System

Abstract

A system is provided.SOLUTION: Means for inputting a destination and a request for movement from a user in a voice or text format, voice recognition means for converting the voice input into text data, means for transmitting the text data and current position information of the user to a server, natural language processing means for analyzing the text data and specifying the destination of the user in the server, means for collecting weather information, traffic information, and facility information in order to calculate an optimal movement route, and means for calculating the optimal movement route on the basis of the collected information, A system comprising: means for generating specific route guidance; means for transmitting the generated route guidance to a user's device; speech synthesis means for converting the route guidance received at the device into voice; means for providing the user with the route guidance provided by speech synthesis; and means for monitoring the current position and surrounding conditions of the user in real time while the user is moving and updating an optimal route based on the updated information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional route guidance systems required users to constantly operate devices such as smartphones visually, increasing the risk of accidents caused by users walking while using their smartphones. Furthermore, it was difficult to reflect real-time weather and traffic information, and the system often failed to provide optimal route guidance. Furthermore, for users who are not good at reading maps, it was difficult to reach their destination using only visual information, making it difficult to achieve efficient travel. [Means for solving the problem]

[0005] The present invention provides a means for users to input their destination and travel requests by voice or text, and converts the input voice data into text using a voice recognition means. Furthermore, this text data and the user's current location information are sent to a server, where the server identifies the user's destination using natural language processing means. The system also includes a means for collecting weather, traffic, and facility information for calculating the optimal travel route. Based on this information, the server generates an optimal travel route and specific route guidance, and transmits this data to the user's terminal. The terminal converts the received data into speech using a speech synthesis means and provides it to the user. The system also includes a means for monitoring the user's current location and surrounding conditions in real time while the user is traveling, and for appropriately updating the optimal route based on the updated information. This minimizes visual interaction and achieves safe and efficient route guidance.

[0006] A "user" is a person who uses the system and receives guidance to a destination.

[0007] A "destination" is a location or target point to which a user is going.

[0008] A "travel request" is a request regarding a destination and a means of transportation that a user inputs to the system.

[0009] "A means for inputting in the form of voice or text" is an interface that allows a user to input instructions to the system using voice or text.

[0010] "Speech recognition means" is a technology that converts voice input into text data.

[0011] "Text data" is character information converted by a voice recognition means.

[0012] "Current location information" is data that indicates the user's current location, and is primarily generated using GPS technology.

[0013] A "server" is a computer system that receives, analyzes, and processes data sent by users.

[0014] "Natural language processing means" is a technology for analyzing text data and understanding and processing its meaning.

[0015] "Weather information" is data about current and forecast weather.

[0016] "Traffic information" refers to data on the operation status of public transportation and road congestion.

[0017] "Facility information" is data about buildings and services located along the route.

[0018] A "travel route" is a route that a user must take to reach a destination.

[0019] "Specific directions" are information that includes detailed instructions for a user to reach a destination.

[0020] "Speech synthesis means" is a technology that converts text data into speech.

[0021] "Voice guidance" refers to voice instructions generated by a voice synthesis means.

[0022] "Monitoring" means continuously monitoring the user's current location and surroundings.

[0023] "Means for updating" refers to the function of appropriately changing the guidance provided by the system based on new information.

[0024] Based on these definitions, the present invention provides a system that supports users' mobility safely and efficiently. [Brief explanation of the drawings]

[0025] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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, a 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), and an APU (Accelerated Processing Unit).

[0029] 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.

[0030] 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.

[0031] 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), Bluetooth (registered trademark), etc.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0037] 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.

[0038] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0039] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0045] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0046] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. The following describes a specific embodiment of the system.

[0047] Overview of the embodiment

[0048] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific directions based on this travel route are generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides the user with voice guidance. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated as appropriate based on the updated information.

[0049] Program processing details

[0050] Receiving User Input

[0051] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they might say, "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system.

[0052] Data analysis on the server

[0053] The device sends the text data and current location information obtained from GPS to the server. The server analyzes the received text data and identifies the destination by natural language processing. For example, the location "Shinjuku Station" is identified.

[0054] Gathering information and calculating the best route

[0055] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination information. This allows the optimal travel route to be calculated taking into account traffic congestion and weather. For example, a route such as "The optimal route from the current location to Shinjuku Station is to take the train, transfer at Station A, and get off at Station B" may be generated.

[0056] Providing audio guidance

[0057] The server then sends the generated route guidance data to the smartphone, which then converts the received data into voice using a speech synthesis system and provides directions to the user. For example, the smartphone provides voice instructions such as, "Take the train on line A that arrives in two minutes, get off at station B, and transfer to line C."

[0058] Real-time information updates

[0059] While traveling, the server monitors the user's current location and surrounding conditions in real time, and responds immediately if new information or emergencies arise. For example, if the traffic congestion situation on the road to the destination changes, the server will recalculate the optimal route in real time and provide new guidance. This allows the user to travel safely and efficiently based on the latest information.

[0060] The above is a description of a specific embodiment of the present invention. This eliminates the need for map reading skills, allowing users to safely reach their destination without constantly looking at their smartphone. Furthermore, by flexibly responding to changes in weather and traffic conditions, users can travel efficiently along the optimal route.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[0064] Step 2:

[0065] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[0066] Step 3:

[0067] The device sends the converted text data and the current location information obtained from the GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station" and the current location data (latitude and longitude) are sent.

[0068] Step 4:

[0069] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[0070] Step 5:

[0071] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[0072] Step 6:

[0073] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[0074] Step 7:

[0075] The server then sends the generated route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0076] Step 8:

[0077] The device converts the route guidance data received from the server into speech using a speech synthesis system. The text data, such as "Take the train on line A arriving in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station," is converted into speech.

[0078] Step 9:

[0079] The device will then provide the user with directions converted into voice, such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0080] Step 10:

[0081] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road on the right is clear, so please proceed that way" are provided in real time.

[0082] The above are the specific processing steps of the system. This series of processes allows the user to reach their destination safely and efficiently through voice guidance.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Currently, in order to reach their destination efficiently, users are required to have map reading skills and to be aware of traffic information in advance. In particular, it is difficult to flexibly respond to changing traffic and weather conditions in real time, and there are limited ways to receive appropriate guidance while traveling, which can cause additional stress and risk to users. Furthermore, when using voice input, the recognition accuracy and analysis capabilities are often insufficient, making it difficult to provide accurate guidance.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for inputting a destination and travel requests from a user in voice or text format, speech recognition means for converting the voice input into text data, means for transmitting the text data and the user's current location information to the server, natural language processing means, means for collecting weather information, traffic information, and location information to calculate an optimal travel route, and means for providing the user with navigation prompts generated via a generative AI model, thereby enabling the user to safely and efficiently receive optimal route guidance that is updated in real time.

[0088] A "destination" is a final destination to which a user wishes to travel.

[0089] "Travel requests" refer to specific requests about how the user wants to travel, such as arriving in the shortest time possible or taking a specific route.

[0090] "Means for input in voice or text form" refers to an interface that allows a user to input instructions or questions by voice or text using the terminal.

[0091] "Speech recognition means" refers to technology that analyzes a user's voice data and converts it into corresponding text data.

[0092] "Text data" refers to character string data converted by speech recognition means.

[0093] "Server" refers to a computer system that receives data sent from a user's terminal via a network and analyzes and processes it.

[0094] "Current location information" refers to real-time location data obtained by a user's device using location information technology such as GPS.

[0095] "Natural language processing means" refers to technology for analyzing text data, understanding natural human language, and identifying intent.

[0096] "Weather information" refers to weather data related to the user's current location and travel route.

[0097] "Traffic information" refers to movement-related dynamic data such as congestion on roads and public transportation and accident information.

[0098] "Location information" refers to geographic data such as landmarks and facilities related to a travel route.

[0099] "Means for calculating optimal travel routes" refers to algorithms or technologies that calculate routes that will allow users to reach their destinations efficiently based on collected information.

[0100] "Route guidance data" refers to data that includes specific travel instructions and route information provided to a user.

[0101] "Speech synthesis means" refers to a technology that converts text data into speech and provides the user with voice guidance.

[0102] "Real-time monitoring" refers to constantly monitoring the user's current location and surrounding conditions, and collecting and updating information.

[0103] "Generative AI Model" refers to the artificial intelligence technology used to generate the driving prompts that are provided to the user.

[0104] "Prompt sentence" refers to text data generated by a generative AI model and provided to the user, including travel routes and operation instructions.

[0105] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. Specific embodiments of this system are described in detail below.

[0106] User input

[0107] A user uses a mobile device such as a smartphone to input their destination and travel requests in voice or text format. For example, the user might say, "Please tell me the route to Shinjuku Station." This input is made via a user interface.

[0108] Voice recognition by device

[0109] The device uses a speech recognition system to convert voice input into text data. Specifically, it uses a speech recognition API to obtain the voice content as text data. The device stores this converted text data internally.

[0110] Sending data from the device to the server

[0111] The device sends text data and current location information to the server. The current location information is obtained using location information technology such as GPS. This data is sent to the server using a REST API.

[0112] Server-based natural language processing and analysis

[0113] The server analyzes the received text data and performs natural language processing to identify the user's destination. For natural language processing, for example, Apache OpenNLP or a cloud-based natural language processing API is used. This allows the server to identify a specific destination, such as "Shinjuku Station."

[0114] Server information collection

[0115] The server utilizes weather information APIs and traffic information APIs to collect the necessary information based on the user's current location and the specified destination. For example, OpenWeatherMap API and Google Maps API are used in this step. The server integrates and processes the data obtained from these APIs.

[0116] Optimal route calculation by server

[0117] The server calculates the optimal travel route based on the collected information. This calculation uses algorithms such as Dijkstra's algorithm and A algorithm. The server generates the calculated travel route as route guidance data. For example, it generates a detailed route such as "The optimal route from your current location to Shinjuku Station is to take the train, transfer at station A, and get off at station B."

[0118] Sending data from the server to the device

[0119] The server then sends the generated route guidance data to the device in JSON format using a REST API. The device then converts the data into speech using a speech synthesis system.

