System

Abstract

An object of a system according to an embodiment is to enable a visually impaired person to grasp a surrounding situation and safely go out.SOLUTION: A system includes a recognition part, a voice guide part, and an information acquisition part. The recognition unit recognizes a surrounding situation. The voice guidance unit issues an instruction by voice based on the information recognized by the recognition unit. The information acquisition unit acquires information using a camera or a sensor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for visually impaired people to understand their surroundings and go out safely.

[0005] The system according to the embodiment aims to enable visually impaired people to understand their surroundings and go out safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a voice guidance unit, and an information acquisition unit. The recognition unit recognizes the surrounding situation. The voice guidance unit issues voice instructions based on the information recognized by the recognition unit. The information acquisition unit acquires information using a camera or a sensor. [Effects of the Invention]

[0007] The system according to the embodiment can enable visually impaired people to understand their surroundings and go out safely. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides audio notification of surrounding conditions to visually impaired people. This system uses AI to recognize surrounding conditions and provide audio instructions based on the recognized information, enabling visually impaired people to go out safely. For example, the system recognizes the traffic of cars, bicycles, and people at crosswalks, train stations, inside stores, etc., and speaks to visually impaired people via audio to instruct them to avoid danger. This allows visually impaired people to go out safely and live with peace of mind. This system provides visually impaired people with opportunities to go out safely and live with peace of mind. For example, when visually impaired people use train stations, audio guidance of train arrival and departure information allows them to move smoothly. Furthermore, in stores, they can enjoy shopping with peace of mind by understanding the movements of others and the location of obstacles.

[0029] A visual assistance system according to an embodiment includes a recognition unit, a voice guidance unit, and an information acquisition unit. The recognition unit recognizes the surrounding situation. Examples of the surrounding situation include, but are not limited to, obstacles, traffic conditions, and weather. The recognition unit monitors the surrounding situation in real time using, for example, a camera or a sensor. For example, the recognition unit detects approaching cars and bicycles at crosswalks, acquires information about train arrivals and departures at stations, and grasps the movements of other people and the location of obstacles inside stores. The voice guidance unit issues voice instructions based on the information recognized by the recognition unit. For example, the voice guidance unit provides specific voice instructions such as, "A car is approaching ahead. Be careful," or "A bicycle is passing on the right." This allows visually impaired people to understand the surrounding situation and take appropriate action. The information acquisition unit acquires information using a camera or a sensor. For example, the information acquisition unit detects approaching cars and bicycles at crosswalks, acquires information about train arrivals and departures at stations, and grasps the movements of other people and the location of obstacles inside stores. This allows visually impaired people to recognize their surroundings and give voice instructions so they can go out safely.

[0030] The voice guidance unit can provide voice guidance in real time based on the walking speed and direction of the visually impaired person. The voice guidance unit provides voice guidance in real time, taking into account, for example, the walking speed and direction of the visually impaired person. For example, when a visually impaired person crosses a crosswalk, the voice guidance unit provides instructions such as "It is safe to cross now" or "The traffic light is changing. Please cross quickly." The voice guidance unit can also provide appropriate voice guidance according to the walking speed and direction of the visually impaired person. For example, when a visually impaired person uses a station, the voice guidance unit provides information on train arrivals and departures in real time. In this way, appropriate voice guidance according to the walking speed and direction of the visually impaired person is provided.

[0031] The information acquisition unit can detect approaching cars and bicycles at crosswalks. The information acquisition unit detects approaching cars and bicycles at crosswalks, for example. For example, the information acquisition unit detects approaching cars and bicycles in real time using a camera or a sensor. Furthermore, when the information acquisition unit detects approaching cars or bicycles, it can provide information to the audio guidance unit and issue appropriate instructions to visually impaired people. In this way, it is possible to detect approaching cars and bicycles at crosswalks and provide safety information to visually impaired people.

[0032] The information acquisition unit can acquire information about train arrivals and departures at stations. The information acquisition unit acquires, for example, information about train arrivals and departures at stations. For example, the information acquisition unit acquires information about train arrivals and departures in real time using a camera or a sensor. Furthermore, when the information acquisition unit acquires information about train arrivals and departures, it can provide the information to the voice guidance unit and provide appropriate guidance to visually impaired people. In this way, it is possible to acquire information about train arrivals and departures at stations and provide appropriate guidance to visually impaired people.

[0033] The information acquisition unit can grasp the movements of other people and the positions of obstacles in the store. The information acquisition unit grasps, for example, the movements of other people and the positions of obstacles in the store. For example, the information acquisition unit grasps the movements of other people and the positions of obstacles in real time using a camera or a sensor. Furthermore, when the information acquisition unit grasps the movements of other people and the positions of obstacles, it can provide the information to the voice guidance unit and give appropriate instructions to the visually impaired person. In this way, it is possible to grasp the movements of other people and the positions of obstacles in the store and provide safety information to the visually impaired person.

[0034] The voice guidance unit can provide specific instructions by voice, such as, "A car is approaching ahead. Be careful." or "A bicycle is passing on the right." The voice guidance unit can provide specific instructions by voice, such as, "A car is approaching ahead. Be careful." or "A bicycle is passing on the right." For example, the voice guidance unit can provide specific instructions by voice to a visually impaired person according to the surrounding situation. The voice guidance unit can also provide appropriate voice guidance according to the walking speed and direction of the visually impaired person. This allows specific instructions to be provided by voice to a visually impaired person, enabling them to avoid danger.

[0035] The recognition unit can analyze surrounding audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit can analyze surrounding audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit can analyze the engine sounds of surrounding cars to recognize approaching cars. The recognition unit can also analyze the sounds of conversations between surrounding people to recognize human movement. The recognition unit can also analyze the bell sounds of surrounding bicycles to recognize approaching bicycles. In this way, recognition accuracy is improved by integrating surrounding audio information and visual information. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can improve recognition accuracy by using an AI model that uses surrounding audio information and visual information as input and improves recognition accuracy.

