text-to-speech in multiple languages ​​using speech synthesis technology. For example, when reading an English book, English speech synthesis is used, and when reading a Japanese book, Japanese speech synthesis is used. This allows visually impaired individuals to access information in multiple languages. Some or all of the above-described processes in multilingual capabilities may be performed using AI, or not. For example, multilingual capabilities can input user input into a generating AI, which can then perform multilingual responses and text-to-speech.

[0087] The wearable device includes a service provider that retrieves the latest information online and provides it to the user. This service provider has the functionality to enable visually impaired individuals to obtain the latest information in real time. For example, it can retrieve real-time data via the internet and provide it to the user. The service provider can also perform regular updates to always provide the latest information. For example, it can retrieve and provide the latest information such as news, weather forecasts, and traffic information. Furthermore, the service provider can retrieve and provide specific information in response to user requests. For example, if a user voice-inputs "Please tell me the latest news," the service provider can retrieve and read aloud the latest news. This allows visually impaired individuals to obtain the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data retrieved from the internet into a generating AI and have the generating AI generate information to provide to the user.

[0088] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring the current location based on the estimated emotions. The acquisition unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, the frequency of acquiring the current location can be increased to provide more detailed navigation information. Also, if the user is relaxed, the frequency of acquiring the current location can be decreased to conserve battery power. Furthermore, if the user is in a hurry, the frequency of acquiring the current location can be increased to provide faster navigation. In this way, by adjusting the frequency of acquiring the current location according to the user's emotions, more appropriate navigation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the frequency of acquiring the current location.

[0089] The acquisition unit can analyze the user's past movement history and select the optimal method for acquiring the current location. The acquisition unit analyzes the user's past movement history using, for example, GPS logs and records of movement routes. For example, it can adjust the accuracy of current location acquisition based on places the user has frequently visited in the past. It can also analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify places where the user has gotten lost in the past and increase the frequency of current location acquisition in those areas. This improves the accuracy of current location acquisition based on past movement history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past movement data into a generating AI and have the generating AI select the optimal method for acquiring the current location.

[0090] The acquisition unit can improve the accuracy of acquiring the current location by considering the user's movement speed and direction. For example, the acquisition unit can acquire the user's movement speed using GPS speed sensors or acceleration sensors. For example, if the user is moving at high speed, the accuracy of acquiring the current location can be increased. It can also acquire the user's movement direction using a compass or gyro sensor, and increase the frequency of acquiring the current location if the user frequently changes direction. Furthermore, if the user is walking, the accuracy of acquiring the current location can be adjusted according to the movement speed. In this way, the accuracy of acquiring the current location is improved by considering the movement speed and direction. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input movement speed and direction data into a generating AI and have the generating AI perform the task of improving the accuracy of acquiring the current location.

[0091] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the current location based on the estimated user emotions. The acquisition unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is nervous, the timing of acquiring the current location can be shortened to provide a sense of security. Conversely, if the user is relaxed, the timing of acquiring the current location can be lengthened to reduce battery consumption. Furthermore, if the user is in a hurry, the timing of acquiring the current location can be shortened to provide rapid navigation. In this way, adjusting the timing of acquiring the current location according to the user's emotions enables more appropriate navigation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the timing of acquiring the current location.

[0092] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information when acquiring the user's current location. The acquisition unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can prioritize the acquisition of information about tourist spots. If the user is in a commercial facility, it can also prioritize the acquisition of store information. Furthermore, if the user is using public transportation, it can also prioritize the acquisition of service information. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI execute the acquisition of highly relevant information.

