System

Abstract

An object of a system according to an embodiment is to enable a visually / hearing-impaired person to safely reach a destination using public transportation.SOLUTION: A system includes a availability status acquiring part, a braille block information acquiring part, a map generating part, and a voice guiding part. The availability status acquiring unit acquires availability status of each public transportation facility. The braille block information acquisition unit acquires braille block information of each local government body. The map generating unit generates a map based on the information acquired by the availability status acquiring unit and the braille block information acquiring unit. The voice guidance unit performs voice guidance based on the map generated by the map generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, visually and hearing impaired people have had difficulty reaching their destinations when using public transportation due to a lack of information on tactile paving and audio guidance.

[0005] The system according to the embodiment aims to enable visually and hearing impaired people to reach their destinations safely using public transportation. [Means for solving the problem]

[0006] The system according to the embodiment includes an operation status acquisition unit, a tactile paving block information acquisition unit, a map generation unit, and a voice guidance unit. The operation status acquisition unit acquires the operation status of each public transportation facility. The tactile paving block information acquisition unit acquires tactile paving block information from each local government. The map generation unit generates a map based on the information acquired by the operation status acquisition unit and the tactile paving block information acquisition unit. The voice guidance unit provides voice guidance based on the map generated by the map generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable visually and hearing impaired people to reach their destination safely using public transportation. [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) The braille block map generation app according to an embodiment of the present invention is a system that supports visually and hearing impaired people to reach their destinations safely and efficiently using public transportation. As a result, the braille block map generation app can support visually and hearing impaired people to reach their destinations safely and efficiently using public transportation.

[0029] A braille block map generation app according to an embodiment includes an operation status acquisition unit, a braille block information acquisition unit, a map generation unit, and an audio guidance unit. The operation status acquisition unit acquires the operation status of each public transportation facility. For example, the operation status acquisition unit acquires train delay information in real time. The operation status acquisition unit can also acquire bus operation status. The operation status acquisition unit can also acquire subway operation status. For example, the operation status acquisition unit acquires transportation facility operation information via an API and updates it in real time. The braille block information acquisition unit acquires braille block information from each local government. For example, the braille block information acquisition unit acquires the installation locations of braille blocks. The braille block information acquisition unit can also acquire status information of the braille blocks. The braille block information acquisition unit can also acquire updated information about the braille blocks. For example, the braille block information acquisition unit acquires braille block information from a local government database and reflects the latest information. The map generation unit generates a map based on the information acquired by the operation status acquisition unit and the braille block information acquisition unit. For example, the map generation unit uses a generation AI to generate a map that reflects the latest train operation status and braille block information. The map generation unit can also suggest optimal routes based on past data. The map generation unit can also generate a detailed map of a station premises. For example, the map generation unit uses a generation AI to generate a detailed map that includes location information of elevators and escalators within a station premises. The voice guidance unit provides voice guidance based on the map generated by the map generation unit. For example, the voice guidance unit uses a generation AI to generate voice guidance for visually and hearing impaired persons. The voice guidance unit can also provide real-time voice guidance. The voice guidance unit can also provide voice guidance based on the user's emotional state. For example, the voice guidance unit uses a generation AI to analyze the user's emotional state and provide voice guidance that gives the user a sense of security. As a result, the braille block map generation app according to the embodiment can support visually and hearing impaired persons in safely and efficiently reaching their destinations using public transportation. For example, the braille block map generation app provides route guidance based on the latest train operation status and braille block information.By utilizing past data, optimal route suggestions can be made, allowing passengers to avoid congestion and move smoothly. Detailed maps and voice guidance within the station allow passengers to move around without getting lost even in complex station premises.

[0030] The operation status acquisition unit simultaneously collects weather and disaster information and can suggest safe routes to the user. For example, when the generation AI collects operation status and Braille block information, the operation status acquisition unit acquires weather data in real time and suggests routes according to changes in the weather. For example, indoor routes are given priority in the event of heavy rain or strong winds. The operation status acquisition unit also collects disaster information and generates routes that respond to emergencies such as earthquakes and floods. For example, routes that include evacuation routes are suggested in the event of an earthquake. The operation status acquisition unit also generates routes that the user can travel safely based on weather and disaster information and updates them in real time. For example, routes with a low risk of flooding are suggested in the event of heavy rain. This makes it possible to provide routes that the user can travel safely.