[0120] Voice guidance via terminal

[0121] The device uses a speech synthesis system, such as the Google Text-to-Speech API, to convert the received route guidance data into audio. For example, the device might provide instructions such as, "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C."

[0122] User movement and real-time updates

[0123] The user begins traveling by following the voice guidance from the device, and the smartphone continues to periodically send GPS location information to the server. The server monitors the user's current location and surrounding conditions in real time, and recalculates the optimal route based on new traffic and weather information. For example, if a traffic accident occurs along the way, it immediately calculates a detour route and sends that information to the device. The device then provides new route guidance to the user via voice.

[0124] Using generative AI models

[0125] The system uses a generative AI model to generate prompts for directions to be provided to the user. These prompts are designed to be intuitively understandable to the user. For example, a prompt might be, "Please tell me the route to Shinjuku Station."

[0126] In this way, users can safely and efficiently receive optimal route guidance that is updated in real time.By combining technologies such as voice recognition, natural language processing, real-time information collection and analysis, and voice synthesis, this system can significantly improve users' travel experience.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] User input

[0130] The user uses the voice input function of the smartphone to input their destination and travel requests. Specifically, they input "Please tell me the route to Shinjuku Station." This voice data is sent to the device.

[0131] Step 2:

[0132] Voice recognition by device

[0133] The device uses a speech recognition system (for example, a speech recognition API) to convert the user's voice input into text data. The input is voice data, and the output is text data such as "Please tell me the route to Shinjuku Station." In this process, the voice waveform is converted into text.

[0134] Step 3:

[0135] Sending data from the device to the server

[0136] The device sends text data obtained by speech recognition and current location information obtained from GPS to the server. The input is the text data and current location information, and the output is a request containing this information sent to the server. This request is sent as JSON format data.

[0137] Step 4:

[0138] Server-based natural language processing and analysis

[0139] The server analyzes the received text data. Specifically, it uses a natural language processing algorithm (for example, Apache OpenNLP) to identify the user's destination. The input is the text data "Please tell me the route to Shinjuku Station," and the output is the identified destination, "Shinjuku Station." During this process, the text data is analyzed and semantic analysis is performed.

[0140] Step 5:

[0141] Server information collection

[0142] The server collects necessary data from weather information APIs and traffic information APIs based on the user's current location and the specified destination. The input is the current location and destination information, and the output is collected data such as weather information and traffic information. The server integrates this data to obtain real-time information useful for travel.

[0143] Step 6:

[0144] Optimal route calculation by server

[0145] The server calculates the optimal travel route based on the collected information. Specifically, it uses shortest path calculation algorithms such as Dijkstra's algorithm and A algorithm. The inputs are weather information, traffic information, and information on the current location and destination, and the output is the optimal travel route. For example, it calculates a specific route such as "The optimal route from the current location to Shinjuku Station is to take the train, change at station A, and get off at station B."

[0146] Step 7:

[0147] Sending data from the server to the device

[0148] The server sends the generated optimal route guidance data to the terminal. The input is the optimal travel route information, and the output is the JSON formatted guidance data sent to the terminal. This data includes specific route guidance and route information.

[0149] Step 8:

[0150] Device-based speech synthesis

[0151] The device uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the received route guidance data into speech. The input is route guidance data, and the output is voice guidance. For example, specific instructions such as "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C" are provided by voice.

[0152] Step 9:

[0153] User movement and real-time updates

[0154] The user starts moving according to the voice guidance from the device. During the movement, the device periodically sends GPS location information to the server. The input is the user's current location information, and the output is the real-time updated location information sent to the server.

[0155] Step 10:

[0156] Real-time updates from the server

[0157] The server periodically receives information about the user's current location and the latest weather and traffic information, and recalculates the optimal route. If there is a traffic accident or a sudden change in weather along the way, the server takes that information into account and sends a new optimal route to the user. The input is real-time information, and the output is updated guidance data for the optimal route. The device receives this data and again provides guidance using a voice synthesis system.

[0158] Through the above processing steps, the system provides users with an optimal travel route that is updated in real time, thereby improving the safety and efficiency of travel.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] Modern self-driving vehicles often lack the ability to automatically correct routes based on traffic and weather information while allowing users to easily give voice instructions to their destinations and providing real-time guidance on optimal routes. This can compromise safety and efficiency during travel. A particular challenge is the inability to quickly respond to complex traffic situations and unpredictable weather changes.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes a speech recognition means for converting speech input into text data, a natural language processing means for identifying the user's destination, and a means for collecting weather information, traffic information, and facility information for calculating the optimal travel route, thereby enabling the server to guide the user along the optimal route to the destination specified by speech and to modify the route in real time based on the latest information while traveling.

[0164] A "user" is a person who uses the system to give instructions for travel to a destination.

[0165] A "voice recognition means" is a device or system that converts a user's voice input into text data.

[0166] A "natural language processing means" is a device or algorithm that analyzes text data and identifies the user's destination.

[0167] "Weather information" is data about current and forecast weather.

[0168] "Traffic information" refers to data on road congestion, traffic accidents, construction work, etc.

[0169] "Facility information" is data on buildings and locations related to the destination and stops along the way.

[0170] The "optimal travel route" is the most efficient and safe route calculated based on weather information, traffic information, and facility information.

[0171] A "voice synthesis means" is a device or system that converts text data into voice and provides it to the user.

[0172] An "autonomous vehicle" is a motor vehicle that drives itself without driver intervention.

[0173] "Real-time monitoring" means constantly monitoring the user's current location and surrounding conditions as up-to-date information.

[0174] "Driving control" means managing the operation of an autonomous vehicle according to an optimal driving path.

[0175] This invention is a navigation system for autonomous vehicles that supports users' travel safely and efficiently. This system is composed of a combination of speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis technologies.

[0176] System Overview

[0177] Users use a mobile device such as a smartphone to input their destination and travel requests in voice or text format. The system operates as follows after receiving this input.

[0178] Hardware and software used

[0179] Hardware: smartphones, microphones, self-driving vehicles

[0180] Software: Python3, Google Speech Recognition API, Google Text-to-Speech (gTTS), Geopy library, OpenWeather API, HERE Traffic API

[0181] Data processing and calculation

[0182] 1. Speech Recognition:

[0183] The user inputs the destination by voice through the smartphone's microphone.

[0184] The Google Speech Recognition API in the smartphone converts the voice data into text data.

[0185] 2. Natural Language Processing:

[0186] The smartphone sends the acquired text data and the user's current location information (GPS data) to the server.

[0187] The server analyzes the text data using natural language processing means and identifies the user's destination.

[0188] 3. Real-time information collection and optimal route calculation:

[0189] Based on the destination and current location information, the server obtains weather information, traffic information, and facility information through various APIs.

[0190] Based on the collected information, the optimal travel route is calculated using libraries such as Geopy.

[0191] 4. Generate voice prompts:

[0192] The server sends the calculated optimal route information to the smartphone.

[0193] The smartphone uses Google Text-to-Speech (gTTS) to convert route information into voice guidance.

[0194] 5. Real-time information updates:

[0195] While moving, the server monitors the current location and surrounding conditions, updating the optimal route in real time.

[0196] If necessary, the server sends updated guidance data to the smartphone and provides new guidance via voice synthesis.

[0197] Specific examples

[0198] For example, if a user says, "Please tell me the route to Shibuya Station," this voice command is captured by the smartphone's microphone. The voice data is converted into text data (e.g., "To Shibuya Station") using the Google Speech Recognition API. The text data and current location information are then sent to the server, which uses natural language processing to identify the destination as "Shibuya Station."

[0199] Here, the server obtains weather and traffic information from the API and calculates the optimal route. For example, it takes into account the current traffic congestion and weather conditions and generates specific guidance such as "The optimal route from your current location to Shibuya Station is to take the train on Line A and get off at Station B."

[0200] This information is sent to the smartphone and provided to the user as voice guidance using Google Text-to-Speech (gTTS). While the user is moving, the server constantly monitors the current location and traffic conditions in real time, recalculating the optimal route based on the latest information as needed and providing new guidance.

[0201] Prompt Sentence Examples

[0202] "When a user says 'to Shibuya Station,' how can I calculate the optimal route to the destination and provide guidance incorporating real-time traffic and weather information? How can I implement this in Python?"

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The user uses the smartphone's microphone to input their destination by voice. This voice data becomes the first input to the system. For example, they might say, "Please tell me the route to Shibuya Station."

[0206] Step 2:

[0207] The voice data acquired by the device is converted into text data using the Google Speech Recognition API. This is a process using speech recognition, where the input is raw voice data and the output is text data such as "To Shibuya Station."

[0208] Step 3:

[0209] The device sends the current location information obtained from GPS along with the text data to the server. The input here is the text data and GPS data, and the output is the data sent to the server.

[0210] Step 4:

[0211] The server analyzes the received text data using natural language processing and identifies the user's destination. The input is text data, and the output is the identified destination, such as "Shibuya Station."

[0212] Step 5:

[0213] The server uses APIs to collect weather, traffic, and facility information based on the user's current location and destination information. The input is GPS data and the identified destination, and the output is various real-time information.

[0214] Step 6:

[0215] The server calculates the optimal route using the collected real-time information. For example, using the Geopy library, the input is real-time information and current location / destination information, and the output is specific route guidance data.

[0216] Step 7:

[0217] The server then sends the generated route guidance data to the smartphone. The input is route guidance data, and the output is data transmission to the smartphone.

[0218] Step 8:

[0219] The device converts the received route guidance data into voice data using Google Text-to-Speech (gTTS) and provides it to the user as voice guidance. The input is text data for route guidance and the output is voice data, and this voice guidance is played back to the user.

[0220] Step 9:

[0221] While moving, the server monitors the current location and surrounding conditions in real time, and recalculates the optimal route based on new information as needed. The recalculated route is then sent back to the smartphone, which then provides the user with updated guidance via voice synthesis. Here, the input is real-time location information and surrounding information, and the output is updated route guidance data and its voice guidance.