[0036] The recognition unit can adjust the recognition algorithm depending on the weather and the time of day. The recognition unit adjusts the recognition algorithm depending on, for example, the weather and the time of day. For example, the recognition unit performs recognition by increasing the accuracy of visual information when it is raining. The recognition unit can also perform recognition by increasing the accuracy of audio information at night. The recognition unit can also perform recognition by balancing visual information and audio information when it is sunny. This enables optimal recognition depending on the weather and the time of day. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can adjust the recognition algorithm using an AI model that uses information on the weather and the time of day as input and adjusts the recognition algorithm.

[0037] The recognition unit can improve recognition accuracy by referring to past recognition data. The recognition unit improves recognition accuracy by referring to, for example, past recognition data. For example, the recognition unit improves current recognition accuracy based on approaching car data recognized in the past. The recognition unit can also improve current recognition accuracy based on movement data of people recognized in the past. The recognition unit can also improve current recognition accuracy based on approaching bicycle data recognized in the past. In this way, current recognition accuracy is improved by referring to past recognition data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can improve recognition accuracy by using an AI model that uses past recognition data as input and improves recognition accuracy.

[0038] The recognition unit can narrow down the recognition targets based on the user's location information. The recognition unit narrows down the recognition targets based on, for example, the user's location information. For example, when the user is at a crosswalk, the recognition unit can prioritize recognizing approaching cars and bicycles. Furthermore, when the user is at a station, the recognition unit can prioritize recognizing the arrival and departure of trains. Furthermore, when the user is inside a store, the recognition unit can prioritize recognizing the movements of other people and the positions of obstacles. This makes it possible to narrow down the recognition targets based on the user's location information and provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can narrow down the recognition targets using an AI model that uses the user's location information as input and narrows down the recognition targets.

[0039] The recognition unit can customize the recognition target by referring to the user's past behavioral history. The recognition unit customizes the recognition target by referring to the user's past behavioral history, for example. For example, the recognition unit customizes the recognition target based on routes the user has frequently used in the past. The recognition unit can also customize the recognition target based on dangerous situations the user has encountered in the past. The recognition unit can also recognize situations that require caution during specific time periods from the user's past behavioral history. This allows the recognition target to be customized based on the user's past behavioral history, making it possible to provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can customize the recognition target using an AI model that uses the user's past behavioral history as input and customizes the recognition target.

[0040] The recognition unit can adjust the recognition algorithm based on the user's device information. For example, the recognition unit adjusts the recognition algorithm based on the user's device information. For example, if the user is using a smartphone, the recognition unit adjusts the recognition algorithm according to the camera resolution. Furthermore, if the user is using a smartwatch, the recognition unit can adjust the recognition algorithm according to the sensor accuracy. Furthermore, if the user is using a tablet, the recognition unit can adjust the recognition algorithm according to the screen size. This allows the recognition algorithm to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can adjust the recognition algorithm using an AI model that uses the user's device information as input and adjusts the recognition algorithm.

[0041] The voice guidance unit can optimize the guidance content by referring to the user's past responses. The voice guidance unit optimizes the guidance content by referring to the user's past responses, for example. For example, the voice guidance unit provides optimal guidance content based on guidance methods that the user has previously preferred. The voice guidance unit can also optimize the current guidance content based on guidance content that the user has previously responded well to. The voice guidance unit can also provide optimal guidance content for a specific situation based on the user's past response history. This makes it possible to optimize the guidance content based on the user's past responses and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can optimize the guidance content using an AI model that uses the user's past responses as input and optimizes the guidance content.

[0042] The voice guidance unit can adjust the volume of the voice based on the ambient noise level. The voice guidance unit adjusts the volume of the voice based on, for example, the ambient noise level. For example, when the ambient environment is noisy, the voice guidance unit increases the volume of the voice guidance. Furthermore, when the ambient environment is quiet, the voice guidance unit can decrease the volume of the voice guidance. Furthermore, the voice guidance unit can adjust the volume of the voice guidance in real time according to the ambient noise level. This makes it possible to adjust the volume of the voice according to the ambient noise level and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can adjust the volume of the voice using an AI model that uses the ambient noise level as input and adjusts the volume of the voice.

[0043] The voice guidance unit can provide multilingual guidance according to the user's language setting. The voice guidance unit can provide multilingual guidance according to the user's language setting, for example. For example, the voice guidance unit can automatically set the language of the voice guidance based on the language setting of the user's device. The voice guidance unit can also provide a language switching function when the user uses multiple languages. The voice guidance unit can also provide voice guidance in a specific language when the user selects that language. This provides multilingual guidance according to the user's language setting. Some or all of the above-described processing in the voice guidance unit can be performed using, for example, AI or without AI. For example, the voice guidance unit can provide multilingual guidance using an AI model that uses the user's language setting as input and provides multilingual guidance.

[0044] The voice guidance unit can customize the guidance content based on the user's location information. The voice guidance unit customizes the guidance content based on, for example, the user's location information. For example, when the user is at a crosswalk, the voice guidance unit can prioritize guidance about approaching cars and bicycles. Furthermore, when the user is at a station, the voice guidance unit can prioritize guidance about train arrivals and departures. Furthermore, when the user is inside a store, the voice guidance unit can prioritize guidance about the movements of other people and the locations of obstacles. This allows the guidance content to be customized based on the user's location information, making it possible to provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice guidance unit can customize the guidance content using an AI model that uses the user's location information as input and customizes the guidance content.

[0045] The voice guidance unit can adjust the guidance method based on the user's device information. The voice guidance unit adjusts the guidance method based on the user's device information, for example. For example, if the user is using a smartphone, the voice guidance unit adjusts the volume and tone of the voice guidance. Furthermore, if the user is using a smartwatch, the voice guidance unit can provide concise and highly visible voice guidance. Furthermore, if the user is using a tablet, the voice guidance unit can adjust the content of the voice guidance according to the screen size. This allows the guidance method to be adjusted based on the user's device information and more appropriate guidance to be provided. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can adjust the guidance method using an AI model that uses the user's device information as input and adjusts the guidance method.