[0093] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring the current location. For example, the acquisition unit can analyze the user's social media activity. For example, it can acquire information about places where the user has checked in on social media. It can also acquire the current location based on the location information of photos shared by the user on social media. Furthermore, it can prioritize acquiring information about places that the user follows on social media. In this way, relevant information can be obtained by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0094] The generation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated emotions. The generation unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, it can use an algorithm that prioritizes the safest route. If the user is relaxed, it can also use an algorithm that prioritizes a route with good scenery. Furthermore, if the user is in a hurry, it can use an algorithm that prioritizes the route that can be reached in the shortest time. In this way, by adjusting the route calculation algorithm according to the user's emotions, a more appropriate route can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the route calculation algorithm.

[0095] The generation unit can predict the optimal route by referring to past travel data during route calculation. The generation unit refers to past travel data, for example, using GPS logs or records of travel routes. For example, it predicts the optimal route based on routes previously used by the user. It can also predict routes that avoid congestion from the user's past travel data. Furthermore, it can analyze the user's past travel data and predict the most efficient route. In this way, the optimal route can be predicted by referring to past travel data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the prediction of the optimal route.

[0096] The generation unit can generate the optimal route by considering traffic conditions and weather information during route calculation. For example, the generation unit can generate the optimal route based on real-time traffic congestion information. For example, it can calculate a route that avoids congestion using traffic information obtained from traffic sensors or the internet. It can also generate the optimal route by considering real-time weather information. For example, it can calculate a route that avoids rain or snow based on weather data. Furthermore, it can generate the optimal route by considering the real-time operation status of public transportation. For example, it can calculate the most efficient route based on operation information. In this way, the optimal route can be generated by considering traffic conditions and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input traffic information and weather information into a generation AI and have the generation AI execute the generation of the optimal route.

[0097] The generation unit can estimate the user's emotions and determine route priorities based on those emotions. The generation unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, it may prioritize a safe route. If the user is relaxed, it may also prioritize a route with good scenery. Furthermore, if the user is in a hurry, it may prioritize a route that can be reached in the shortest time. This allows for the provision of more appropriate routes by prioritizing routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and route prioritization.

[0098] The generation unit can generate the optimal route by considering the user's geographical location information during route calculation. The generation unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can generate a route that passes through tourist spots. If the user is in a commercial facility, it can also generate a route that takes store information into account. Furthermore, if the user is using public transportation, it can also generate a route that takes service information into account. In this way, the optimal route can be generated by considering geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform the generation of the optimal route.

[0099] The generation unit can analyze a user's social media activity and generate relevant routes when calculating routes. For example, the generation unit can analyze a user's social media activity. For example, it can generate a route that passes through places the user has checked in to on social media. It can also generate a route based on the location information of photos the user has shared on social media. Furthermore, it can generate a route that passes through places the user follows on social media. In this way, relevant routes can be generated by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input social media data into a generation AI and have the generation AI perform the generation of relevant routes.

[0100] The service provider can estimate the user's emotions and adjust the way directions are presented based on the estimated emotions. The service provider estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, detailed directions can be provided. If the user is relaxed, concise directions can be provided. Furthermore, if the user is in a hurry, quick directions can be provided. By adjusting the way directions are presented according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the way directions are presented.

[0101] The service provider can select the optimal guidance method by referring to the user's past travel history when providing directions. The service provider can refer to the user's past travel history, for example, by using GPS logs or records of travel routes. For example, it can select the optimal guidance method based on routes the user has used in the past. It can also select a guidance method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past travel data into a generating AI and have the generating AI perform the selection of the optimal guidance method.

[0102] The service provider can adjust the timing of directions by considering the user's current speed and direction. For example, the service provider can obtain the user's speed using GPS speed sensors or acceleration sensors. For instance, if the user is moving at high speed, the service provider can advance the timing of directions. It can also obtain the user's direction of movement using a compass or gyroscope sensor, and shorten the timing of directions if the user frequently changes direction. Furthermore, if the user is walking, the service provider can adjust the timing of directions according to their speed. In this way, the timing of directions can be optimized by considering the speed and direction of movement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input speed and direction data into a generating AI and have the generating AI adjust the timing of directions.