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[0224] Overview of the embodiment

[0225] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific route guidance based on this travel route is generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides voice guidance to the user. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated appropriately based on the updated information. In addition, an emotion recognition means is used to grasp the user's emotional state, and route guidance is provided according to that emotion.

[0226] Program processing details

[0227] Receiving User Input

[0228] Using a smartphone, a user inputs their destination and travel requests by voice or text. For example, they might input "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system. Furthermore, the emotion recognition engine also analyzes the user's emotions when inputting the voice.

[0229] Data analysis on the server

[0230] The device sends the text data, current location information obtained from GPS, and emotion recognition results to the server. The server analyzes the received text data and identifies the destination through natural language processing. For example, the location "Shinjuku Station" may be identified. The user's emotion data is also reflected appropriately at this stage.

[0231] Gathering information and calculating the best route

[0232] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination. This allows the optimal travel route to be calculated, taking into account traffic congestion and weather. For example, if the user is feeling stressed, a route that takes an uncrowded train or passes through a relaxing stopover point will be selected.

[0233] Generating specific directions based on emotions

[0234] The server uses the results of the emotion recognition engine to reflect the user's emotional state in the generated route guidance. For example, if the user is feeling stressed, it will add a recommendation to take a break in a quiet place. Specific route guidance data might include instructions such as, "There is a park on the way to your destination, so it would be a good idea to take a short break."

[0235] Providing audio guidance

[0236] The server then sends the generated specific route guidance data to the smartphone. The smartphone then converts the received data into voice using a speech synthesis system. Voice guidance such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break there" is provided.

[0237] Real-time information updates

[0238] While moving, the server monitors the current location and surrounding conditions in real time. If new information or an emergency occurs, it responds immediately. For example, if there is a traffic jam on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[0239] The above is a description of a specific embodiment of the present invention. This series of processes allows the user to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making travel more stress-free and comfortable.

[0240] The processing flow will be explained below.

[0241] Step 1:

[0242] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[0243] Step 2:

[0244] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[0245] Step 3:

[0246] The device uses an emotion recognition engine to analyze the user's emotions during voice input. Emotional data is generated based on the user's tone of voice, speaking rate, pauses, etc. At this time, emotional states such as "the user is nervous" are identified.

[0247] Step 4:

[0248] The device sends the converted text data, emotion recognition data, and current location information obtained from GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station," the current location data, and the emotion recognition result (e.g., "I'm nervous") are sent.

[0249] Step 5:

[0250] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[0251] Step 6:

[0252] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[0253] Step 7:

[0254] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[0255] Step 8:

[0256] The server also takes into account the results of the emotion recognition engine to generate directions that reflect the user's emotional state. For example, if the user is feeling nervous, the server will select a route that is relatively relaxing and includes quiet stops. In this way, a route such as "Take Line A to Station B, then transfer to Line C to reach Shinjuku Station. Stopping at a park along the way will help you relax" will be generated.

[0257] Step 9:

[0258] The server then sends the generated specific route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by a park on the way to relax."

[0259] Step 10:

[0260] The device then converts the route guidance data received from the server into speech using a speech synthesis system. The text data is converted into speech, such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. You can also stop by a park on the way to relax."

[0261] Step 11:

[0262] The device will then provide the user with directions converted into audio, such as "Take the train on line A, which arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by the park on the way to relax."

[0263] Step 12:

[0264] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[0265] These are the specific processing steps of the system. This series of processes allows users to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making the journey itself more stress-free and comfortable.

[0266] Example 2

[0267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0268] Conventional travel guidance systems provide routes without considering the user's emotional state, which can cause stress for the user. Furthermore, many systems are unable to respond to real-time changes in the situation, making it difficult for users to deal with congestion and unexpected situations. Therefore, there was a need for the development of a system that provides guidance that takes the user's psychological state into consideration and always guides them to the optimal route.

[0269] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0270] In this invention, the server includes a natural language processing means, a means for grasping an emotional state, and a means for calculating an optimal travel route, which enables the server to analyze a user's voice input, provide optimal route guidance according to the user's emotional state, and respond to changes in the situation in real time.

[0271] "Speech recognition means" is a technology for converting a user's voice input into text data.

[0272] "Natural language processing means" is a technology for analyzing text data to identify the user's intentions and destination.

[0273] "Emotional state" is data that represents the psychological state of the user, and indicates emotions such as stress or joy that the user is feeling.

[0274] "Speech synthesis means" is a technology for outputting text data as voice.

[0275] An "optimal travel route" is a route that is determined to be the most efficient and safe way to reach a destination.

[0276] "Weather information" is data about current and future weather conditions.

[0277] "Traffic information" refers to data on congestion and delays on roads and public transportation.

[0278] "Facility information" is data relating to facilities on the travel route, and includes, for example, location information of restaurants, parks, and the like.

[0279] "Real-time monitoring" refers to monitoring the user's current location and surrounding conditions in real time.

[0280] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[0281] Receiving User Input

[0282] Users use mobile devices such as smartphones to input their destinations and travel requests in voice or text format. When a user types, "Please tell me the route to Shinjuku Station," the smartphone's voice recognition system (e.g., Google Voice Recognition) converts the voice data into text data. At this time, an emotion recognition engine (e.g., Amazon Rekognition) also analyzes the voice data in parallel to determine the user's emotional state.

[0283] Sending current location information and text data

[0284] The device sends the text data converted by the speech recognition system, the current location information obtained from GPS, and the emotion recognition results to the server. This data includes the user's current location, destination, and emotional state.

[0285] Data analysis and destination identification

[0286] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API) to identify the user's destination. The server also analyzes the emotional data to understand the user's psychological state. For example, if "Shinjuku Station" is identified as the destination, it may be determined that the user is feeling stressed.

[0287] Gathering necessary information

[0288] Based on the specified destination and current location, the server uses weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API) to collect the necessary data, including existing congestion and weather data.

[0289] Calculating the best route

[0290] The server uses the collected information to calculate the optimal route, taking the user's emotional state into account in the process. For example, if the user is feeling stressed, the server can choose a quieter, more relaxing route to avoid crowds.

[0291] Generating directions based on emotions

[0292] The server generates specific route guidance that reflects the results of the emotion recognition engine. For example, it might generate a guidance sentence such as, "There is a park on the way to your destination, so it would be a good idea to take a short break." This guidance takes the user's psychological state into consideration.

[0293] Providing guidance data

[0294] The server sends the generated route guidance data to the device. The device converts the received data into speech using a speech synthesis system (e.g., Google Text-to-Speech) and provides the user with audio guidance. For example, the device might say, "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[0295] Real-time information updates

[0296] While moving, the device sends its current location and surrounding conditions to the server in real time. The server uses this information to update the guidance as needed. For example, if there is traffic congestion on the route, the instructions will be instantly changed to, "The road to the right is clear, so please take that route."

[0297] Examples and prompts

[0298] Example of a user entering "Route to Shinjuku Station" by voice:

[0299] 1. The user types into their smartphone, "Please tell me the route to Shinjuku Station."

[0300] 2. The voice recognition system converts this into text, and the emotion recognition engine identifies it as "stress."

[0301] 3. The device sends this data to the server.

[0302] 4. The server determines the destination, "Shinjuku Station," and the user's emotional state.

[0303] 5. The server uses weather information API and traffic information API to calculate the optimal route and select a route that passes through a quiet park, etc.

[0304] 6. The server generates guidance data such as "There is a park on the way to your destination, so it would be a good idea to take a short break there," and sends it to the device.

[0305] 7. The terminal uses a speech synthesis system to convert the information into speech and provide guidance to the user.

[0306] 8. The server monitors the latest situation and updates the information.

[0307] Prompt Sentence Examples

[0308] "Please explain the program process flow for a system in which a user uses a smartphone to ask for directions by voice, converts that voice into text data, and performs emotion recognition. Furthermore, please state the specific steps in order in which the server calculates the optimal route based on the current location and destination information, and provides voice guidance according to the user's emotional state."

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Step 1:

[0311] The user uses a smartphone to input their destination and travel requests in voice or text format. For example, they might say, "Please tell me the route to Shinjuku Station." The input voice is captured as voice data through the smartphone's microphone.

[0312] Input: User voice input

[0313] Output: Audio data

[0314] Step 2:

[0315] The device analyzes the acquired voice data using a voice recognition system (e.g., Google Voice Recognition), converts the voice into text data, and then analyzes the emotional state of the user at the time of voice input using an emotion recognition engine (e.g., Amazon Rekognition).

[0316] Input: Audio data

[0317] Output: Text data and emotional state data

[0318] Step 3:

[0319] The device sends the converted text data, current location information obtained from GPS, and emotional state data resulting from emotion recognition to the server.

[0320] Input: Text data, current location information, emotional state data

[0321] Output: The transmitted dataset (text data, current location information, emotional state data)

[0322] Step 4:

[0323] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API). In addition to identifying the destination, it also analyzes the emotional state data to understand the user's psychological state. For example, the destination "Shinjuku Station" is identified from the text data, and "stress" is identified from the emotional state data.

[0324] Input: Received dataset (text data, current location information, emotional state data)

[0325] Output: Destination information, user's emotional state

[0326] Step 5:

[0327] Based on the destination information and current location, the server collects the necessary external data using weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API). At this stage, congestion status and weather data are obtained.

[0328] Input: Destination information, current location information

[0329] Output: Collected weather and traffic information

[0330] Step 6:

[0331] The server calculates the optimal route based on collected weather and traffic information, as well as the user's emotional state. For example, if the user is feeling stressed, the server will select a quiet route to avoid crowds and suggest a route that includes a relaxing park along the way.

[0332] Input: Weather information, traffic information, user's emotional state, current location information, destination information

[0333] Output: Optimal travel route data

[0334] Step 7:

[0335] The server generates specific route guidance data based on the emotion recognition results, such as "On your way to Shinjuku Station, it might be a good idea to take a short break in a quiet park."