[0046] The voice guidance unit can improve the guidance content by reflecting the user's past feedback. For example, the voice guidance unit improves the guidance content by reflecting the user's past feedback. For example, the voice guidance unit improves the guidance content based on feedback provided by the user in the past. The voice guidance unit can also provide optimal guidance content for a specific situation based on the user's past feedback. The voice guidance unit can also analyze the user's feedback history and optimize the guidance content. This makes it possible to improve the guidance content based on the user's past feedback and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can improve the guidance content using an AI model that uses the user's past feedback as input and improves the guidance content.

[0047] The information acquisition unit can improve the acquisition accuracy by analyzing surrounding environmental information. The information acquisition unit can improve the acquisition accuracy by analyzing, for example, the surrounding environmental information. For example, the information acquisition unit can analyze the intensity of surrounding light to improve the acquisition accuracy of visual information. The information acquisition unit can also analyze the intensity of surrounding sound to improve the acquisition accuracy of audio information. The information acquisition unit can also analyze the ambient temperature and humidity to improve the acquisition accuracy of environmental information. In this way, the analysis of the surrounding environmental information improves the accuracy of information acquisition. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can improve the acquisition accuracy by using an AI model that inputs surrounding environmental information and improves acquisition accuracy.

[0048] The information acquisition unit can customize the acquisition target by referring to the user's past behavioral history. The information acquisition unit customizes the acquisition target by referring to the user's past behavioral history, for example. For example, the information acquisition unit customizes the acquisition target based on routes the user has frequently used in the past. The information acquisition unit can also customize the acquisition target based on dangerous situations the user has encountered in the past. The information acquisition unit can also acquire information on situations requiring caution during specific time periods from the user's past behavioral history. This allows the acquisition target to be customized based on the user's past behavioral history, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can customize the acquisition target using an AI model that uses the user's past behavioral history as input and customizes the acquisition target.

[0049] The information acquisition unit can adjust the acquisition method taking into account the user's device information. For example, the information acquisition unit adjusts the acquisition method taking into account the user's device information. For example, if the user is using a smartphone, the information acquisition unit adjusts the information acquisition method according to the camera resolution. Furthermore, if the user is using a smartwatch, the information acquisition unit can adjust the information acquisition method according to the sensor accuracy. Furthermore, if the user is using a tablet, the information acquisition unit can adjust the information acquisition method according to the screen size. This allows the acquisition method to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can adjust the acquisition method using an AI model that uses the user's device information as input and adjusts the acquisition method.

[0050] The information acquisition unit can narrow down the acquisition targets based on the user's location information. The information acquisition unit narrows down the acquisition targets based on, for example, the user's location information. For example, when the user is at a crosswalk, the information acquisition unit prioritizes acquiring information about approaching cars and bicycles. Furthermore, when the user is at a station, the information acquisition unit can prioritize acquiring information about train arrivals and departures. Furthermore, when the user is inside a store, the information acquisition unit can prioritize acquiring information about the movements of other people and the location of obstacles. This makes it possible to narrow down the acquisition targets based on the user's location information and provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can narrow down the acquisition targets using an AI model that uses the user's location information as input and narrows down the acquisition targets.

[0051] The information acquisition unit can customize the acquisition method by reflecting the user's past feedback. The information acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the information acquisition unit improves the information acquisition method based on feedback provided by the user in the past. The information acquisition unit can also provide an optimal information acquisition method for a specific situation based on the user's past feedback. The information acquisition unit can also analyze the user's feedback history and optimize the information acquisition method. This allows the acquisition method to be customized based on the user's past feedback and provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can customize the acquisition method using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0052] The information acquisition unit can adjust the acquisition method based on the user's device information. For example, the information acquisition unit adjusts the acquisition method based on the user's device information. For example, if the user is using a smartphone, the information acquisition unit adjusts the information acquisition method according to the camera resolution. Furthermore, if the user is using a smartwatch, the information acquisition unit can adjust the information acquisition method according to the sensor accuracy. Furthermore, if the user is using a tablet, the information acquisition unit can adjust the information acquisition method according to the screen size. This allows the acquisition method to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can adjust the acquisition method using an AI model that uses the user's device information as input and adjusts the acquisition method.

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

[0054] The visual assistance system may further include a vibration feedback unit. The vibration feedback unit provides a warning through vibration when the visually impaired person encounters a specific situation. For example, if a car is approaching at a crosswalk, the vibration feedback unit vibrates the wrist-worn device to warn the visually impaired person. The vibration feedback unit can also vibrate when a train is arriving at a station. Furthermore, the vibration feedback unit can provide a warning through vibration when an obstacle is approaching inside a store. This allows the visually impaired person to receive not only audio guidance but also vibration feedback, allowing them to act more safely.

[0055] The information acquisition unit may further include a temperature sensor. The temperature sensor monitors the ambient temperature in real time and provides appropriate information to visually impaired persons. For example, the temperature sensor can detect sudden changes in temperature when they are out and guide them to appropriate clothing. The temperature sensor can also detect temperature changes inside stations or stores and provide information to provide a comfortable environment for visually impaired persons. Furthermore, the temperature sensor can encourage visually impaired persons to take appropriate actions to reduce the risk of heatstroke and hypothermia. This allows visually impaired persons to go out safely and comfortably.

[0056] The information acquisition unit can further include a humidity sensor. The humidity sensor monitors the ambient humidity in real time and provides appropriate information to visually impaired people. For example, the humidity sensor can detect high or low humidity when going out and guide the visually impaired people to appropriate measures. The humidity sensor can also detect humidity changes inside stations or stores and provide information to provide a comfortable environment for visually impaired people. Furthermore, the humidity sensor can encourage visually impaired people to take appropriate actions to reduce the risk of mold and dust mites. This allows visually impaired people to go out safely and comfortably.

[0057] The information acquisition unit can further include an air quality sensor. The air quality sensor monitors the surrounding air quality in real time and provides appropriate information to visually impaired people. For example, the air quality sensor can detect air pollution and allergen concentrations when they are out and guide the visually impaired people to appropriate measures. The air quality sensor can also detect changes in air quality inside stations and stores and provide information to provide a comfortable environment for visually impaired people. Furthermore, the air quality sensor can encourage visually impaired people to take appropriate actions to reduce health risks. This allows visually impaired people to go out safely and comfortably.