[0031] The map generation unit can analyze the user's movement history and provide an individually optimized route. For example, the map generation unit uses a generation AI to analyze the user's past movement history and propose an individually optimized route. For example, the map generation unit generates a route taking into account routes used in the past and preferred means of transportation. The map generation unit also proposes a route to avoid congestion based on the user's movement history. For example, it predicts busy times from past data and provides guidance on a route to travel during less crowded times. The map generation unit also analyzes the user's movement history and proposes a route that includes facilities and services that the user prefers. For example, it generates a route that passes through cafes and restaurants that the user has visited in the past. This makes it possible to provide the user with an individually optimized route.

[0032] The map generation unit can also provide route guidance for elderly people and children other than the visually or hearing impaired. For example, the map generation unit provides route guidance for elderly people based on information collected by the generation AI. For example, the map generation unit suggests routes that prioritize the use of elevators and escalators. In addition, the map generation unit generates safe routes to provide route guidance for children. For example, the map generation unit provides guidance on routes that pass through roads with little traffic or parks. In addition, in order to provide route guidance for elderly people and children, the map generation unit collects barrier-free information and provides it to the user. For example, the map generation unit suggests routes with few steps or routes with handrails. This makes it possible to provide route guidance for elderly people and children.

[0033] The map generation unit can also provide access information to tourist spots and event venues. For example, the generation AI collects information on tourist spots and provides access information for the visually and hearing impaired. For example, the map generation unit proposes a route that includes information on braille blocks and audio guidance within the tourist spot. In addition, to provide access information to event venues, the generation AI collects information on event dates and generates an optimal route. For example, the map generation unit provides route guidance that takes into account the congestion status of the event venue. In addition, to provide access information to tourist spots and event venues, the generation AI collects information on the operation status of public transportation in real time and provides it to the user. For example, the map generation unit proposes a route based on the operation status of trains and buses. This makes it possible to provide access information to tourist spots and event venues.

[0034] The map generation unit analyzes past data, learns the user's movement patterns, and can predict future movement. For example, the map generation unit uses a generation AI to analyze past movement data and learn the user's movement patterns. For example, it predicts routes to be used on specific days of the week or time periods and suggests future movement. The map generation unit also predicts future movement based on the user's past movement history. For example, it predicts the routes and time periods that the user frequently uses from past data and suggests the optimal route. The map generation unit also predicts future movement by using a generation AI to analyze past data and learn the user's movement patterns. For example, it predicts movement patterns according to changes in seasons and weather and suggests routes. This makes it possible to predict the user's future movement and provide the optimal route.

[0035] The map generation unit can propose personalized routes that take into account the user's preferences and habits based on past data. For example, the map generation unit uses a generation AI to analyze past data and learn the user's preferences and habits. For example, for a user who likes a particular cafe or restaurant, the map generation unit proposes a route that passes through that location. The map generation unit also proposes personalized routes based on the user's past travel history. For example, the map generation unit generates routes that take into account routes used at specific times of day and preferred means of transportation. The map generation unit also uses a generation AI to analyze past data and propose routes that take into account the user's preferences and habits. For example, the map generation unit generates routes that pass through the user's favorite scenery or quiet places. This makes it possible to provide personalized routes based on the user's preferences and habits.

[0036] The map generation unit can compare the user's movement patterns in other cities and countries based on past data and propose the optimal route from a global perspective. For example, the generation AI in the map generation unit analyzes past data and compares the user's movement patterns in other cities and countries. For example, it proposes the optimal route based on movement data from different cities. The map generation unit also analyzes the movement patterns of other countries and proposes the optimal route from a global perspective. For example, it generates a route that takes different cultures and transportation systems into consideration. The map generation unit also compares the user's movement patterns in other cities and countries based on past data and proposes the optimal route. For example, it provides guidance on a route that takes into account the congestion levels and transportation means of different cities. This makes it possible to provide the optimal route from a global perspective.

[0037] The map generation unit can provide route guidance according to specific events or seasons based on past data. For example, the generation AI analyzes past data to provide route guidance according to specific events. For example, it proposes the optimal route for fireworks displays and festivals. In addition, in order to provide route guidance according to the season, the generation AI generates the optimal route based on past data. For example, during cherry blossom season, it provides guidance on a route that passes through cherry blossom viewing spots. In addition, the map generation unit can provide route guidance according to specific events or seasons based on past data. For example, during the Christmas season, it proposes a route that passes through illumination spots. This makes it possible to provide route guidance according to specific events or seasons.