[0336] Input: Optimal travel route data, user emotional state

[0337] Output: Specific route guidance data

[0338] Step 8:

[0339] The server sends the generated route guidance data to the device. The device then converts the received route guidance data into voice using a speech synthesis system (e.g., Google Text-to-Speech). For example, the device might play a voice message such as, "Please board the train arriving in two minutes and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[0340] Input: Specific route data

[0341] Output: Voice guidance

[0342] Step 9:

[0343] While traveling, the device sends its current location and surrounding conditions to the server in real time, and the server updates the guidance based on the new information. For example, if there is a traffic jam on the route, the server will immediately provide instructions such as, "The road to the right is clear, so please take that road."

[0344] Input: Real-time location data, surrounding situation data

[0345] Output: Updated navigation data

[0346] This is the specific flow of the program processing for this system. As a result, users can travel comfortably thanks to the emotion recognition function and receive optimal guidance in real time.

[0347] (Application example 2)

[0348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] Conventional mobility assistance systems often do not take into account the user's emotions or psychological state when providing route guidance to the user's destination, which means they are unable to reduce stress and anxiety during travel. Furthermore, they can sometimes have difficulty responding quickly to unexpected changes in traffic conditions. This makes it difficult for users to travel in a relaxed mood.

[0350] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and adjusting route guidance based on that emotional state, means for recommending relaxing places and scenery at specific points on the travel route, and means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on that location information. This reduces the user's stress and anxiety during travel and enables a comfortable travel experience.

[0351] "User" refers to a person who uses the system to receive travel guidance to a destination.

[0352] "Destination" refers to the location to which the user wishes to travel.

[0353] "Voice or text format" refers to one of the ways in which a user expresses their destination or travel needs to the system, such as voice input or text input.

[0354] "Speech recognition means" refers to technology that analyzes the voice input by the user and converts it into text data.

[0355] "Text data" refers to character string data converted from voice data by a voice recognition means.

[0356] "User's current location information" is information indicating the user's real-time geographical location, and is generally obtained using technology such as GPS.

[0357] "Server" refers to the central computing device that analyzes and processes data sent by users and generates optimal travel routes and guidance information.

[0358] "Natural language processing means" refers to technology that analyzes text data, understands its meaning, and identifies the user's intention (destination).

[0359] The "optimal travel route" refers to the optimal route for a user to reach a destination safely and efficiently.

[0360] "Weather information, traffic information, and facility information" refers to external data required for calculating travel routes, and refers to information about weather conditions, traffic congestion, and surrounding facilities.

[0361] "Speech synthesis means" refers to a technology that converts the generated route guidance data into a voice format and provides it to the user.

[0362] "Emotional state" is the result of analyzing the user's psychological state, and indicates a state such as stress, relaxation, or excitement.

[0363] "Relaxing places and scenery" refers to quiet places and beautiful scenery recommended to reduce the user's stress and anxiety.

[0364] This invention is a system that supports users' safe and efficient travel and provides optimal guidance according to the user's psychological state through emotion recognition. This system is installed in an in-vehicle display or in-vehicle infotainment system and is designed for self-driving vehicles.

[0365] Hardware and Software Configuration

[0366] In-vehicle system configuration

[0367] Users input their destination and travel requests in voice form using a microphone inside the car. The in-car system includes the following main components:

[0368] Microphone: A device for receiving a user's voice input.

[0369] Speech Recognition API: Analyzes user voice input and converts it into text data. For example, Google Cloud Speech-to-Text is used.

[0370] On-board GPS module: Obtains the user's current location information.

[0371] On-board CPU: Processes data and performs calculations, and communicates with the server via an internet connection.

[0372] In-car speaker: Provides voice-synthesized directions to the user.

[0373] Server Configuration

[0374] On the server side, the main components include:

[0375] Natural language processing engine: Analyzes the received text data and identifies the destination. For example, various natural language processing libraries are used.

[0376] Emotion recognition engine: Analyzes user voice input and video data from in-car cameras to identify the user's emotional state. For example, AWS Comprehend or IBM Watson can be used.

[0377] Data Collection API: Quickly retrieve weather, traffic, and facility information. Examples include OpenWeatherMap and Google Maps APIs.

[0378] Route calculation algorithm: Calculates the optimal travel path and generates route guidance based on the user's emotional state.

[0379] Speech synthesis engine: Converts the generated route guidance data into speech format, for example, using Amazon Polly or IBM Watson TTS.

[0380] Specific examples of processing steps

[0381] User Input and Sentiment Analysis

[0382] The user speaks to the in-car system, saying, "Please take me to Shinjuku Station." The in-car system's voice recognition API converts this into text data and sends it to the server. At the same time, the user's emotional data is collected from the in-car camera and additional sensors and analyzed by the emotion recognition engine.

[0383] Data analysis and route calculation on the server

[0384] The server analyzes the received text data (to Shinjuku Station) using a natural language processing engine to identify the destination. Next, it uses a data collection API to collect real-time weather and traffic information and calculates the optimal route based on this. If the user wants to relax, it could guide them to a scenic route that avoids crowds.

[0385] Providing audio guidance

[0386] Specific route guidance data generated by the server (e.g., "Turn right at the next stop, go through the park, then go straight") is converted into voice data by a speech synthesis engine and sent to the in-vehicle system. Voice guidance is provided to the user through the in-vehicle speaker.

[0387] Real-time monitoring and guidance updates

[0388] The server obtains the user's current location information in real time from the GPS module and updates the route accordingly. For example, if a sudden traffic jam occurs, the server calculates an alternative route and provides updated guidance to the user.

[0389] Prompt Sentence Examples

[0390] "When a user asks for a recommended route to Shinjuku Station, please suggest the optimal route taking into account real-time traffic information and emotional data. Also, please generate guidance that will help the user relax."

[0391] This system allows users to travel safely and efficiently, and its emotion recognition functionality ensures a stress-free travel experience.

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Step 1:

[0394] The user uses the in-car microphone to input their destination by voice. In this case, the user says, "Please take me to Shinjuku Station." The in-car system's voice recognition API (e.g., Google Cloud Speech-to-Text) captures this voice data and converts it into text data. The input data is voice data, and the output data is text data.

[0395] Step 2:

[0396] The converted text data and the user's current location information are sent from the in-vehicle system to the server. The in-vehicle system uses a GPS module to obtain real-time location information and transmits this location information at the same time. The input data is the text data and current location information, and the output data is the data sent to the server.

[0397] Step 3:

[0398] The server analyzes the received text data using a natural language processing engine and identifies the user's destination. For example, the destination "Shinjuku Station" is identified. The input data is text data, and the output data is destination information.

[0399] Step 4:

[0400] The server performs emotion recognition using the user's voice input and video data from the in-car camera. Using an emotion recognition engine (e.g., AWS Comprehend), it analyzes whether the user needs to relax, for example. The input data are the voice and video data, and the output data are the emotion recognition results.

[0401] Step 5:

[0402] The server obtains weather, traffic, and facility information using data collection APIs (e.g., OpenWeatherMap, Google Maps API). Then, it calculates the optimal travel route based on this information. The input data are destination information, current location information, emotion recognition results, and information from external APIs, and the output data is the optimal travel route.

[0403] Step 6:

[0404] The server generates guidance based on the user's emotional state, recommending relaxing places and scenery along the route. For example, it generates guidance such as "Turn right next time, pass through the park, then go straight." The input data are the optimal route and emotion recognition results, and the output data are specific route guidance.

[0405] Step 7:

[0406] The generated specific route guidance data is converted into speech by a speech synthesis engine (e.g., Amazon Polly). The server sends this speech data to the in-vehicle system. The input data is the route guidance data, and the output data is speech data.

[0407] Step 8:

[0408] The in-vehicle system receives the voice data and provides it to the user through the in-vehicle speaker. The user receives the guidance, "Turn right at the next stop, through the park, then go straight." The input data is the voice data, and the output data is the voice guidance to the user.

[0409] Step 9:

[0410] The server monitors the user's current location and traffic conditions in real time, and updates the route accordingly if new information becomes available. For example, if a sudden traffic jam occurs, the server calculates an alternative route and immediately provides updated guidance to the user. The input data is real-time location information and new traffic information, and the output data is the updated route guidance.

[0411] 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.

[0412] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0413] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0417] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] 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.

[0419] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0420] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0421] 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. 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.

[0422] 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.

[0423] 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 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.

[0424] 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.

[0425] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0426] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0427] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. The following describes a specific embodiment of the system.

[0428] Overview of the embodiment

[0429] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific directions based on this travel route are generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides the user with voice guidance. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated as appropriate based on the updated information.

[0430] Program processing details

[0431] Receiving User Input

[0432] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they might say, "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system.

[0433] Data analysis on the server

[0434] The device sends the text data and current location information obtained from GPS to the server. The server analyzes the received text data and identifies the destination by natural language processing. For example, the location "Shinjuku Station" is identified.

[0435] Gathering information and calculating the best route

[0436] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination information. This allows the optimal travel route to be calculated taking into account traffic congestion and weather. For example, a route such as "The optimal route from the current location to Shinjuku Station is to take the train, transfer at Station A, and get off at Station B" may be generated.

[0437] Providing audio guidance

[0438] The server then sends the generated route guidance data to the smartphone, which then converts the received data into voice using a speech synthesis system and provides directions to the user. For example, the smartphone provides voice instructions such as, "Take the train on line A that arrives in two minutes, get off at station B, and transfer to line C."

[0439] Real-time information updates

[0440] While traveling, the server monitors the user's current location and surrounding conditions in real time, and responds immediately if new information or emergencies arise. For example, if the traffic congestion situation on the road to the destination changes, the server will recalculate the optimal route in real time and provide new guidance. This allows the user to travel safely and efficiently based on the latest information.

[0441] The above is a description of a specific embodiment of the present invention. This eliminates the need for map reading skills, allowing users to safely reach their destination without constantly looking at their smartphone. Furthermore, by flexibly responding to changes in weather and traffic conditions, users can travel efficiently along the optimal route.

[0442] The processing flow will be explained below.

[0443] Step 1:

[0444] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[0445] Step 2:

[0446] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[0447] Step 3:

[0448] The device sends the converted text data and the current location information obtained from the GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station" and the current location data (latitude and longitude) are sent.