[0103] The service provider can estimate the user's emotions and determine the priority of directions based on the estimated emotions. The service provider estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, it will prioritize a safe route. If the user is relaxed, it may also prioritize a route with good scenery. Furthermore, if the user is in a hurry, it may also prioritize the route that can be reached in the shortest time. In this way, by determining the priority of directions according to the user's emotions, more appropriate directions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's voice data and facial expression data into a generative AI and have the generative AI perform emotion estimation and determination of directions priority.

[0104] The service provider can select the optimal guidance method when providing directions, taking into account the user's geographical location information. The service provider can acquire the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can select a guidance method that goes through tourist spots. If the user is in a commercial facility, it can also select a guidance method that takes store information into account. Furthermore, if the user is using public transportation, it can also select a guidance method that takes service information into account. In this way, the optimal guidance method can be selected by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal guidance method.

[0105] The service provider can analyze the user's social media activity and provide relevant guidance information when providing directions. For example, the service provider can analyze the user's social media activity. For example, it can provide information about places the user has checked in to on social media. It can also provide guidance information based on the location information of photos the user has shared on social media. Furthermore, it can provide information about places the user follows on social media. In this way, relevant guidance information can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the task of providing relevant guidance information.

[0106] The reception unit can estimate the user's emotions and adjust the input reception method based on the estimated emotions. The reception unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input and accept input quickly. This allows for more appropriate input by adjusting the input reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform 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 can refer to the user's past input history using, for example, input logs and records of input content. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also provide predictive input based on the content the user has entered in the past. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by referring to past input history. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input past input data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0108] The reception unit can improve the accuracy of its reception process by considering the user's current situation and environment when receiving input. For example, the reception unit can acquire information about the user's current situation and environment using ambient noise, light intensity, temperature, etc. For instance, if the user is in a noisy environment, it can improve the accuracy of voice input. It can also adjust the timing of input reception if the user is on the move. Furthermore, if the user is in a quiet environment, it can provide detailed input options. This improves the accuracy of reception by considering the current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input environmental data into a generating AI and have the generating AI perform the necessary adjustments to improve reception accuracy.

[0109] The reception unit can estimate the user's emotions and determine the priority of input processing based on the estimated emotions. The reception unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, important inputs may be prioritized. If the user is relaxed, detailed inputs may be prioritized. Furthermore, if the user is in a hurry, quick inputs may be prioritized. This allows for more appropriate input by determining the priority of input processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's voice data and facial expression data into a generative AI and have the generative AI perform emotion estimation and determine the priority of input processing.

[0110] The reception unit can select the optimal reception method by considering the user's geographical location information when receiving input. The reception unit can obtain the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize receiving tourist information. Similarly, if the user is in a commercial facility, it can prioritize receiving store information. Furthermore, if the user is using public transportation, it can prioritize receiving service information. In this way, the optimal reception method can be selected by considering the geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select the optimal reception method.

[0111] The reception unit can analyze the user's social media activity and receive relevant input information when receiving input. For example, the reception unit can analyze the user's social media activity. For example, it can receive information about places the user has checked in to on social media. It can also receive input information based on the location information of photos the user has shared on social media. Furthermore, it can receive information about places the user follows on social media. In this way, relevant input information can be received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input social media data into a generating AI and have the generating AI perform the reception of relevant input information.

[0112] The detection unit can estimate the user's emotions and adjust the sensitivity of obstacle detection based on the estimated emotions. The detection unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, the sensitivity of obstacle detection can be increased. Conversely, if the user is relaxed, the sensitivity of obstacle detection can be decreased. Furthermore, if the user is in a hurry, the sensitivity of obstacle detection can be increased. In this way, by adjusting the sensitivity of obstacle detection according to the user's emotions, more appropriate detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of obstacle detection sensitivity.