[0038] When generating a detailed map of a station premises, the map generation unit can analyze the flow of people in real time and suggest routes that avoid congestion. For example, when the generation AI generates a detailed map of a station premises, the map generation unit analyzes the flow of people in real time and suggests routes that avoid congestion. For example, it guides users to less crowded passages, avoiding congested areas. The map generation unit also collects data from surveillance cameras and sensors within the station and analyzes the flow of people in real time. For example, it identifies areas where congestion is occurring and notifies the user. The map generation unit also allows the generation AI to analyze the flow of people in real time and dynamically update routes that avoid congestion. For example, if congestion is resolved, it re-suggests the shortest route. This makes it possible to provide routes that avoid congestion.

[0039] When generating a detailed map of the station premises, the map generation unit can track the user's location information in real time and dynamically update the route. For example, the map generation unit uses a generation AI to track the user's location information in real time and dynamically update the detailed map of the station premises. For example, the map generation unit recalculates the optimal route every time the user moves. The map generation unit also obtains location information from the user's smartphone or wearable device and provides real-time route guidance. For example, it provides guidance on the next action when the user approaches an elevator. The map generation unit also uses a generation AI to dynamically update the detailed map of the station premises based on the user's location information and propose the optimal route. For example, it fine-tunes the route as the user approaches the destination. This allows the map generation unit to track the user's location information in real time and dynamically update the route.

[0040] The map generation unit can also provide guidance to other complex facilities, such as shopping malls and airports, based on a detailed map of the station premises. For example, the map generation unit uses a generation AI to provide shopping mall guidance based on a detailed map of the station premises. For example, the map generation unit proposes a route including information on the locations of stores and elevators and escalators. To provide airport guidance, the map generation unit generates a detailed map of the airport and provides it to the user. For example, it provides guidance on the locations of check-in counters and gates. The map generation unit can also generate detailed maps of other complex facilities and provide guidance for the visually or hearing impaired. For example, it provides route guidance within large event venues and hospitals. This makes it possible to provide guidance to other complex facilities, such as shopping malls and airports.

[0041] The map generation unit provides visual guidance displays based on a detailed map of the station premises, making it possible to accommodate users other than the visually impaired. For example, the generation AI of the map generation unit provides visual guidance displays based on a detailed map of the station premises. For example, this is displayed on digital signage or a smartphone app. The map generation unit also provides visual guidance displays based on the generation AI of the map premises to accommodate users other than the visually impaired. For example, guidance is provided using maps and pictograms. The map generation unit also provides visual guidance displays based on a detailed map of the station premises by the generation AI, making it easy for users to understand intuitively. For example, color coding and icons are used for display. This makes it possible to provide visual guidance displays that are also suitable for users other than the visually impaired.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Braille block map generation app can further include a health status acquisition unit that monitors the user's health status. For example, the health status acquisition unit can monitor the user's heart rate and blood pressure in real time, and if an abnormality is detected, suggest a route to the nearest medical facility. The health status acquisition unit can also analyze the user's walking speed and fatigue level, and if a break is needed, guide the user to a route that includes rest spots. Furthermore, the health status acquisition unit can generate a reasonable travel route based on the user's health data. For example, for a user who has difficulty walking for long periods of time, it can suggest a route that prioritizes short distances.

[0044] The Braille block map generation app can also include a recommendation unit that suggests tourist spots and restaurants based on the user's preferences. For example, the recommendation unit analyzes the user's past visit history and evaluation data to suggest tourist spots that the user likes. The recommendation unit can also guide the user to nearby restaurants and cafes based on the user's current location and travel route. Furthermore, the recommendation unit can provide event information based on the user's preferences. For example, if the user likes music events, the recommendation unit can provide information on nearby concerts.

[0045] The Braille block map generation app can also include an emergency notification function to ensure the user's safety while traveling. For example, the emergency notification function provides a function to call the police or an ambulance with one touch when the user encounters an emergency. The emergency notification function can also automatically send the user's current location to a designated destination, enabling a prompt response. Furthermore, the emergency notification function can include a function to send emergency notifications to the user's family and friends. For example, a message containing the user's current location and details of the emergency can be sent to contacts designated by the user.