[0449] Step 4:

[0450] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[0451] Step 5:

[0452] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[0453] Step 6:

[0454] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[0455] Step 7:

[0456] The server then sends the generated route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0457] Step 8:

[0458] The device converts the route guidance data received from the server into speech using a speech synthesis system. The text data, such as "Take the train on line A arriving in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station," is converted into speech.

[0459] Step 9:

[0460] The device will then provide the user with directions converted into voice, such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0461] Step 10:

[0462] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road on the right is clear, so please proceed that way" are provided in real time.

[0463] The above are the specific processing steps of the system. This series of processes allows the user to reach their destination safely and efficiently through voice guidance.

[0464] Example 1

[0465] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0466] Currently, in order to reach their destination efficiently, users are required to have map reading skills and to be aware of traffic information in advance. In particular, it is difficult to flexibly respond to changing traffic and weather conditions in real time, and there are limited ways to receive appropriate guidance while traveling, which can cause additional stress and risk to users. Furthermore, when using voice input, the recognition accuracy and analysis capabilities are often insufficient, making it difficult to provide accurate guidance.

[0467] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0468] In this invention, the server includes means for inputting a destination and travel requests from a user in voice or text format, speech recognition means for converting the voice input into text data, means for transmitting the text data and the user's current location information to the server, natural language processing means, means for collecting weather information, traffic information, and location information to calculate an optimal travel route, and means for providing the user with navigation prompts generated via a generative AI model, thereby enabling the user to safely and efficiently receive optimal route guidance that is updated in real time.

[0469] A "destination" is a final destination to which a user wishes to travel.

[0470] "Travel requests" refer to specific requests about how the user wants to travel, such as arriving in the shortest time possible or taking a specific route.

[0471] "Means for input in voice or text form" refers to an interface that allows a user to input instructions or questions by voice or text using the terminal.

[0472] "Speech recognition means" refers to technology that analyzes a user's voice data and converts it into corresponding text data.

[0473] "Text data" refers to character string data converted by speech recognition means.

[0474] "Server" refers to a computer system that receives data sent from a user's terminal via a network and analyzes and processes it.

[0475] "Current location information" refers to real-time location data obtained by a user's device using location information technology such as GPS.

[0476] "Natural language processing means" refers to technology for analyzing text data, understanding natural human language, and identifying intent.

[0477] "Weather information" refers to weather data related to the user's current location and travel route.

[0478] "Traffic information" refers to movement-related dynamic data such as congestion on roads and public transportation and accident information.

[0479] "Location information" refers to geographic data such as landmarks and facilities related to a travel route.

[0480] "Means for calculating optimal travel routes" refers to algorithms or technologies that calculate routes that will allow users to reach their destinations efficiently based on collected information.

[0481] "Route guidance data" refers to data that includes specific travel instructions and route information provided to a user.

[0482] "Speech synthesis means" refers to a technology that converts text data into speech and provides the user with voice guidance.

[0483] "Real-time monitoring" refers to constantly monitoring the user's current location and surrounding conditions, and collecting and updating information.

[0484] "Generative AI Model" refers to the artificial intelligence technology used to generate the driving prompts that are provided to the user.

[0485] "Prompt sentence" refers to text data generated by a generative AI model and provided to the user, including travel routes and operation instructions.

[0486] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. Specific embodiments of this system are described in detail below.

[0487] User input

[0488] A user uses a mobile device such as a smartphone to input their destination and travel requests in voice or text format. For example, the user might say, "Please tell me the route to Shinjuku Station." This input is made via a user interface.

[0489] Voice recognition by device

[0490] The device uses a speech recognition system to convert voice input into text data. Specifically, it uses a speech recognition API to obtain the voice content as text data. The device stores this converted text data internally.

[0491] Sending data from the device to the server

[0492] The device sends text data and current location information to the server. The current location information is obtained using location information technology such as GPS. This data is sent to the server using a REST API.

[0493] Server-based natural language processing and analysis

[0494] The server analyzes the received text data and performs natural language processing to identify the user's destination. For natural language processing, for example, Apache OpenNLP or a cloud-based natural language processing API is used. This allows the server to identify a specific destination, such as "Shinjuku Station."

[0495] Server information collection

[0496] The server utilizes weather information APIs and traffic information APIs to collect the necessary information based on the user's current location and the specified destination. For example, OpenWeatherMap API and Google Maps API are used in this step. The server integrates and processes the data obtained from these APIs.

[0497] Optimal route calculation by server

[0498] The server calculates the optimal travel route based on the collected information. This calculation uses algorithms such as Dijkstra's algorithm and A algorithm. The server generates the calculated travel route as route guidance data. For example, it generates a detailed route such as "The optimal route from your current location to Shinjuku Station is to take the train, transfer at station A, and get off at station B."

[0499] Sending data from the server to the device

[0500] The server then sends the generated route guidance data to the device in JSON format using a REST API. The device then converts the data into speech using a speech synthesis system.

[0501] Voice guidance via terminal

[0502] The device uses a speech synthesis system, such as the Google Text-to-Speech API, to convert the received route guidance data into audio. For example, the device might provide instructions such as, "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C."

[0503] User movement and real-time updates

[0504] The user begins traveling by following the voice guidance from the device, and the smartphone continues to periodically send GPS location information to the server. The server monitors the user's current location and surrounding conditions in real time, and recalculates the optimal route based on new traffic and weather information. For example, if a traffic accident occurs along the way, it immediately calculates a detour route and sends that information to the device. The device then provides new route guidance to the user via voice.

[0505] Using generative AI models

[0506] The system uses a generative AI model to generate prompts for directions to be provided to the user. These prompts are designed to be intuitively understandable to the user. For example, a prompt might be, "Please tell me the route to Shinjuku Station."

[0507] In this way, users can safely and efficiently receive optimal route guidance that is updated in real time.By combining technologies such as voice recognition, natural language processing, real-time information collection and analysis, and voice synthesis, this system can significantly improve users' travel experience.

[0508] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0509] Step 1:

[0510] User input

[0511] The user uses the voice input function of the smartphone to input their destination and travel requests. Specifically, they input "Please tell me the route to Shinjuku Station." This voice data is sent to the device.

[0512] Step 2:

[0513] Voice recognition by device

[0514] The device uses a speech recognition system (for example, a speech recognition API) to convert the user's voice input into text data. The input is voice data, and the output is text data such as "Please tell me the route to Shinjuku Station." In this process, the voice waveform is converted into text.

[0515] Step 3:

[0516] Sending data from the device to the server

[0517] The device sends text data obtained by speech recognition and current location information obtained from GPS to the server. The input is the text data and current location information, and the output is a request containing this information sent to the server. This request is sent as JSON format data.

[0518] Step 4:

[0519] Server-based natural language processing and analysis

[0520] The server analyzes the received text data. Specifically, it uses a natural language processing algorithm (for example, Apache OpenNLP) to identify the user's destination. The input is the text data "Please tell me the route to Shinjuku Station," and the output is the identified destination, "Shinjuku Station." During this process, the text data is analyzed and semantic analysis is performed.

[0521] Step 5:

[0522] Server information collection

[0523] The server collects necessary data from weather information APIs and traffic information APIs based on the user's current location and the specified destination. The input is the current location and destination information, and the output is collected data such as weather information and traffic information. The server integrates this data to obtain real-time information useful for travel.

[0524] Step 6:

[0525] Optimal route calculation by server

[0526] The server calculates the optimal travel route based on the collected information. Specifically, it uses shortest path calculation algorithms such as Dijkstra's algorithm and A algorithm. The inputs are weather information, traffic information, and information on the current location and destination, and the output is the optimal travel route. For example, it calculates a specific route such as "The optimal route from the current location to Shinjuku Station is to take the train, change at station A, and get off at station B."

[0527] Step 7:

[0528] Sending data from the server to the device

[0529] The server sends the generated optimal route guidance data to the terminal. The input is the optimal travel route information, and the output is the JSON formatted guidance data sent to the terminal. This data includes specific route guidance and route information.

[0530] Step 8:

[0531] Device-based speech synthesis

[0532] The device uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the received route guidance data into speech. The input is route guidance data, and the output is voice guidance. For example, specific instructions such as "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C" are provided by voice.

[0533] Step 9:

[0534] User movement and real-time updates

[0535] The user starts moving according to the voice guidance from the device. During the movement, the device periodically sends GPS location information to the server. The input is the user's current location information, and the output is the real-time updated location information sent to the server.

[0536] Step 10:

[0537] Real-time updates from the server

[0538] The server periodically receives information about the user's current location and the latest weather and traffic information, and recalculates the optimal route. If there is a traffic accident or a sudden change in weather along the way, the server takes that information into account and sends a new optimal route to the user. The input is real-time information, and the output is updated guidance data for the optimal route. The device receives this data and again provides guidance using a voice synthesis system.

[0539] Through the above processing steps, the system provides users with an optimal travel route that is updated in real time, thereby improving the safety and efficiency of travel.

[0540] (Application example 1)

[0541] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0542] Modern self-driving vehicles often lack the ability to automatically correct routes based on traffic and weather information while allowing users to easily give voice instructions to their destinations and providing real-time guidance on optimal routes. This can compromise safety and efficiency during travel. A particular challenge is the inability to quickly respond to complex traffic situations and unpredictable weather changes.

[0543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0544] In this invention, the server includes a speech recognition means for converting speech input into text data, a natural language processing means for identifying the user's destination, and a means for collecting weather information, traffic information, and facility information for calculating the optimal travel route, thereby enabling the server to guide the user along the optimal route to the destination specified by speech and to modify the route in real time based on the latest information while traveling.

[0545] A "user" is a person who uses the system to give instructions for travel to a destination.

[0546] A "voice recognition means" is a device or system that converts a user's voice input into text data.

[0547] A "natural language processing means" is a device or algorithm that analyzes text data and identifies the user's destination.

[0548] "Weather information" is data about current and forecast weather.

[0549] "Traffic information" refers to data on road congestion, traffic accidents, construction work, etc.

[0550] "Facility information" is data on buildings and locations related to the destination and stops along the way.

[0551] The "optimal travel route" is the most efficient and safe route calculated based on weather information, traffic information, and facility information.