[0058] The recognition unit may further include a gesture recognition function. The gesture recognition function recognizes hand and body movements of a visually impaired person and provides appropriate information. For example, the gesture recognition function can provide audio guidance about the surrounding situation when a visually impaired person raises their hand. The gesture recognition function can also provide specific information when a visually impaired person makes a specific gesture. Furthermore, the gesture recognition function can respond quickly when a visually impaired person makes a specific gesture in an emergency. This allows visually impaired people to receive information more intuitively through hand and body movements.

[0059] The recognition unit may further include a voice recognition function. The voice recognition function recognizes voice commands from a visually impaired person and provides appropriate information. For example, the voice recognition function can provide audible guidance of the surrounding situation when a visually impaired person says, "Tell me about my surroundings." The voice recognition function can also provide detailed information through a voice command when a visually impaired person arrives at a specific location. Furthermore, the voice recognition function can respond quickly when a visually impaired person issues a specific voice command in an emergency. This allows a visually impaired person to receive information more intuitively through voice commands.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The recognition unit recognizes the surrounding situation. Examples of the surrounding situation include, but are not limited to, obstacles, traffic conditions, and weather. The recognition unit monitors the surrounding situation in real time using, for example, a camera or a sensor. For example, the recognition unit detects approaching cars and bicycles at a crosswalk, obtains information on train arrivals and departures at a station, and grasps the movements of other people and the location of obstacles inside a store. Step 2: The voice guidance unit issues voice instructions based on the information recognized by the recognition unit. For example, the voice guidance unit provides specific voice instructions such as "A vehicle is approaching ahead. Be careful" or "A bicycle is passing on the right." This allows the visually impaired person to understand their surroundings and take appropriate action. Step 3: The information acquisition unit acquires information using a camera or sensor. For example, the information acquisition unit detects approaching cars or bicycles at a crosswalk, acquires information about train arrivals and departures at a station, and grasps the movements of other people and the location of obstacles in a store.

[0062] (Example 2) A system according to an embodiment of the present invention provides audio notification of surrounding conditions to visually impaired people. This system uses AI to recognize surrounding conditions and provide audio instructions based on the recognized information, enabling visually impaired people to go out safely. For example, the system recognizes the traffic of cars, bicycles, and people at crosswalks, train stations, inside stores, etc., and speaks to visually impaired people via audio to instruct them to avoid danger. This allows visually impaired people to go out safely and live with peace of mind. This system provides visually impaired people with opportunities to go out safely and live with peace of mind. For example, when visually impaired people use train stations, audio guidance of train arrival and departure information allows them to move smoothly. Furthermore, in stores, they can enjoy shopping with peace of mind by understanding the movements of others and the location of obstacles.

[0063] A visual assistance system according to an embodiment includes a recognition unit, a voice guidance unit, and an information acquisition unit. The recognition unit recognizes the surrounding situation. Examples of the surrounding situation include, but are not limited to, obstacles, traffic conditions, and weather. The recognition unit monitors the surrounding situation in real time using, for example, a camera or a sensor. For example, the recognition unit detects approaching cars and bicycles at crosswalks, acquires information about train arrivals and departures at stations, and grasps the movements of other people and the location of obstacles inside stores. The voice guidance unit issues voice instructions based on the information recognized by the recognition unit. For example, the voice guidance unit provides specific voice instructions such as, "A car is approaching ahead. Be careful," or "A bicycle is passing on the right." This allows visually impaired people to understand the surrounding situation and take appropriate action. The information acquisition unit acquires information using a camera or a sensor. For example, the information acquisition unit detects approaching cars and bicycles at crosswalks, acquires information about train arrivals and departures at stations, and grasps the movements of other people and the location of obstacles inside stores. This allows visually impaired people to recognize their surroundings and give voice instructions so they can go out safely.

[0064] The voice guidance unit can provide voice guidance in real time based on the walking speed and direction of the visually impaired person. The voice guidance unit provides voice guidance in real time, taking into account, for example, the walking speed and direction of the visually impaired person. For example, when a visually impaired person crosses a crosswalk, the voice guidance unit provides instructions such as "It is safe to cross now" or "The traffic light is changing. Please cross quickly." The voice guidance unit can also provide appropriate voice guidance according to the walking speed and direction of the visually impaired person. For example, when a visually impaired person uses a station, the voice guidance unit provides information on train arrivals and departures in real time. In this way, appropriate voice guidance according to the walking speed and direction of the visually impaired person is provided.

[0065] The information acquisition unit can detect approaching cars and bicycles at crosswalks. The information acquisition unit detects approaching cars and bicycles at crosswalks, for example. For example, the information acquisition unit detects approaching cars and bicycles in real time using a camera or a sensor. Furthermore, when the information acquisition unit detects approaching cars or bicycles, it can provide information to the audio guidance unit and issue appropriate instructions to visually impaired people. In this way, it is possible to detect approaching cars and bicycles at crosswalks and provide safety information to visually impaired people.

[0066] The information acquisition unit can acquire information about train arrivals and departures at stations. The information acquisition unit acquires, for example, information about train arrivals and departures at stations. For example, the information acquisition unit acquires information about train arrivals and departures in real time using a camera or a sensor. Furthermore, when the information acquisition unit acquires information about train arrivals and departures, it can provide the information to the voice guidance unit and provide appropriate guidance to visually impaired people. In this way, it is possible to acquire information about train arrivals and departures at stations and provide appropriate guidance to visually impaired people.

[0067] The information acquisition unit can grasp the movements of other people and the positions of obstacles in the store. The information acquisition unit grasps, for example, the movements of other people and the positions of obstacles in the store. For example, the information acquisition unit grasps the movements of other people and the positions of obstacles in real time using a camera or a sensor. Furthermore, when the information acquisition unit grasps the movements of other people and the positions of obstacles, it can provide the information to the voice guidance unit and give appropriate instructions to the visually impaired person. In this way, it is possible to grasp the movements of other people and the positions of obstacles in the store and provide safety information to the visually impaired person.