[0113] The detection unit can select the optimal detection method by referring to the user's past movement history when an obstacle is detected. The detection unit refers to the user's past movement history, for example, using GPS logs or records of movement routes. For example, it can select the optimal detection method based on routes that the user has frequently used in the past. It can also select a detection method that avoids congestion based on the user's past movement history. Furthermore, it can analyze the user's past movement history and select the most efficient detection method. In this way, the optimal detection method can be selected by referring to past movement history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past movement data into a generating AI and have the generating AI perform the selection of the optimal detection method.

[0114] The detection unit can estimate the user's emotions and determine the priority of obstacle detection based on the estimated user emotions. The detection unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, it can prioritize detecting important obstacles. If the user is relaxed, it can also prioritize detecting detailed obstacles. Furthermore, if the user is in a hurry, it can also prioritize detecting obstacles quickly. This allows for more appropriate detection by determining the priority of obstacle detection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determination of obstacle detection priorities.

[0115] The detection unit can select the optimal detection method when detecting an obstacle, taking into account the user's geographical location information. The detection unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize detecting obstacles around tourist spots. If the user is in a commercial facility, it can also prioritize detecting obstacles around stores. Furthermore, if the user is using public transportation, it can also prioritize detecting obstacles around stations and bus stops. This allows the detection unit to select the optimal detection method by considering geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI select the optimal detection method.

[0116] The feedback unit can estimate the user's emotions and adjust the intensity of the feedback based on the estimated emotions. The feedback unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, it can provide strong feedback. If the user is relaxed, it can provide weak feedback. Furthermore, if the user is in a hurry, it can provide rapid feedback. By adjusting the intensity of the feedback according to the user's emotions, more appropriate feedback becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the feedback intensity.

[0117] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. The feedback unit can refer to the user's past feedback history, for example, by using feedback logs or records of feedback content. For example, it can provide optimal feedback based on the feedback methods the user has preferred in the past. It can also select a feedback method for a specific situation from the user's past feedback history. Furthermore, it can analyze the user's past feedback history and select the most effective feedback method. In this way, the optimal feedback method can be selected by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI perform the selection of the optimal feedback method.

[0118] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. The feedback unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, important feedback can be prioritized. If the user is relaxed, detailed feedback can be prioritized. Furthermore, if the user is in a hurry, quick feedback can be prioritized. This allows for more appropriate feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determine the priority of feedback.

[0119] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. The feedback unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, if the user is in a tourist area, it can prioritize providing feedback around tourist spots. If the user is in a commercial facility, it can also prioritize providing feedback around stores. Furthermore, if the user is using public transportation, it can also prioritize providing feedback around stations and bus stops. In this way, the optimal feedback method can be selected by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0120] The service provider can estimate the user's emotions and adjust the reading style based on the estimated emotions. The service provider estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, the service provider will read in a calm voice. If the user is relaxed, the service provider can also read in a cheerful voice. Furthermore, if the user is in a hurry, the service provider can read quickly and concisely. By adjusting the reading style according to the user's emotions, more appropriate reading becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the reading style.

[0121] The service provider can select the optimal reading method by referring to the user's past reading history during reading. The service provider can refer to the user's past reading history, for example, by using reading logs or records of reading content. For example, it can provide the optimal reading method based on the reading method the user has preferred in the past. It can also select a reading method for a specific genre or author from the user's past reading history. Furthermore, it can analyze the user's past reading history and select the most effective reading method. In this way, the optimal reading method can be selected by referring to the past reading history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past reading data into a generating AI and have the generating AI select the optimal reading method.

[0122] The service provider can estimate the user's emotions and determine the reading priority based on the estimated emotions. The service provider estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, important information will be prioritized for reading. If the user is relaxed, detailed information may be prioritized for reading. Furthermore, if the user is in a hurry, quick information may be prioritized for reading. This allows for more appropriate reading by determining the reading priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determination of reading priority.