[0046] The Braille block map generation app can also include an entertainment section that provides entertainment to users while they are traveling. For example, the entertainment section can provide a function to play music or audiobooks according to the user's preferences. The entertainment section can also provide audio guidance on historical and cultural information related to the user's travel route. Furthermore, the entertainment section can provide quizzes and games that users can enjoy while traveling. For example, a system could be introduced in which users are asked quizzes about places they visit, and points are accumulated for correct answers.

[0047] The Braille block map generation app may further include a communication unit that supports communication while the user is traveling. For example, the communication unit may provide a function that allows the user to chat with other users in real time. The communication unit may also provide a function that allows the user to keep in touch with other users nearby. Furthermore, the communication unit may include a bulletin board function that allows the user to post questions or requests for advice while traveling. For example, if the user gets lost, the user can receive advice from other users.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The operation status acquisition unit acquires the operation status of each public transportation facility. For example, it acquires train delay information, bus operation status, and subway operation status in real time. The operation status acquisition unit acquires transportation facility operation information via API and updates it in real time. Step 2: The Braille block information acquisition unit acquires Braille block information from each local government. For example, it acquires information on the installation location, status, and updates of the Braille blocks. The Braille block information acquisition unit acquires Braille block information from the local government's database and reflects the latest information. Step 3: The map generation unit generates a map based on the information acquired by the operation status acquisition unit and the braille block information acquisition unit. For example, a generation AI can be used to generate a map that reflects the latest operation status and braille block information. Furthermore, it can also suggest optimal routes based on past data and generate detailed maps of station premises. Step 4: The voice guidance unit provides voice guidance based on the map generated by the map generation unit. For example, it uses generation AI to generate voice guidance for the visually or hearing impaired, and provides real-time voice guidance and voice guidance according to the user's emotional state.

[0050] (Example 2) The braille block map generation app according to an embodiment of the present invention is a system that supports visually and hearing impaired people to reach their destinations safely and efficiently using public transportation. As a result, the braille block map generation app can support visually and hearing impaired people to reach their destinations safely and efficiently using public transportation.

[0051] A braille block map generation app according to an embodiment includes an operation status acquisition unit, a braille block information acquisition unit, a map generation unit, and an audio guidance unit. The operation status acquisition unit acquires the operation status of each public transportation facility. For example, the operation status acquisition unit acquires train delay information in real time. The operation status acquisition unit can also acquire bus operation status. The operation status acquisition unit can also acquire subway operation status. For example, the operation status acquisition unit acquires transportation facility operation information via an API and updates it in real time. The braille block information acquisition unit acquires braille block information from each local government. For example, the braille block information acquisition unit acquires the installation locations of braille blocks. The braille block information acquisition unit can also acquire status information of the braille blocks. The braille block information acquisition unit can also acquire updated information about the braille blocks. For example, the braille block information acquisition unit acquires braille block information from a local government database and reflects the latest information. The map generation unit generates a map based on the information acquired by the operation status acquisition unit and the braille block information acquisition unit. For example, the map generation unit uses a generation AI to generate a map that reflects the latest train operation status and braille block information. The map generation unit can also suggest optimal routes based on past data. The map generation unit can also generate a detailed map of a station premises. For example, the map generation unit uses a generation AI to generate a detailed map that includes location information of elevators and escalators within a station premises. The voice guidance unit provides voice guidance based on the map generated by the map generation unit. For example, the voice guidance unit uses a generation AI to generate voice guidance for visually and hearing impaired persons. The voice guidance unit can also provide real-time voice guidance. The voice guidance unit can also provide voice guidance based on the user's emotional state. For example, the voice guidance unit uses a generation AI to analyze the user's emotional state and provide voice guidance that gives the user a sense of security. As a result, the braille block map generation app according to the embodiment can support visually and hearing impaired persons in safely and efficiently reaching their destinations using public transportation. For example, the braille block map generation app provides route guidance based on the latest train operation status and braille block information.By utilizing past data, optimal route suggestions can be made, allowing passengers to avoid congestion and move smoothly. Detailed maps and voice guidance within the station allow passengers to move around without getting lost even in complex station premises.

[0052] The operation status acquisition unit simultaneously collects weather and disaster information and can suggest safe routes to the user. For example, when the generation AI collects operation status and Braille block information, the operation status acquisition unit acquires weather data in real time and suggests routes according to changes in the weather. For example, indoor routes are given priority in the event of heavy rain or strong winds. The operation status acquisition unit also collects disaster information and generates routes that respond to emergencies such as earthquakes and floods. For example, routes that include evacuation routes are suggested in the event of an earthquake. The operation status acquisition unit also generates routes that the user can travel safely based on weather and disaster information and updates them in real time. For example, routes with a low risk of flooding are suggested in the event of heavy rain. This makes it possible to provide routes that the user can travel safely.