[0068] The voice guidance unit can provide specific instructions by voice, such as, "A car is approaching ahead. Be careful." or "A bicycle is passing on the right." The voice guidance unit can provide specific instructions by voice, such as, "A car is approaching ahead. Be careful." or "A bicycle is passing on the right." For example, the voice guidance unit can provide specific instructions by voice to a visually impaired person according to the surrounding situation. The voice guidance unit can also provide appropriate voice guidance according to the walking speed and direction of the visually impaired person. This allows specific instructions to be provided by voice to a visually impaired person, enabling them to avoid danger.

[0069] The recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. For example, the recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. For example, when the user is nervous, the recognition unit can increase the recognition accuracy to provide more detailed information. When the user is relaxed, the recognition unit can maintain the recognition accuracy at normal levels and provide the minimum necessary information. When the user is in a hurry, the recognition unit can increase the recognition accuracy to provide information quickly. This allows the recognition accuracy to be adjusted according to the user's emotion and more appropriate information to be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The recognition unit can analyze surrounding audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit can analyze surrounding audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit can analyze the engine sounds of surrounding cars to recognize approaching cars. The recognition unit can also analyze the sounds of conversations between surrounding people to recognize human movement. The recognition unit can also analyze the bell sounds of surrounding bicycles to recognize approaching bicycles. In this way, recognition accuracy is improved by integrating surrounding audio information and visual information. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can improve recognition accuracy by using an AI model that uses surrounding audio information and visual information as input and improves recognition accuracy.

[0071] The recognition unit can adjust the recognition algorithm depending on the weather and the time of day. The recognition unit adjusts the recognition algorithm depending on, for example, the weather and the time of day. For example, the recognition unit performs recognition by increasing the accuracy of visual information when it is raining. The recognition unit can also perform recognition by increasing the accuracy of audio information at night. The recognition unit can also perform recognition by balancing visual information and audio information when it is sunny. This enables optimal recognition depending on the weather and the time of day. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can adjust the recognition algorithm using an AI model that uses information on the weather and the time of day as input and adjusts the recognition algorithm.

[0072] The recognition unit can improve recognition accuracy by referring to past recognition data. The recognition unit improves recognition accuracy by referring to, for example, past recognition data. For example, the recognition unit improves current recognition accuracy based on approaching car data recognized in the past. The recognition unit can also improve current recognition accuracy based on movement data of people recognized in the past. The recognition unit can also improve current recognition accuracy based on approaching bicycle data recognized in the past. In this way, current recognition accuracy is improved by referring to past recognition data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can improve recognition accuracy by using an AI model that uses past recognition data as input and improves recognition accuracy.

[0073] The recognition unit can estimate the user's emotion and prioritize the recognition results based on the estimated user emotion. For example, the recognition unit can estimate the user's emotion and prioritize the recognition results based on the estimated user emotion. For example, if the user is nervous, the recognition unit can prioritize recognizing and notifying dangerous situations. Also, if the user is relaxed, the recognition unit can prioritize recognizing and notifying normal situations. Also, if the user is in a hurry, the recognition unit can prioritize recognizing and notifying situations requiring a quick response. This allows the recognition results to be prioritized according to the user's emotion and more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The recognition unit can narrow down the recognition targets based on the user's location information. The recognition unit narrows down the recognition targets based on, for example, the user's location information. For example, when the user is at a crosswalk, the recognition unit can prioritize recognizing approaching cars and bicycles. Furthermore, when the user is at a station, the recognition unit can prioritize recognizing the arrival and departure of trains. Furthermore, when the user is inside a store, the recognition unit can prioritize recognizing the movements of other people and the positions of obstacles. This makes it possible to narrow down the recognition targets based on the user's location information and provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can narrow down the recognition targets using an AI model that uses the user's location information as input and narrows down the recognition targets.

[0075] The recognition unit can customize the recognition target by referring to the user's past behavioral history. The recognition unit customizes the recognition target by referring to the user's past behavioral history, for example. For example, the recognition unit customizes the recognition target based on routes the user has frequently used in the past. The recognition unit can also customize the recognition target based on dangerous situations the user has encountered in the past. The recognition unit can also recognize situations that require caution during specific time periods from the user's past behavioral history. This allows the recognition target to be customized based on the user's past behavioral history, making it possible to provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can customize the recognition target using an AI model that uses the user's past behavioral history as input and customizes the recognition target.

[0076] The recognition unit can adjust the recognition algorithm based on the user's device information. For example, the recognition unit adjusts the recognition algorithm based on the user's device information. For example, if the user is using a smartphone, the recognition unit adjusts the recognition algorithm according to the camera resolution. Furthermore, if the user is using a smartwatch, the recognition unit can adjust the recognition algorithm according to the sensor accuracy. Furthermore, if the user is using a tablet, the recognition unit can adjust the recognition algorithm according to the screen size. This allows the recognition algorithm to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can adjust the recognition algorithm using an AI model that uses the user's device information as input and adjusts the recognition algorithm.

[0077] The voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on the estimated user's emotions. The voice guidance unit, for example, estimates the user's emotions and adjusts the tone and speed of the voice guidance based on the estimated user's emotions. For example, if the user is nervous, the voice guidance unit can provide guidance slowly in a calm tone. If the user is relaxed, the voice guidance unit can provide guidance at a normal speed in a bright tone. If the user is in a hurry, the voice guidance unit can provide guidance in a quick and concise tone. This allows the tone and speed of the voice guidance to be adjusted according to the user's emotions, thereby providing more appropriate guidance. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The voice guidance unit can optimize the guidance content by referring to the user's past responses. The voice guidance unit optimizes the guidance content by referring to the user's past responses, for example. For example, the voice guidance unit provides optimal guidance content based on guidance methods that the user has previously preferred. The voice guidance unit can also optimize the current guidance content based on guidance content that the user has previously responded well to. The voice guidance unit can also provide optimal guidance content for a specific situation based on the user's past response history. This makes it possible to optimize the guidance content based on the user's past responses and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can optimize the guidance content using an AI model that uses the user's past responses as input and optimizes the guidance content.