[0552] A "voice synthesis means" is a device or system that converts text data into voice and provides it to the user.

[0553] An "autonomous vehicle" is a motor vehicle that drives itself without driver intervention.

[0554] "Real-time monitoring" means constantly monitoring the user's current location and surrounding conditions as up-to-date information.

[0555] "Driving control" means managing the operation of an autonomous vehicle according to an optimal driving path.

[0556] This invention is a navigation system for autonomous vehicles that supports users' travel safely and efficiently. This system is composed of a combination of speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis technologies.

[0557] System Overview

[0558] Users use a mobile device such as a smartphone to input their destination and travel requests in voice or text format. The system operates as follows after receiving this input.

[0559] Hardware and software used

[0560] Hardware: smartphones, microphones, self-driving vehicles

[0561] Software: Python3, Google Speech Recognition API, Google Text-to-Speech (gTTS), Geopy library, OpenWeather API, HERE Traffic API

[0562] Data processing and calculation

[0563] 1. Speech Recognition:

[0564] The user inputs the destination by voice through the smartphone's microphone.

[0565] The Google Speech Recognition API in the smartphone converts the voice data into text data.

[0566] 2. Natural Language Processing:

[0567] The smartphone sends the acquired text data and the user's current location information (GPS data) to the server.

[0568] The server analyzes the text data using natural language processing means and identifies the user's destination.

[0569] 3. Real-time information collection and optimal route calculation:

[0570] Based on the destination and current location information, the server obtains weather information, traffic information, and facility information through various APIs.

[0571] Based on the collected information, the optimal travel route is calculated using libraries such as Geopy.

[0572] 4. Generate voice prompts:

[0573] The server sends the calculated optimal route information to the smartphone.

[0574] The smartphone uses Google Text-to-Speech (gTTS) to convert route information into voice guidance.

[0575] 5. Real-time information updates:

[0576] While moving, the server monitors the current location and surrounding conditions, updating the optimal route in real time.

[0577] If necessary, the server sends updated guidance data to the smartphone and provides new guidance via voice synthesis.

[0578] Specific examples

[0579] For example, if a user says, "Please tell me the route to Shibuya Station," this voice command is captured by the smartphone's microphone. The voice data is converted into text data (e.g., "To Shibuya Station") using the Google Speech Recognition API. The text data and current location information are then sent to the server, which uses natural language processing to identify the destination as "Shibuya Station."

[0580] Here, the server obtains weather and traffic information from the API and calculates the optimal route. For example, it takes into account the current traffic congestion and weather conditions and generates specific guidance such as "The optimal route from your current location to Shibuya Station is to take the train on Line A and get off at Station B."

[0581] This information is sent to the smartphone and provided to the user as voice guidance using Google Text-to-Speech (gTTS). While the user is moving, the server constantly monitors the current location and traffic conditions in real time, recalculating the optimal route based on the latest information as needed and providing new guidance.

[0582] Prompt Sentence Examples

[0583] "When a user says 'to Shibuya Station,' how can I calculate the optimal route to the destination and provide guidance incorporating real-time traffic and weather information? How can I implement this in Python?"

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] The user uses the smartphone's microphone to input their destination by voice. This voice data becomes the first input to the system. For example, they might say, "Please tell me the route to Shibuya Station."

[0587] Step 2:

[0588] The voice data acquired by the device is converted into text data using the Google Speech Recognition API. This is a process using speech recognition, where the input is raw voice data and the output is text data such as "To Shibuya Station."

[0589] Step 3:

[0590] The device sends the current location information obtained from GPS along with the text data to the server. The input here is the text data and GPS data, and the output is the data sent to the server.

[0591] Step 4:

[0592] The server analyzes the received text data using natural language processing and identifies the user's destination. The input is text data, and the output is the identified destination, such as "Shibuya Station."

[0593] Step 5:

[0594] The server uses APIs to collect weather, traffic, and facility information based on the user's current location and destination information. The input is GPS data and the identified destination, and the output is various real-time information.

[0595] Step 6:

[0596] The server calculates the optimal route using the collected real-time information. For example, using the Geopy library, the input is real-time information and current location / destination information, and the output is specific route guidance data.

[0597] Step 7:

[0598] The server then sends the generated route guidance data to the smartphone. The input is route guidance data, and the output is data transmission to the smartphone.

[0599] Step 8:

[0600] The device converts the received route guidance data into voice data using Google Text-to-Speech (gTTS) and provides it to the user as voice guidance. The input is text data for route guidance and the output is voice data, and this voice guidance is played back to the user.

[0601] Step 9:

[0602] While moving, the server monitors the current location and surrounding conditions in real time, and recalculates the optimal route based on new information as needed. The recalculated route is then sent back to the smartphone, which then provides the user with updated guidance via voice synthesis. Here, the input is real-time location information and surrounding information, and the output is updated route guidance data and its voice guidance.

[0603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0604] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[0605] Overview of the embodiment

[0606] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific route guidance based on this travel route is generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides voice guidance to the user. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated appropriately based on the updated information. In addition, an emotion recognition means is used to grasp the user's emotional state, and route guidance is provided according to that emotion.

[0607] Program processing details

[0608] Receiving User Input

[0609] Using a smartphone, a user inputs their destination and travel requests by voice or text. For example, they might input "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system. Furthermore, the emotion recognition engine also analyzes the user's emotions when inputting the voice.

[0610] Data analysis on the server

[0611] The device sends the text data, current location information obtained from GPS, and emotion recognition results to the server. The server analyzes the received text data and identifies the destination through natural language processing. For example, the location "Shinjuku Station" may be identified. The user's emotion data is also reflected appropriately at this stage.

[0612] Gathering information and calculating the best route

[0613] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination. This allows the optimal travel route to be calculated, taking into account traffic congestion and weather. For example, if the user is feeling stressed, a route that takes an uncrowded train or passes through a relaxing stopover point will be selected.

[0614] Generating specific directions based on emotions

[0615] The server uses the results of the emotion recognition engine to reflect the user's emotional state in the generated route guidance. For example, if the user is feeling stressed, it will add a recommendation to take a break in a quiet place. Specific route guidance data might include instructions such as, "There is a park on the way to your destination, so it would be a good idea to take a short break."

[0616] Providing audio guidance

[0617] The server then sends the generated specific route guidance data to the smartphone. The smartphone then converts the received data into voice using a speech synthesis system. Voice guidance such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break there" is provided.

[0618] Real-time information updates

[0619] While moving, the server monitors the current location and surrounding conditions in real time. If new information or an emergency occurs, it responds immediately. For example, if there is a traffic jam on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[0620] The above is a description of a specific embodiment of the present invention. This series of processes allows the user to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making travel more stress-free and comfortable.

[0621] The processing flow will be explained below.

[0622] Step 1:

[0623] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[0624] Step 2:

[0625] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[0626] Step 3:

[0627] The device uses an emotion recognition engine to analyze the user's emotions during voice input. Emotional data is generated based on the user's tone of voice, speaking rate, pauses, etc. At this time, emotional states such as "the user is nervous" are identified.

[0628] Step 4:

[0629] The device sends the converted text data, emotion recognition data, and current location information obtained from GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station," the current location data, and the emotion recognition result (e.g., "I'm nervous") are sent.

[0630] Step 5:

[0631] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[0632] Step 6:

[0633] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[0634] Step 7:

[0635] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[0636] Step 8:

[0637] The server also takes into account the results of the emotion recognition engine to generate directions that reflect the user's emotional state. For example, if the user is feeling nervous, the server will select a route that is relatively relaxing and includes quiet stops. In this way, a route such as "Take Line A to Station B, then transfer to Line C to reach Shinjuku Station. Stopping at a park along the way will help you relax" will be generated.

[0638] Step 9:

[0639] The server then sends the generated specific route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by a park on the way to relax."

[0640] Step 10:

[0641] The device then converts the route guidance data received from the server into speech using a speech synthesis system. The text data is converted into speech, such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. You can also stop by a park on the way to relax."

[0642] Step 11:

[0643] The device will then provide the user with directions converted into audio, such as "Take the train on line A, which arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by the park on the way to relax."

[0644] Step 12:

[0645] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[0646] These are the specific processing steps of the system. This series of processes allows users to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making the journey itself more stress-free and comfortable.

[0647] Example 2

[0648] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] Conventional travel guidance systems provide routes without considering the user's emotional state, which can cause stress for the user. Furthermore, many systems are unable to respond to real-time changes in the situation, making it difficult for users to deal with congestion and unexpected situations. Therefore, there was a need for the development of a system that provides guidance that takes the user's psychological state into consideration and always guides them to the optimal route.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0651] In this invention, the server includes a natural language processing means, a means for grasping an emotional state, and a means for calculating an optimal travel route, which enables the server to analyze a user's voice input, provide optimal route guidance according to the user's emotional state, and respond to changes in the situation in real time.

[0652] "Speech recognition means" is a technology for converting a user's voice input into text data.

[0653] "Natural language processing means" is a technology for analyzing text data to identify the user's intentions and destination.

[0654] "Emotional state" is data that represents the psychological state of the user, and indicates emotions such as stress or joy that the user is feeling.

[0655] "Speech synthesis means" is a technology for outputting text data as voice.

[0656] An "optimal travel route" is a route that is determined to be the most efficient and safe way to reach a destination.

[0657] "Weather information" is data about current and future weather conditions.

[0658] "Traffic information" refers to data on congestion and delays on roads and public transportation.

[0659] "Facility information" is data relating to facilities on the travel route, and includes, for example, location information of restaurants, parks, and the like.

[0660] "Real-time monitoring" refers to monitoring the user's current location and surrounding conditions in real time.

[0661] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[0662] Receiving User Input

[0663] Users use mobile devices such as smartphones to input their destinations and travel requests in voice or text format. When a user types, "Please tell me the route to Shinjuku Station," the smartphone's voice recognition system (e.g., Google Voice Recognition) converts the voice data into text data. At this time, an emotion recognition engine (e.g., Amazon Rekognition) also analyzes the voice data in parallel to determine the user's emotional state.