[0053] The map generation unit can analyze the user's movement history and provide an individually optimized route. For example, the map generation unit uses a generation AI to analyze the user's past movement history and propose an individually optimized route. For example, the map generation unit generates a route taking into account routes used in the past and preferred means of transportation. The map generation unit also proposes a route to avoid congestion based on the user's movement history. For example, it predicts busy times from past data and provides guidance on a route to travel during less crowded times. The map generation unit also analyzes the user's movement history and proposes a route that includes facilities and services that the user prefers. For example, it generates a route that passes through cafes and restaurants that the user has visited in the past. This makes it possible to provide the user with an individually optimized route.

[0054] The map generation unit can use the emotion estimation function to preferentially suggest routes that give the user a sense of security. The map generation unit, for example, uses the emotion estimation function to suggest routes that give the user a sense of security. For example, it preferentially guides the user to routes that the user has used safely based on their past travel history. The map generation unit also analyzes the user's real-time emotional state to generate routes that give the user a sense of security. For example, if the user is feeling anxious, it suggests a route with less congestion. The map generation unit also uses the emotion estimation function to dynamically update routes that give the user a sense of security. For example, if the user's emotions change during travel, it immediately regenerates a route. This makes it possible to provide a route that gives the user a sense of security.

[0055] The map generation unit can also provide route guidance for elderly people and children other than the visually or hearing impaired. For example, the map generation unit provides route guidance for elderly people based on information collected by the generation AI. For example, the map generation unit suggests routes that prioritize the use of elevators and escalators. In addition, the map generation unit generates safe routes to provide route guidance for children. For example, the map generation unit provides guidance on routes that pass through roads with little traffic or parks. In addition, in order to provide route guidance for elderly people and children, the map generation unit collects barrier-free information and provides it to the user. For example, the map generation unit suggests routes with few steps or routes with handrails. This makes it possible to provide route guidance for elderly people and children.

[0056] The map generation unit can also provide access information to tourist spots and event venues. For example, the generation AI collects information on tourist spots and provides access information for the visually and hearing impaired. For example, the map generation unit proposes a route that includes information on braille blocks and audio guidance within the tourist spot. In addition, to provide access information to event venues, the generation AI collects information on event dates and generates an optimal route. For example, the map generation unit provides route guidance that takes into account the congestion status of the event venue. In addition, to provide access information to tourist spots and event venues, the generation AI collects information on the operation status of public transportation in real time and provides it to the user. For example, the map generation unit proposes a route based on the operation status of trains and buses. This makes it possible to provide access information to tourist spots and event venues.

[0057] The map generation unit can use the emotion estimation function to suggest a route that will not cause stress to the user. For example, the map generation unit uses the emotion estimation function to suggest a route that will not cause stress to the user. For example, it prioritizes guidance to less crowded times and quieter routes. The map generation unit also analyzes the user's real-time emotional state and generates a route that reduces stress. For example, if the user is feeling stressed, it suggests a route that will help the user relax. The map generation unit also uses the emotion estimation function to dynamically update a route that will not cause stress to the user. For example, if the user's emotion changes during travel, it immediately regenerates the route. This makes it possible to provide a route that will not cause stress to the user.

[0058] The map generation unit analyzes past data, learns the user's movement patterns, and can predict future movement. For example, the map generation unit uses a generation AI to analyze past movement data and learn the user's movement patterns. For example, it predicts routes to be used on specific days of the week or time periods and suggests future movement. The map generation unit also predicts future movement based on the user's past movement history. For example, it predicts the routes and time periods that the user frequently uses from past data and suggests the optimal route. The map generation unit also predicts future movement by using a generation AI to analyze past data and learn the user's movement patterns. For example, it predicts movement patterns according to changes in seasons and weather and suggests routes. This makes it possible to predict the user's future movement and provide the optimal route.