[0079] The voice guidance unit can adjust the volume of the voice based on the ambient noise level. The voice guidance unit adjusts the volume of the voice based on, for example, the ambient noise level. For example, when the ambient environment is noisy, the voice guidance unit increases the volume of the voice guidance. Furthermore, when the ambient environment is quiet, the voice guidance unit can decrease the volume of the voice guidance. Furthermore, the voice guidance unit can adjust the volume of the voice guidance in real time according to the ambient noise level. This makes it possible to adjust the volume of the voice according to the ambient noise level and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can adjust the volume of the voice using an AI model that uses the ambient noise level as input and adjusts the volume of the voice.

[0080] The voice guidance unit can provide multilingual guidance according to the user's language setting. The voice guidance unit can provide multilingual guidance according to the user's language setting, for example. For example, the voice guidance unit can automatically set the language of the voice guidance based on the language setting of the user's device. The voice guidance unit can also provide a language switching function when the user uses multiple languages. The voice guidance unit can also provide voice guidance in a specific language when the user selects that language. This provides multilingual guidance according to the user's language setting. Some or all of the above-described processing in the voice guidance unit can be performed using, for example, AI or without AI. For example, the voice guidance unit can provide multilingual guidance using an AI model that uses the user's language setting as input and provides multilingual guidance.

[0081] The voice guidance unit can estimate the user's emotions and determine the priority of guidance content based on the estimated user emotions. The voice guidance unit, for example, estimates the user's emotions and determines the priority of guidance content based on the estimated user emotions. For example, if the user is nervous, the voice guidance unit can prioritize guidance on dangerous situations. Furthermore, if the user is relaxed, the voice guidance unit can prioritize guidance on normal situations. Furthermore, if the user is in a hurry, the voice guidance unit can prioritize guidance on situations that require a quick response. This allows the priority of guidance content to be determined according to the user's emotions, making it possible to provide more appropriate guidance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The voice guidance unit can customize the guidance content based on the user's location information. The voice guidance unit customizes the guidance content based on, for example, the user's location information. For example, when the user is at a crosswalk, the voice guidance unit can prioritize guidance about approaching cars and bicycles. Furthermore, when the user is at a station, the voice guidance unit can prioritize guidance about train arrivals and departures. Furthermore, when the user is inside a store, the voice guidance unit can prioritize guidance about the movements of other people and the locations of obstacles. This allows the guidance content to be customized based on the user's location information, making it possible to provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice guidance unit can customize the guidance content using an AI model that uses the user's location information as input and customizes the guidance content.

[0083] The voice guidance unit can adjust the guidance method based on the user's device information. The voice guidance unit adjusts the guidance method based on the user's device information, for example. For example, if the user is using a smartphone, the voice guidance unit adjusts the volume and tone of the voice guidance. Furthermore, if the user is using a smartwatch, the voice guidance unit can provide concise and highly visible voice guidance. Furthermore, if the user is using a tablet, the voice guidance unit can adjust the content of the voice guidance according to the screen size. This allows the guidance method to be adjusted based on the user's device information and more appropriate guidance to be provided. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can adjust the guidance method using an AI model that uses the user's device information as input and adjusts the guidance method.

[0084] The voice guidance unit can improve the guidance content by reflecting the user's past feedback. For example, the voice guidance unit improves the guidance content by reflecting the user's past feedback. For example, the voice guidance unit improves the guidance content based on feedback provided by the user in the past. The voice guidance unit can also provide optimal guidance content for a specific situation based on the user's past feedback. The voice guidance unit can also analyze the user's feedback history and optimize the guidance content. This makes it possible to improve the guidance content based on the user's past feedback and provide more appropriate guidance. Some or all of the above-described processing in the voice guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice guidance unit can improve the guidance content using an AI model that uses the user's past feedback as input and improves the guidance content.

[0085] The information acquisition unit can estimate the user's emotion and adjust the timing of information acquisition based on the estimated user's emotion. The information acquisition unit, for example, estimates the user's emotion and adjusts the timing of information acquisition based on the estimated user's emotion. For example, when the user is nervous, the information acquisition unit frequently acquires and provides information. Furthermore, when the user is relaxed, the information acquisition unit can acquire and provide information at a normal timing. Furthermore, when the user is in a hurry, the information acquisition unit can quickly acquire and provide information. This allows the timing of information acquisition to be adjusted according to the user's emotion and more appropriate information to be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The information acquisition unit can improve the acquisition accuracy by analyzing surrounding environmental information. The information acquisition unit can improve the acquisition accuracy by analyzing, for example, the surrounding environmental information. For example, the information acquisition unit can analyze the intensity of surrounding light to improve the acquisition accuracy of visual information. The information acquisition unit can also analyze the intensity of surrounding sound to improve the acquisition accuracy of audio information. The information acquisition unit can also analyze the ambient temperature and humidity to improve the acquisition accuracy of environmental information. In this way, the analysis of the surrounding environmental information improves the accuracy of information acquisition. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can improve the acquisition accuracy by using an AI model that inputs surrounding environmental information and improves acquisition accuracy.

[0087] The information acquisition unit can customize the acquisition target by referring to the user's past behavioral history. The information acquisition unit customizes the acquisition target by referring to the user's past behavioral history, for example. For example, the information acquisition unit customizes the acquisition target based on routes the user has frequently used in the past. The information acquisition unit can also customize the acquisition target based on dangerous situations the user has encountered in the past. The information acquisition unit can also acquire information on situations requiring caution during specific time periods from the user's past behavioral history. This allows the acquisition target to be customized based on the user's past behavioral history, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can customize the acquisition target using an AI model that uses the user's past behavioral history as input and customizes the acquisition target.