[0123] The service provider can select the optimal reading method by considering the user's geographical location information during reading. The service provider can acquire the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize reading information related to tourist spots. If the user is in a commercial facility, it can also prioritize reading store information. Furthermore, if the user is using public transportation, it can also prioritize reading service information. In this way, the service provider can select the optimal reading method by considering the geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal reading method.

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

[0125] Wearable devices can also be equipped with a health management unit that monitors the user's health status. This unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if the heart rate is abnormally high, it can provide an alert prompting the user to take a break. It can also provide guidance on relaxation if blood pressure is high. Furthermore, if body temperature is elevated, it can warn of the risk of heatstroke. This makes it easier for visually impaired individuals to manage their health while outdoors.

[0126] Wearable devices can also include a music provider that estimates the user's emotions and selects music based on those emotions. For example, if the user is relaxed, it can provide calming music. If the user is stressed, it can provide relaxing music. Furthermore, if the user wants to feel energized, it can provide upbeat music. This allows visually impaired individuals to enjoy music that matches their emotions even when they are out and about.

[0127] Wearable devices can also be equipped with a meal management unit to support users in managing their diet. This unit records the contents of meals consumed by the user and analyzes nutritional balance. For example, if the user inputs the contents of their meal by voice, it can provide information on calories and nutrients. It can also suggest ingredients and recipes containing specific nutrients if the user needs to consume them. Furthermore, it can provide healthy eating advice based on the user's meal history. This makes it easier for visually impaired individuals to maintain a healthy diet.

[0128] Wearable devices can also include a relaxation section that estimates the user's emotions and provides relaxation guidance based on those emotions. For example, if the user is feeling stressed, it can provide guidance on deep breathing or meditation. If the user is relaxed, it can also provide relaxation music or nature sounds. Furthermore, if the user is feeling anxious, it can provide reassuring messages. This makes it easier for visually impaired people to relax even when they are out and about.

[0129] Wearable devices can also be equipped with an exercise management unit to support the user's physical activity. This unit records the user's exercise volume and type, and analyzes the effects of the exercise. For example, if the user walks or runs, it calculates the distance and calories burned. Furthermore, if the user sets specific exercise goals, it can monitor their progress and report on their achievement. It can also provide advice on the user's form and posture while exercising. This makes it easier for visually impaired individuals to exercise effectively.

[0130] Wearable devices can also include a communication unit that estimates the user's emotions and adjusts the way it communicates based on those emotions. For example, if the user is feeling lonely, it can provide messages encouraging them to contact friends or family. If the user is relaxed, it can offer lighthearted topics of conversation. Furthermore, if the user is stressed, it can provide encouraging messages. This makes it easier for visually impaired people to communicate appropriately even when they are out and about.

[0131] Wearable devices can also be equipped with a sleep management unit to support the user's sleep. This unit records the user's sleep patterns and analyzes sleep quality. For example, it can provide music or guidance to help the user relax before going to sleep. It can also provide an alarm to encourage natural waking. Furthermore, it can offer advice to improve sleep quality based on the user's sleep data. This can make it easier for visually impaired individuals to achieve high-quality sleep.

[0132] Wearable devices can also include an exercise unit that estimates the user's emotions and adjusts the exercise content based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user wants to feel more energetic, it can suggest energetic exercises. Furthermore, if the user is relaxed, it can suggest light stretching or yoga. This makes it easier for visually impaired people to perform appropriate exercises even when they are outdoors.

[0133] Wearable devices can also be equipped with a learning support unit to assist users in their learning. This unit provides information related to what the user wants to learn, based on voice input. For example, if a user wants to learn about a specific topic, it can provide articles and audio materials on that topic. If a user wants to learn a language, it can also provide pronunciation practice and teach word meanings. Furthermore, it can record the user's learning progress and suggest what to learn next. This makes it easier for visually impaired individuals to learn effectively even when they are out and about.