[0059] The map generation unit can propose personalized routes that take into account the user's preferences and habits based on past data. For example, the map generation unit uses a generation AI to analyze past data and learn the user's preferences and habits. For example, for a user who likes a particular cafe or restaurant, the map generation unit proposes a route that passes through that location. The map generation unit also proposes personalized routes based on the user's past travel history. For example, the map generation unit generates routes that take into account routes used at specific times of day and preferred means of transportation. The map generation unit also uses a generation AI to analyze past data and propose routes that take into account the user's preferences and habits. For example, the map generation unit generates routes that pass through the user's favorite scenery or quiet places. This makes it possible to provide personalized routes based on the user's preferences and habits.

[0060] The map generation unit can use the emotion estimation function to identify the route that the user was most satisfied with from past data and propose a similar route. The map generation unit, for example, uses the emotion estimation function to identify the route that the user was most satisfied with from past data. For example, it analyzes past movement history and emotion data to extract routes that have high satisfaction. The map generation unit also identifies routes that have high satisfaction based on the user's past emotion data and proposes similar routes. For example, it generates routes that have similar conditions to routes that the user was satisfied with in the past. The map generation unit also uses the emotion estimation function to identify the route that the user was most satisfied with from past data and proposes a similar route. For example, it learns the characteristics of routes that have high satisfaction and generates a new route based on that. This makes it possible to provide a similar route based on the route that the user was most satisfied with.

[0061] The map generation unit can compare the user's movement patterns in other cities and countries based on past data and propose the optimal route from a global perspective. For example, the generation AI in the map generation unit analyzes past data and compares the user's movement patterns in other cities and countries. For example, it proposes the optimal route based on movement data from different cities. The map generation unit also analyzes the movement patterns of other countries and proposes the optimal route from a global perspective. For example, it generates a route that takes different cultures and transportation systems into consideration. The map generation unit also compares the user's movement patterns in other cities and countries based on past data and proposes the optimal route. For example, it provides guidance on a route that takes into account the congestion levels and transportation means of different cities. This makes it possible to provide the optimal route from a global perspective.

[0062] The map generation unit can provide route guidance according to specific events or seasons based on past data. For example, the generation AI analyzes past data to provide route guidance according to specific events. For example, it proposes the optimal route for fireworks displays and festivals. In addition, in order to provide route guidance according to the season, the generation AI generates the optimal route based on past data. For example, during cherry blossom season, it provides guidance on a route that passes through cherry blossom viewing spots. In addition, the map generation unit can provide route guidance according to specific events or seasons based on past data. For example, during the Christmas season, it proposes a route that passes through illumination spots. This makes it possible to provide route guidance according to specific events or seasons.

[0063] The map generation unit can use the emotion estimation function to identify routes where the user felt anxious from past data and propose improvements. The map generation unit, for example, uses the emotion estimation function to identify routes where the user felt anxious from past data. For example, it analyzes past movement history and emotion data to extract routes where the user felt anxious. The map generation unit also identifies routes where the user felt anxious based on the user's past emotion data and proposes improvements. For example, it generates an alternative route to the route where the user felt anxious. The map generation unit also uses the emotion estimation function to identify routes where the user felt anxious from past data and proposes improvements. For example, it learns the characteristics of routes where the user felt anxious and generates a new route based on that. This makes it possible to identify routes where the user felt anxious and provide improvements.

[0064] When generating a detailed map of a station premises, the map generation unit can analyze the flow of people in real time and suggest routes that avoid congestion. For example, when the generation AI generates a detailed map of a station premises, the map generation unit analyzes the flow of people in real time and suggests routes that avoid congestion. For example, it guides users to less crowded passages, avoiding congested areas. The map generation unit also collects data from surveillance cameras and sensors within the station and analyzes the flow of people in real time. For example, it identifies areas where congestion is occurring and notifies the user. The map generation unit also allows the generation AI to analyze the flow of people in real time and dynamically update routes that avoid congestion. For example, if congestion is resolved, it re-suggests the shortest route. This makes it possible to provide routes that avoid congestion.

[0065] When generating a detailed map of the station premises, the map generation unit can track the user's location information in real time and dynamically update the route. For example, the map generation unit uses a generation AI to track the user's location information in real time and dynamically update the detailed map of the station premises. For example, the map generation unit recalculates the optimal route every time the user moves. The map generation unit also obtains location information from the user's smartphone or wearable device and provides real-time route guidance. For example, it provides guidance on the next action when the user approaches an elevator. The map generation unit also uses a generation AI to dynamically update the detailed map of the station premises based on the user's location information and propose the optimal route. For example, it fine-tunes the route as the user approaches the destination. This allows the map generation unit to track the user's location information in real time and dynamically update the route.