[0088] The information acquisition unit can adjust the acquisition method taking into account the user's device information. For example, the information acquisition unit adjusts the acquisition method taking into account the user's device information. For example, if the user is using a smartphone, the information acquisition unit adjusts the information acquisition method according to the camera resolution. Furthermore, if the user is using a smartwatch, the information acquisition unit can adjust the information acquisition method according to the sensor accuracy. Furthermore, if the user is using a tablet, the information acquisition unit can adjust the information acquisition method according to the screen size. This allows the acquisition method to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can adjust the acquisition method using an AI model that uses the user's device information as input and adjusts the acquisition method.

[0089] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. The information acquisition unit, for example, estimates the user's emotions and determines the priority of information to be acquired based on the estimated user emotions. For example, if the user is nervous, the information acquisition unit can prioritize acquiring information about dangerous situations. Also, if the user is relaxed, the information acquisition unit can prioritize acquiring information about normal situations. Also, if the user is in a hurry, the information acquisition unit can prioritize acquiring information about situations requiring a quick response. This allows the priority of information to be acquired to be determined according to the user's emotions, and more appropriate information to be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The information acquisition unit can narrow down the acquisition targets based on the user's location information. The information acquisition unit narrows down the acquisition targets based on, for example, the user's location information. For example, when the user is at a crosswalk, the information acquisition unit prioritizes acquiring information about approaching cars and bicycles. Furthermore, when the user is at a station, the information acquisition unit can prioritize acquiring information about train arrivals and departures. Furthermore, when the user is inside a store, the information acquisition unit can prioritize acquiring information about the movements of other people and the location of obstacles. This makes it possible to narrow down the acquisition targets based on the user's location information and provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can narrow down the acquisition targets using an AI model that uses the user's location information as input and narrows down the acquisition targets.

[0091] The information acquisition unit can customize the acquisition method by reflecting the user's past feedback. The information acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the information acquisition unit improves the information acquisition method based on feedback provided by the user in the past. The information acquisition unit can also provide an optimal information acquisition method for a specific situation based on the user's past feedback. The information acquisition unit can also analyze the user's feedback history and optimize the information acquisition method. This allows the acquisition method to be customized based on the user's past feedback and provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can customize the acquisition method using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0092] The information acquisition unit can adjust the acquisition method based on the user's device information. For example, the information acquisition unit adjusts the acquisition method based on the user's device information. For example, if the user is using a smartphone, the information acquisition unit adjusts the information acquisition method according to the camera resolution. Furthermore, if the user is using a smartwatch, the information acquisition unit can adjust the information acquisition method according to the sensor accuracy. Furthermore, if the user is using a tablet, the information acquisition unit can adjust the information acquisition method according to the screen size. This allows the acquisition method to be adjusted based on the user's device information, making it possible to provide more appropriate information. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can adjust the acquisition method using an AI model that uses the user's device information as input and adjusts the acquisition method. === Hard Collateral 1-1 === Each of the above-described elements, including the recognition unit, voice guidance unit, and information acquisition unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the recognition unit can monitor the surrounding situation in real time using the camera 42 or sensors of the smart device 14. For example, the specific processing unit 290 of the data processing device 12 allows the recognition unit to analyze the surrounding situation and identify obstacles and traffic conditions. The voice guidance unit is implemented, for example, by the control unit 46A of the smart device 14 and issues voice instructions based on information from the recognition unit. For example, the specific processing unit 290 of the data processing device 12 allows the voice guidance unit to generate specific instructions and provide them to a visually impaired person through the speaker 40B of the smart device 14. The information acquisition unit acquires information using, for example, the camera 42 or sensors of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, voice guidance unit, and information acquisition unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit can monitor the surrounding situation in real time using the camera 42 or sensors of the smart glasses 214. For example, the specific processing unit 290 of the data processing device 12 allows the recognition unit to analyze the surrounding situation and identify obstacles and traffic conditions. The voice guidance unit is realized, for example, by the control unit 46A of the smart glasses 214 and issues voice instructions based on information from the recognition unit. For example, the specific processing unit 290 of the data processing device 12 causes the voice guidance unit to generate specific instructions and provide them to the visually impaired person through the speaker 240 of the smart glasses 214. The information acquisition unit, for example, acquires information using the camera 42 or sensors of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the recognition unit, voice guidance unit, and information acquisition unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit can monitor the surrounding situation in real time using the camera 42 or sensors of the headset terminal 314. For example, the specific processing unit 290 of the data processing device 12 allows the recognition unit to analyze the surrounding situation and identify obstacles and traffic conditions. The voice guidance unit is realized, for example, by the control unit 46A of the headset terminal 314 and issues voice instructions based on information from the recognition unit. For example, the specific processing unit 290 of the data processing device 12 causes the voice guidance unit to generate specific instructions and provide them to a visually impaired person through the speaker 240 of the headset terminal 314. The information acquisition unit acquires information, for example, using the camera 42 or sensors of the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the recognition unit, voice guidance unit, and information acquisition unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit can monitor the surrounding situation in real time using the camera 42 or sensors of the robot 414. For example, the specific processing unit 290 of the data processing device 12 allows the recognition unit to analyze the surrounding situation and identify obstacles and traffic conditions. The voice guidance unit is realized, for example, by the control unit 46A of the robot 414 and issues voice instructions based on information from the recognition unit. For example, the specific processing unit 290 of the data processing device 12 allows the voice guidance unit to generate specific instructions and provide them to visually impaired persons through the speaker 240 of the robot 414. The information acquisition unit, for example, acquires information using the camera 42 or sensors of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The visual assistance system may further include a vibration feedback unit. The vibration feedback unit provides a warning through vibration when the visually impaired person encounters a specific situation. For example, if a car is approaching at a crosswalk, the vibration feedback unit vibrates the wrist-worn device to warn the visually impaired person. The vibration feedback unit can also vibrate when a train is arriving at a station. Furthermore, the vibration feedback unit can provide a warning through vibration when an obstacle is approaching inside a store. This allows the visually impaired person to receive not only audio guidance but also vibration feedback, allowing them to act more safely.