[0134] Wearable devices can also feature a reminder section that estimates the user's emotions and adjusts the content of reminders based on those emotions. For example, if the user is stressed, the reminder content can be made concise. Conversely, if the user is relaxed, a more detailed reminder can be provided. Furthermore, if the user is in a hurry, important reminders can be prioritized. This makes it easier for visually impaired individuals to receive appropriate reminders even when they are out and about.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The acquisition unit determines the current location of a visually impaired person when they go out. For example, it acquires the current location using GPS, Wi-Fi location information, cell tower information, etc. Step 2: The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, it uses a generation AI to calculate the shortest distance, shortest time, and a route that takes traffic conditions into consideration. Step 3: The providing unit provides directions based on the route generated by the generating unit. For example, directions are provided using methods such as voice guidance, vibration guidance, and visual guidance. Step 4: The reception desk receives user input. For example, it accepts user input through methods 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, it generates a response using a generation AI in the form of voice response, text response, vibration response, etc. Step 6: The response provider unit provides the response generated by the response generator unit. For example, the response is provided by methods such as voice response, text response, or vibration response. Step 7: This reception desk accepts the title of the book you want to read. For example, it accepts the book title via voice input, text input, etc. Step 8: The text-to-speech service unit reads the content aloud based on the information received by the reception unit. For example, it may use speech synthesis technology to perform the reading.

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the smart device 14. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance using the control unit 46A of the smart device 14. The reception unit receives user input using the touch panel 38A or microphone 38B of the smart device 14. The response generation unit generates a response using the specific processing unit 290 of the data processing device 12. The response provision unit provides the response using the speaker 40B of the smart device 14. The main reception unit receives the book title using the microphone 38B of the smart device 14. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the smart device 14. The feedback unit provides feedback using the vibration motor of the smart device 14. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages ​​using the specific processing unit 290 of the data processing device 12. The provision unit acquires the latest information using 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 using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

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

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the smart glasses 214. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the smart glasses 214. The reception unit receives user input using the microphone 238 of the smart glasses 214. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides the response using the speaker 240 of the smart glasses 214. The main reception unit receives the book title using the microphone 238 of the smart glasses 214. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the smart glasses 214. The feedback unit provides feedback using the vibration motor of the smart glasses 214. The text-to-speech function reads aloud specific chapters or pages using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages ​​using the specific processing unit 290 of the data processing device 12. The information provision unit acquires the latest information using 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 using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

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

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the headset terminal 314. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the headset terminal 314. The reception unit receives user input using the microphone 238 of the headset terminal 314. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides a response using the speaker 240 of the headset terminal 314. The main reception unit receives the book title using the microphone 238 of the headset terminal 314. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the headset terminal 314. The feedback unit provides feedback using the vibration motor of the headset terminal 314. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages ​​using the specific processing unit 290 of the data processing device 12. The provision unit acquires the latest information using the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the headset terminal 314. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