[0066] The voice guidance unit can use the emotion estimation function to provide voice guidance that allows the user to travel with peace of mind. The voice guidance unit, for example, uses the emotion estimation function to provide voice guidance that allows the user to travel with peace of mind. For example, if the user is feeling anxious, an encouraging message is added. The voice guidance unit also analyzes the user's real-time emotional state and generates voice guidance that gives the user a sense of security. For example, guidance is provided in a tone that allows the user to relax. The voice guidance unit also uses the emotion estimation function to dynamically update voice guidance that allows the user to travel with peace of mind. For example, if the user's emotion changes, the voice guidance is immediately adjusted. This makes it possible to provide voice guidance that allows the user to travel with peace of mind.

[0067] The map generation unit can also provide guidance to other complex facilities, such as shopping malls and airports, based on a detailed map of the station premises. For example, the map generation unit uses a generation AI to provide shopping mall guidance based on a detailed map of the station premises. For example, the map generation unit proposes a route including information on the locations of stores and elevators and escalators. To provide airport guidance, the map generation unit generates a detailed map of the airport and provides it to the user. For example, it provides guidance on the locations of check-in counters and gates. The map generation unit can also generate detailed maps of other complex facilities and provide guidance for the visually or hearing impaired. For example, it provides route guidance within large event venues and hospitals. This makes it possible to provide guidance to other complex facilities, such as shopping malls and airports.

[0068] The map generation unit provides visual guidance displays based on a detailed map of the station premises, making it possible to accommodate users other than the visually impaired. For example, the generation AI of the map generation unit provides visual guidance displays based on a detailed map of the station premises. For example, this is displayed on digital signage or a smartphone app. The map generation unit also provides visual guidance displays based on the generation AI of the map premises to accommodate users other than the visually impaired. For example, guidance is provided using maps and pictograms. The map generation unit also provides visual guidance displays based on a detailed map of the station premises by the generation AI, making it easy for users to understand intuitively. For example, color coding and icons are used for display. This makes it possible to provide visual guidance displays that are also suitable for users other than the visually impaired.

[0069] The voice guidance unit can use the emotion estimation function to provide voice guidance that does not cause stress to the user. The voice guidance unit, for example, uses the emotion estimation function to provide voice guidance that does not cause stress to the user. For example, guidance is provided in a tone that allows the user to relax. The voice guidance unit also analyzes the user's real-time emotional state and generates voice guidance that reduces stress. For example, if the user is feeling stressed, guidance is provided in a calm voice. The voice guidance unit also uses the emotion estimation function to dynamically update voice guidance that does not cause stress to the user. For example, if the user's emotion changes, the voice guidance is immediately adjusted. This makes it possible to provide voice guidance that does not cause stress to the user.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The Braille block map generation app can further include a health status acquisition unit that monitors the user's health status. For example, the health status acquisition unit can monitor the user's heart rate and blood pressure in real time, and if an abnormality is detected, suggest a route to the nearest medical facility. The health status acquisition unit can also analyze the user's walking speed and fatigue level, and if a break is needed, guide the user to a route that includes rest spots. Furthermore, the health status acquisition unit can generate a reasonable travel route based on the user's health data. For example, for a user who has difficulty walking for long periods of time, it can suggest a route that prioritizes short distances.

[0072] The Braille block map generation app can also include a recommendation unit that suggests tourist spots and restaurants based on the user's preferences. For example, the recommendation unit analyzes the user's past visit history and evaluation data to suggest tourist spots that the user likes. The recommendation unit can also guide the user to nearby restaurants and cafes based on the user's current location and travel route. Furthermore, the recommendation unit can provide event information based on the user's preferences. For example, if the user likes music events, the recommendation unit can provide information on nearby concerts.

[0073] The Braille block map generation app can also include an emergency notification function to ensure the user's safety while traveling. For example, the emergency notification function provides a function to call the police or an ambulance with one touch when the user encounters an emergency. The emergency notification function can also automatically send the user's current location to a designated destination, enabling a prompt response. Furthermore, the emergency notification function can include a function to send emergency notifications to the user's family and friends. For example, a message containing the user's current location and details of the emergency can be sent to contacts designated by the user.

[0074] The Braille block map generation app can also include an entertainment section that provides entertainment to users while they are traveling. For example, the entertainment section can provide a function to play music or audiobooks according to the user's preferences. The entertainment section can also provide audio guidance on historical and cultural information related to the user's travel route. Furthermore, the entertainment section can provide quizzes and games that users can enjoy while traveling. For example, a system could be introduced in which users are asked quizzes about places they visit, and points are accumulated for correct answers.