[0664] Sending current location information and text data

[0665] The device sends the text data converted by the speech recognition system, the current location information obtained from GPS, and the emotion recognition results to the server. This data includes the user's current location, destination, and emotional state.

[0666] Data analysis and destination identification

[0667] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API) to identify the user's destination. The server also analyzes the emotional data to understand the user's psychological state. For example, if "Shinjuku Station" is identified as the destination, it may be determined that the user is feeling stressed.

[0668] Gathering necessary information

[0669] Based on the specified destination and current location, the server uses weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API) to collect the necessary data, including existing congestion and weather data.

[0670] Calculating the best route

[0671] The server uses the collected information to calculate the optimal route, taking the user's emotional state into account in the process. For example, if the user is feeling stressed, the server can choose a quieter, more relaxing route to avoid crowds.

[0672] Generating directions based on emotions

[0673] The server generates specific route guidance that reflects the results of the emotion recognition engine. For example, it might generate a guidance sentence such as, "There is a park on the way to your destination, so it would be a good idea to take a short break." This guidance takes the user's psychological state into consideration.

[0674] Providing guidance data

[0675] The server sends the generated route guidance data to the device. The device converts the received data into speech using a speech synthesis system (e.g., Google Text-to-Speech) and provides the user with audio guidance. For example, the device might say, "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[0676] Real-time information updates

[0677] While moving, the device sends its current location and surrounding conditions to the server in real time. The server uses this information to update the guidance as needed. For example, if there is traffic congestion on the route, the instructions will be instantly changed to, "The road to the right is clear, so please take that route."

[0678] Examples and prompts

[0679] Example of a user entering "Route to Shinjuku Station" by voice:

[0680] 1. The user types into their smartphone, "Please tell me the route to Shinjuku Station."

[0681] 2. The voice recognition system converts this into text, and the emotion recognition engine identifies it as "stress."

[0682] 3. The device sends this data to the server.

[0683] 4. The server determines the destination, "Shinjuku Station," and the user's emotional state.

[0684] 5. The server uses weather information API and traffic information API to calculate the optimal route and select a route that passes through a quiet park, etc.

[0685] 6. The server generates guidance data such as "There is a park on the way to your destination, so it would be a good idea to take a short break there," and sends it to the device.

[0686] 7. The terminal uses a speech synthesis system to convert the information into speech and provide guidance to the user.

[0687] 8. The server monitors the latest situation and updates the information.

[0688] Prompt Sentence Examples

[0689] "Please explain the program process flow for a system in which a user uses a smartphone to ask for directions by voice, converts that voice into text data, and performs emotion recognition. Furthermore, please state the specific steps in order in which the server calculates the optimal route based on the current location and destination information, and provides voice guidance according to the user's emotional state."

[0690] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0691] Step 1:

[0692] The user uses a smartphone to input their destination and travel requests in voice or text format. For example, they might say, "Please tell me the route to Shinjuku Station." The input voice is captured as voice data through the smartphone's microphone.

[0693] Input: User voice input

[0694] Output: Audio data

[0695] Step 2:

[0696] The device analyzes the acquired voice data using a voice recognition system (e.g., Google Voice Recognition), converts the voice into text data, and then analyzes the emotional state of the user at the time of voice input using an emotion recognition engine (e.g., Amazon Rekognition).

[0697] Input: Audio data

[0698] Output: Text data and emotional state data

[0699] Step 3:

[0700] The device sends the converted text data, current location information obtained from GPS, and emotional state data resulting from emotion recognition to the server.

[0701] Input: Text data, current location information, emotional state data

[0702] Output: The transmitted dataset (text data, current location information, emotional state data)

[0703] Step 4:

[0704] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API). In addition to identifying the destination, it also analyzes the emotional state data to understand the user's psychological state. For example, the destination "Shinjuku Station" is identified from the text data, and "stress" is identified from the emotional state data.

[0705] Input: Received dataset (text data, current location information, emotional state data)

[0706] Output: Destination information, user's emotional state

[0707] Step 5:

[0708] Based on the destination information and current location, the server collects the necessary external data using weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API). At this stage, congestion status and weather data are obtained.

[0709] Input: Destination information, current location information

[0710] Output: Collected weather and traffic information

[0711] Step 6:

[0712] The server calculates the optimal route based on collected weather and traffic information, as well as the user's emotional state. For example, if the user is feeling stressed, the server will select a quiet route to avoid crowds and suggest a route that includes a relaxing park along the way.

[0713] Input: Weather information, traffic information, user's emotional state, current location information, destination information

[0714] Output: Optimal travel route data

[0715] Step 7:

[0716] The server generates specific route guidance data based on the emotion recognition results, such as "On your way to Shinjuku Station, it might be a good idea to take a short break in a quiet park."

[0717] Input: Optimal travel route data, user emotional state

[0718] Output: Specific route guidance data

[0719] Step 8:

[0720] The server sends the generated route guidance data to the device. The device then converts the received route guidance data into voice using a speech synthesis system (e.g., Google Text-to-Speech). For example, the device might play a voice message such as, "Please board the train arriving in two minutes and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[0721] Input: Specific route data

[0722] Output: Voice guidance

[0723] Step 9:

[0724] While traveling, the device sends its current location and surrounding conditions to the server in real time, and the server updates the guidance based on the new information. For example, if there is a traffic jam on the route, the server will immediately provide instructions such as, "The road to the right is clear, so please take that road."

[0725] Input: Real-time location data, surrounding situation data

[0726] Output: Updated navigation data

[0727] This is the specific flow of the program processing for this system. As a result, users can travel comfortably thanks to the emotion recognition function and receive optimal guidance in real time.

[0728] (Application example 2)

[0729] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] Conventional mobility assistance systems often do not take into account the user's emotions or psychological state when providing route guidance to the user's destination, which means they are unable to reduce stress and anxiety during travel. Furthermore, they can sometimes have difficulty responding quickly to unexpected changes in traffic conditions. This makes it difficult for users to travel in a relaxed mood.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and adjusting route guidance based on that emotional state, means for recommending relaxing places and scenery at specific points on the travel route, and means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on that location information. This reduces the user's stress and anxiety during travel and enables a comfortable travel experience.

[0732] "User" refers to a person who uses the system to receive travel guidance to a destination.

[0733] "Destination" refers to the location to which the user wishes to travel.

[0734] "Voice or text format" refers to one of the ways in which a user expresses their destination or travel needs to the system, such as voice input or text input.

[0735] "Speech recognition means" refers to technology that analyzes the voice input by the user and converts it into text data.

[0736] "Text data" refers to character string data converted from voice data by a voice recognition means.

[0737] "User's current location information" is information indicating the user's real-time geographical location, and is generally obtained using technology such as GPS.

[0738] "Server" refers to the central computing device that analyzes and processes data sent by users and generates optimal travel routes and guidance information.

[0739] "Natural language processing means" refers to technology that analyzes text data, understands its meaning, and identifies the user's intention (destination).

[0740] The "optimal travel route" refers to the optimal route for a user to reach a destination safely and efficiently.

[0741] "Weather information, traffic information, and facility information" refers to external data required for calculating travel routes, and refers to information about weather conditions, traffic congestion, and surrounding facilities.

[0742] "Speech synthesis means" refers to a technology that converts the generated route guidance data into a voice format and provides it to the user.

[0743] "Emotional state" is the result of analyzing the user's psychological state, and indicates a state such as stress, relaxation, or excitement.

[0744] "Relaxing places and scenery" refers to quiet places and beautiful scenery recommended to reduce the user's stress and anxiety.

[0745] This invention is a system that supports users' safe and efficient travel and provides optimal guidance according to the user's psychological state through emotion recognition. This system is installed in an in-vehicle display or in-vehicle infotainment system and is designed for self-driving vehicles.

[0746] Hardware and Software Configuration

[0747] In-vehicle system configuration

[0748] Users input their destination and travel requests in voice form using a microphone inside the car. The in-car system includes the following main components:

[0749] Microphone: A device for receiving a user's voice input.

[0750] Speech Recognition API: Analyzes user voice input and converts it into text data. For example, Google Cloud Speech-to-Text is used.

[0751] On-board GPS module: Obtains the user's current location information.

[0752] On-board CPU: Processes data and performs calculations, and communicates with the server via an internet connection.

[0753] In-car speaker: Provides voice-synthesized directions to the user.

[0754] Server Configuration

[0755] On the server side, the main components include:

[0756] Natural language processing engine: Analyzes the received text data and identifies the destination. For example, various natural language processing libraries are used.

[0757] Emotion recognition engine: Analyzes user voice input and video data from in-car cameras to identify the user's emotional state. For example, AWS Comprehend or IBM Watson can be used.

[0758] Data Collection API: Quickly retrieve weather, traffic, and facility information. Examples include OpenWeatherMap and Google Maps APIs.

[0759] Route calculation algorithm: Calculates the optimal travel path and generates route guidance based on the user's emotional state.

[0760] Speech synthesis engine: Converts the generated route guidance data into speech format, for example, using Amazon Polly or IBM Watson TTS.

[0761] Specific examples of processing steps

[0762] User Input and Sentiment Analysis

[0763] The user speaks to the in-car system, saying, "Please take me to Shinjuku Station." The in-car system's voice recognition API converts this into text data and sends it to the server. At the same time, the user's emotional data is collected from the in-car camera and additional sensors and analyzed by the emotion recognition engine.

[0764] Data analysis and route calculation on the server

[0765] The server analyzes the received text data (to Shinjuku Station) using a natural language processing engine to identify the destination. Next, it uses a data collection API to collect real-time weather and traffic information and calculates the optimal route based on this. If the user wants to relax, it could guide them to a scenic route that avoids crowds.

[0766] Providing audio guidance

[0767] Specific route guidance data generated by the server (e.g., "Turn right at the next stop, go through the park, then go straight") is converted into voice data by a speech synthesis engine and sent to the in-vehicle system. Voice guidance is provided to the user through the in-vehicle speaker.