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

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the robot 414. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the robot 414. The reception unit receives user input using the microphone 238 of the robot 414. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides a response using the speaker 240 of the robot 414. The main reception unit receives the book title using the microphone 238 of the robot 414. The read-aloud provision unit reads the contents aloud using the specific processing unit 290 of the data processing unit 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensors of the robot 414. The feedback unit provides feedback using the vibration motor of the robot 414. The reading function reads aloud specific chapters or pages using the specific processing unit 290 of the data processing unit 12. The multi-language support function provides responses and readings in multiple languages ​​using the specific processing unit 290 of the data processing unit 12. The providing unit acquires the latest information using the specific processing unit 290 of the data processing unit 12 and provides it using the speaker 240 of the robot 414. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing unit 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A unit that acquires the current location, A generation unit calculates a route to the destination based on the information acquired by the acquisition unit, A providing unit that provides directions based on the route generated by the generation unit, A reception area that receives user input, A response generation unit that generates a response based on the information received by the reception unit, A response providing unit that provides the response generated by the response generating unit, The book submission department accepts the titles of books you want to read, The system includes a reading and providing unit that reads out the content based on the information received by the aforementioned reception unit. A system characterized by the following features. (Note 2) It is equipped with a detection unit that detects and avoids obstacles. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback unit that provides vibration or tactile feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) Features include the ability to read aloud specific chapters or pages. The system described in Appendix 1, characterized by the features described herein. (Note 5) Features support for multiple languages The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a service that retrieves the latest information online and provides it to users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of location acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system analyzes the user's past movement history and selects the optimal method for obtaining their current location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring the current location, accuracy is improved by taking into account the user's movement speed and direction. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring the current location, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring the user's current location, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the root calculation algorithm based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When calculating a route, the system predicts the optimal route by referring to past travel data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When calculating a route, the system takes traffic conditions and weather information into consideration to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines route priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When calculating a route, the system takes the user's geographical location into consideration to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During route calculation, the system analyzes the user's social media activity and generates relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way directions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing directions, the system selects the most suitable guidance method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing directions, the timing of the directions is adjusted considering the user's current speed and direction of movement. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of directions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing directions, the system selects the optimal guidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing directions, the system analyzes the user's social media activity and provides relevant directions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input processing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is When receiving input, the system selects the most suitable processing method by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When receiving input, we improve the accuracy of the input process by taking into account the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of input requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is When receiving input, the system selects the most suitable reception method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant input information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The detection unit, The system estimates the user's emotions and adjusts the sensitivity of obstacle detection based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The detection unit, When an obstacle is detected, the system selects the optimal detection method by referring to the user's past movement history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The detection unit, The system estimates the user's emotions and determines the priority of obstacle detection based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The detection unit, When detecting an obstacle, the system selects the optimal detection method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned feedback unit is It estimates the user's emotions and adjusts the intensity of feedback based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, the system selects the most suitable feedback method by referring to the user's past feedback history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way the text is read aloud based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When reading aloud, the system selects the optimal reading method by referring to the user's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned supply unit is, It estimates the user's emotions and determines the reading priority based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When reading aloud, the system selects the optimal reading method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An emotion estimation unit that estimates the user's emotions, A unit that acquires the current location, A generation unit calculates a route to the destination based on the information acquired by the acquisition unit, A providing unit that provides directions based on the route generated by the generation unit, A reception area that receives user input, A response generation unit that generates a response based on the information received by the reception unit, A response providing unit that provides the response generated by the response generating unit, The book submission department accepts the titles of books you want to read, The system includes a reading and providing unit that reads out the content based on the information received by the aforementioned reception unit, The acquisition unit analyzes logs of past travel routes, duration of stay at each travel route, and current location acquisition methods used, which are recorded as the user's past travel history. Based on the analysis results and the characteristics of places that the user is likely to visit based on the current time and day of the week, the unit selects the optimal current location acquisition method from among several predefined methods, including GPS-based current location acquisition, Wi-Fi access point signal strength-based current location acquisition, and mobile phone base station signal-based current location acquisition, which is judged to provide the highest positioning accuracy in the current situation. If the user's emotion estimated by the emotion estimation unit is a pre-set first emotional state, the unit adjusts the frequency of current location acquisition to be higher than normal. If the estimated user's emotion is a pre-set second emotional state, the unit adjusts the frequency of current location acquisition to be lower than normal. A system characterized by the following features.

2. It is equipped with a detection unit that detects and avoids obstacles. The system according to feature 1.

3. It includes a feedback unit that provides vibration or tactile feedback. The system according to feature 1.

4. The aforementioned text-to-speech unit has the function of reading aloud the content from a specific chapter or page designated by the user. The system according to feature 1.

5. The text-to-speech unit has the function of reading the content aloud in a language selected by the user from among a plurality of pre-set languages, and the response unit has the function of providing the response in a language selected by the user from among the plurality of languages. The system according to feature 1.

6. It includes a service that retrieves the latest information online and provides it to users. The system according to feature 1.

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