[0075] The Braille block map generation app may further include a communication unit that supports communication while the user is traveling. For example, the communication unit may provide a function that allows the user to chat with other users in real time. The communication unit may also provide a function that allows the user to keep in touch with other users nearby. Furthermore, the communication unit may include a bulletin board function that allows the user to post questions or requests for advice while traveling. For example, if the user gets lost, the user can receive advice from other users.

[0076] The Braille block map generation app can also provide a relaxation function according to the user's emotional state. For example, if the user is feeling anxious, the relaxation function can play relaxing music or natural sounds. The relaxation function can also analyze the user's emotional state and provide breathing exercises or meditation guides to reduce stress. Furthermore, the relaxation function can display visual content that helps the user relax. For example, if the user is using a smartphone, the relaxation function can display beautiful landscapes or artwork.

[0077] The Braille Block MAP Generation App can also provide customized voice guidance according to the user's emotional state. For example, if the user is nervous, the voice guidance will provide guidance in a calm and soothing tone. If the user is tired, the voice guidance can also add an encouraging message. Furthermore, the voice guidance can analyze the user's emotional state in real time and dynamically adjust the optimal guidance method. For example, if the user is relaxed, the voice guidance will provide guidance in a light-hearted tone.

[0078] The Braille Block Map Generation app can also provide personalized routes based on the user's emotional state. For example, if the user is feeling anxious, it will prioritize routes that give a sense of security. If the user is feeling relaxed, it can also suggest routes with beautiful scenery. Furthermore, it can analyze the user's emotional state in real time, and if their emotions change during travel, it can instantly regenerate a route. For example, if the user begins to feel stressed, it will suggest a quieter route.

[0079] The Braille Block MAP generation app can also provide emergency response functions based on the user's emotional state. For example, if the user experiences extreme anxiety or panic, it can automatically activate an emergency call function. It can also analyze the user's emotional state in real time and provide access to counseling services as needed. Furthermore, if the user is in an emotionally unstable state, it can guide them to a nearby safe location. For example, if the user is in a panic state, it can suggest a route to the nearest police station or hospital.

[0080] The Braille block map generation app can also provide a feedback function based on the user's emotional state. For example, it can record the emotions the user felt while traveling and review them later. It can also suggest improvements to travel routes and guidance methods based on the user's emotional data. It can also provide a function that allows users to share the emotions they felt while traveling with other users. For example, a user can share the sense of security or anxiety they felt on a particular route with other users for reference.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The operation status acquisition unit acquires the operation status of each public transportation facility. For example, it acquires train delay information, bus operation status, and subway operation status in real time. The operation status acquisition unit acquires transportation facility operation information via API and updates it in real time. Step 2: The Braille block information acquisition unit acquires Braille block information from each local government. For example, it acquires information on the installation location, status, and updates of the Braille blocks. The Braille block information acquisition unit acquires Braille block information from the local government's database and reflects the latest information. Step 3: The map generation unit generates a map based on the information acquired by the operation status acquisition unit and the braille block information acquisition unit. For example, a generation AI can be used to generate a map that reflects the latest operation status and braille block information. Furthermore, it can also suggest optimal routes based on past data and generate detailed maps of station premises. Step 4: The voice guidance unit provides voice guidance based on the map generated by the map generation unit. For example, it uses generation AI to generate voice guidance for the visually or hearing impaired, and provides real-time voice guidance and voice guidance according to the user's emotional state.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an operation status acquisition unit that acquires the operation status of each public transportation facility; a braille block information acquisition unit that acquires braille block information from each local government; a map generation unit that generates a map based on the information acquired by the operation status acquisition unit and the braille block information acquisition unit; a voice guidance unit that provides voice guidance based on the map generated by the map generation unit. A system characterized by:

2. The operation status acquisition unit Collecting weather and disaster information simultaneously and suggesting safe routes to users 2. The system of claim 1.

3. The map generation unit Analyzes a user's travel history and provides an individually optimized route for the user 2. The system of claim 1.

4. The map generation unit Prioritize routes that give users a sense of security 2. The system of claim 1.

5. The map generation unit It also provides route guidance for elderly people and children who are not visually or hearing impaired.

2. The system of claim 1.

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