[0095] The voice guidance unit can estimate the user's emotions and adjust the content of the voice guidance based on the estimated emotions. For example, if the user is nervous, the voice guidance unit can provide more detailed and reassuring guidance. If the user is relaxed, the voice guidance unit can provide concise and minimal guidance. Furthermore, if the user is in a hurry, the voice guidance unit can provide quick and efficient guidance. This allows optimal voice guidance to be provided according to the user's emotions, improving the sense of security and convenience for visually impaired people.

[0096] The information acquisition unit may further include a temperature sensor. The temperature sensor monitors the ambient temperature in real time and provides appropriate information to visually impaired persons. For example, the temperature sensor can detect sudden changes in temperature when they are out and guide them to appropriate clothing. The temperature sensor can also detect temperature changes inside stations or stores and provide information to provide a comfortable environment for visually impaired persons. Furthermore, the temperature sensor can encourage visually impaired persons to take appropriate actions to reduce the risk of heatstroke and hypothermia. This allows visually impaired persons to go out safely and comfortably.

[0097] The information acquisition unit can further include a humidity sensor. The humidity sensor monitors the ambient humidity in real time and provides appropriate information to visually impaired people. For example, the humidity sensor can detect high or low humidity when going out and guide the visually impaired people to appropriate measures. The humidity sensor can also detect humidity changes inside stations or stores and provide information to provide a comfortable environment for visually impaired people. Furthermore, the humidity sensor can encourage visually impaired people to take appropriate actions to reduce the risk of mold and dust mites. This allows visually impaired people to go out safely and comfortably.

[0098] The information acquisition unit can further include an air quality sensor. The air quality sensor monitors the surrounding air quality in real time and provides appropriate information to visually impaired people. For example, the air quality sensor can detect air pollution and allergen concentrations when they are out and guide the visually impaired people to appropriate measures. The air quality sensor can also detect changes in air quality inside stations and stores and provide information to provide a comfortable environment for visually impaired people. Furthermore, the air quality sensor can encourage visually impaired people to take appropriate actions to reduce health risks. This allows visually impaired people to go out safely and comfortably.

[0099] The voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on the estimated emotions. For example, if the user is nervous, the voice guidance unit can provide guidance slowly in a calm tone. If the user is relaxed, the voice guidance unit can provide guidance at a normal speed in a bright tone. Furthermore, if the user is in a hurry, the voice guidance unit can provide guidance in a quick and concise tone. This allows the tone and speed of the voice guidance to be adjusted according to the user's emotions, making it possible to provide more appropriate guidance.

[0100] The recognition unit may further include a facial recognition function. The facial recognition function recognizes the faces of visually impaired people and provides individually customized information. For example, the facial recognition function can provide individualized guidance when a visually impaired person arrives at a specific location. The facial recognition function can also analyze the facial expressions of a visually impaired person, infer their emotions, and provide appropriate information. Furthermore, the facial recognition function can provide information about a specific person when the visually impaired person meets that person. This allows the visually impaired person to receive more personalized information and act with peace of mind.

[0101] The recognition unit may further include a gesture recognition function. The gesture recognition function recognizes hand and body movements of a visually impaired person and provides appropriate information. For example, the gesture recognition function can provide audio guidance about the surrounding situation when a visually impaired person raises their hand. The gesture recognition function can also provide specific information when a visually impaired person makes a specific gesture. Furthermore, the gesture recognition function can respond quickly when a visually impaired person makes a specific gesture in an emergency. This allows visually impaired people to receive information more intuitively through hand and body movements.

[0102] The recognition unit may further include a voice recognition function. The voice recognition function recognizes voice commands from a visually impaired person and provides appropriate information. For example, the voice recognition function can provide audible guidance of the surrounding situation when a visually impaired person says, "Tell me about my surroundings." The voice recognition function can also provide detailed information through a voice command when a visually impaired person arrives at a specific location. Furthermore, the voice recognition function can respond quickly when a visually impaired person issues a specific voice command in an emergency. This allows a visually impaired person to receive information more intuitively through voice commands.

[0103] The recognition unit can further estimate the user's emotions and determine the priority of the recognition results based on the estimated emotions. For example, if the user is nervous, it can prioritize recognizing and notifying dangerous situations. Also, if the user is relaxed, it can prioritize recognizing and notifying normal situations. Furthermore, if the user is in a hurry, it can prioritize recognizing and notifying situations that require a quick response. In this way, it is possible to determine the priority of the recognition results according to the user's emotions and provide more appropriate information.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The recognition unit recognizes the surrounding situation. Examples of the surrounding situation include, but are not limited to, obstacles, traffic conditions, and weather. The recognition unit monitors the surrounding situation in real time using, for example, a camera or a sensor. For example, the recognition unit detects approaching cars and bicycles at a crosswalk, obtains information on train arrivals and departures at a station, and grasps the movements of other people and the location of obstacles inside a store. Step 2: The voice guidance unit issues voice instructions based on the information recognized by the recognition unit. For example, the voice guidance unit provides specific voice instructions such as "A vehicle is approaching ahead. Be careful" or "A bicycle is passing on the right." This allows the visually impaired person to understand their surroundings and take appropriate action. Step 3: The information acquisition unit acquires information using a camera or sensor. For example, the information acquisition unit detects approaching cars or bicycles at a crosswalk, acquires information about train arrivals and departures at a station, and grasps the movements of other people and the location of obstacles in a store.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit that recognizes the surrounding situation; a voice guidance unit that issues voice instructions based on the information recognized by the recognition unit; an information acquisition unit that acquires information using a camera or a sensor; A system characterized by:

2. The voice guidance unit Provides real-time voice guidance based on the walking speed and direction of visually impaired people 2. The system of claim 1.

3. The information acquisition unit Detecting approaching cars and bicycles at crosswalks 2. The system of claim 1.

4. The information acquisition unit Get train arrival and departure information at stations 2. The system of claim 1.

5. The information acquisition unit Understand the movements of other people and the location of obstacles in the store 2. The system of claim 1.

6. The recognition unit Estimate the user's emotion and adjust the recognition accuracy based on the estimated user emotion.

2. The system of claim 1.

7. The recognition unit Analyzes surrounding audio information and integrates it with visual information to improve recognition accuracy 2. The system of claim 1.

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