[0768] Real-time monitoring and guidance updates

[0769] The server obtains the user's current location information in real time from the GPS module and updates the route accordingly. For example, if a sudden traffic jam occurs, the server calculates an alternative route and provides updated guidance to the user.

[0770] Prompt Sentence Examples

[0771] "When a user asks for a recommended route to Shinjuku Station, please suggest the optimal route taking into account real-time traffic information and emotional data. Also, please generate guidance that will help the user relax."

[0772] This system allows users to travel safely and efficiently, and its emotion recognition functionality ensures a stress-free travel experience.

[0773] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0774] Step 1:

[0775] The user uses the in-car microphone to input their destination by voice. In this case, the user says, "Please take me to Shinjuku Station." The in-car system's voice recognition API (e.g., Google Cloud Speech-to-Text) captures this voice data and converts it into text data. The input data is voice data, and the output data is text data.

[0776] Step 2:

[0777] The converted text data and the user's current location information are sent from the in-vehicle system to the server. The in-vehicle system uses a GPS module to obtain real-time location information and transmits this location information at the same time. The input data is the text data and current location information, and the output data is the data sent to the server.

[0778] Step 3:

[0779] The server analyzes the received text data using a natural language processing engine and identifies the user's destination. For example, the destination "Shinjuku Station" is identified. The input data is text data, and the output data is destination information.

[0780] Step 4:

[0781] The server performs emotion recognition using the user's voice input and video data from the in-car camera. Using an emotion recognition engine (e.g., AWS Comprehend), it analyzes whether the user needs to relax, for example. The input data are the voice and video data, and the output data are the emotion recognition results.

[0782] Step 5:

[0783] The server obtains weather, traffic, and facility information using data collection APIs (e.g., OpenWeatherMap, Google Maps API). Then, it calculates the optimal travel route based on this information. The input data are destination information, current location information, emotion recognition results, and information from external APIs, and the output data is the optimal travel route.

[0784] Step 6:

[0785] The server generates guidance based on the user's emotional state, recommending relaxing places and scenery along the route. For example, it generates guidance such as "Turn right next time, pass through the park, then go straight." The input data are the optimal route and emotion recognition results, and the output data are specific route guidance.

[0786] Step 7:

[0787] The generated specific route guidance data is converted into speech by a speech synthesis engine (e.g., Amazon Polly). The server sends this speech data to the in-vehicle system. The input data is the route guidance data, and the output data is speech data.

[0788] Step 8:

[0789] The in-vehicle system receives the voice data and provides it to the user through the in-vehicle speaker. The user receives the guidance, "Turn right at the next stop, through the park, then go straight." The input data is the voice data, and the output data is the voice guidance to the user.

[0790] Step 9:

[0791] The server monitors the user's current location and traffic conditions in real time, and updates the route accordingly if new information becomes available. For example, if a sudden traffic jam occurs, the server calculates an alternative route and immediately provides updated guidance to the user. The input data is real-time location information and new traffic information, and the output data is the updated route guidance.

[0792] 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.

[0793] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0794] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0795] [Third embodiment]

[0796] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0797] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0798] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0799] 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.

[0800] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0801] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0802] 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. 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.

[0803] 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.

[0804] 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 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.

[0805] 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.

[0806] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0807] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0808] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. The following describes a specific embodiment of the system.

[0809] Overview of the embodiment

[0810] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific directions based on this travel route are generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides the user with voice guidance. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated as appropriate based on the updated information.

[0811] Program processing details

[0812] Receiving User Input

[0813] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they might say, "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system.

[0814] Data analysis on the server

[0815] The device sends the text data and current location information obtained from GPS to the server. The server analyzes the received text data and identifies the destination by natural language processing. For example, the location "Shinjuku Station" is identified.

[0816] Gathering information and calculating the best route

[0817] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination information. This allows the optimal travel route to be calculated taking into account traffic congestion and weather. For example, a route such as "The optimal route from the current location to Shinjuku Station is to take the train, transfer at Station A, and get off at Station B" may be generated.

[0818] Providing audio guidance

[0819] The server then sends the generated route guidance data to the smartphone, which then converts the received data into voice using a speech synthesis system and provides directions to the user. For example, the smartphone provides voice instructions such as, "Take the train on line A that arrives in two minutes, get off at station B, and transfer to line C."

[0820] Real-time information updates

[0821] While traveling, the server monitors the user's current location and surrounding conditions in real time, and responds immediately if new information or emergencies arise. For example, if the traffic congestion situation on the road to the destination changes, the server will recalculate the optimal route in real time and provide new guidance. This allows the user to travel safely and efficiently based on the latest information.

[0822] The above is a description of a specific embodiment of the present invention. This eliminates the need for map reading skills, allowing users to safely reach their destination without constantly looking at their smartphone. Furthermore, by flexibly responding to changes in weather and traffic conditions, users can travel efficiently along the optimal route.

[0823] The processing flow will be explained below.

[0824] Step 1:

[0825] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[0826] Step 2:

[0827] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[0828] Step 3:

[0829] The device sends the converted text data and the current location information obtained from the GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station" and the current location data (latitude and longitude) are sent.

[0830] Step 4:

[0831] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[0832] Step 5:

[0833] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[0834] Step 6:

[0835] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[0836] Step 7:

[0837] The server then sends the generated route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0838] Step 8:

[0839] The device converts the route guidance data received from the server into speech using a speech synthesis system. The text data, such as "Take the train on line A arriving in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station," is converted into speech.

[0840] Step 9:

[0841] The device will then provide the user with directions converted into voice, such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[0842] Step 10:

[0843] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road on the right is clear, so please proceed that way" are provided in real time.

[0844] The above are the specific processing steps of the system. This series of processes allows the user to reach their destination safely and efficiently through voice guidance.

[0845] Example 1

[0846] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0847] Currently, in order to reach their destination efficiently, users are required to have map reading skills and to be aware of traffic information in advance. In particular, it is difficult to flexibly respond to changing traffic and weather conditions in real time, and there are limited ways to receive appropriate guidance while traveling, which can cause additional stress and risk to users. Furthermore, when using voice input, the recognition accuracy and analysis capabilities are often insufficient, making it difficult to provide accurate guidance.

[0848] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0849] In this invention, the server includes means for inputting a destination and travel requests from a user in voice or text format, speech recognition means for converting the voice input into text data, means for transmitting the text data and the user's current location information to the server, natural language processing means, means for collecting weather information, traffic information, and location information to calculate an optimal travel route, and means for providing the user with navigation prompts generated via a generative AI model, thereby enabling the user to safely and efficiently receive optimal route guidance that is updated in real time.

[0850] A "destination" is a final destination to which a user wishes to travel.

[0851] "Travel requests" refer to specific requests about how the user wants to travel, such as arriving in the shortest time possible or taking a specific route.

[0852] "Means for input in voice or text form" refers to an interface that allows a user to input instructions or questions by voice or text using the terminal.

[0853] "Speech recognition means" refers to technology that analyzes a user's voice data and converts it into corresponding text data.

[0854] "Text data" refers to character string data converted by speech recognition means.

[0855] "Server" refers to a computer system that receives data sent from a user's terminal via a network and analyzes and processes it.

[0856] "Current location information" refers to real-time location data obtained by a user's device using location information technology such as GPS.

[0857] "Natural language processing means" refers to technology for analyzing text data, understanding natural human language, and identifying intent.

[0858] "Weather information" refers to weather data related to the user's current location and travel route.

[0859] "Traffic information" refers to movement-related dynamic data such as congestion on roads and public transportation and accident information.

[0860] "Location information" refers to geographic data such as landmarks and facilities related to a travel route.

[0861] "Means for calculating optimal travel routes" refers to algorithms or technologies that calculate routes that will allow users to reach their destinations efficiently based on collected information.

[0862] "Route guidance data" refers to data that includes specific travel instructions and route information provided to a user.

[0863] "Speech synthesis means" refers to a technology that converts text data into speech and provides the user with voice guidance.

[0864] "Real-time monitoring" refers to constantly monitoring the user's current location and surrounding conditions, and collecting and updating information.

[0865] "Generative AI Model" refers to the artificial intelligence technology used to generate the driving prompts that are provided to the user.

[0866] "Prompt sentence" refers to text data generated by a generative AI model and provided to the user, including travel routes and operation instructions.

[0867] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. Specific embodiments of this system are described in detail below.

[0868] User input

[0869] A user uses a mobile device such as a smartphone to input their destination and travel requests in voice or text format. For example, the user might say, "Please tell me the route to Shinjuku Station." This input is made via a user interface.

[0870] Voice recognition by device

[0871] The device uses a speech recognition system to convert voice input into text data. Specifically, it uses a speech recognition API to obtain the voice content as text data. The device stores this converted text data internally.

[0872] Sending data from the device to the server

[0873] The device sends text data and current location information to the server. The current location information is obtained using location information technology such as GPS. This data is sent to the server using a REST API.

[0874] Server-based natural language processing and analysis

[0875] The server analyzes the received text data and performs natural language processing to identify the user's destination. For natural language processing, for example, Apache OpenNLP or a cloud-based natural language processing API is used. This allows the server to identify a specific destination, such as "Shinjuku Station."

[0876] Server information collection

[0877] The server utilizes weather information APIs and traffic information APIs to collect the necessary information based on the user's current location and the specified destination. For example, OpenWeatherMap API and Google Maps API are used in this step. The server integrates and processes the data obtained from these APIs.

[0878] Optimal route calculation by server

[0879] The server calculates the optimal travel route based on the collected information. This calculation uses algorithms such as Dijkstra's algorithm and A algorithm. The server generates the calculated travel route as route guidance data. For example, it generates a detailed route such as "The optimal route from your current location to Shinjuku Station is to take the train, transfer at station A, and get off at station B."

[0880] Sending data from the server to the device

[0881] The server then sends the generated route guidance data to the device in JSON format using a REST API. The device then converts the data into speech using a speech synthesis system.

[0882] Voice guidance via terminal

[0883] The device uses a speech synthesis system, such as the Google Text-to-Speech API, to convert the received route guidance data into audio. For example, the device might provide instructions such as, "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C."

[0884] User movement and real-time updates

[0885] The user begins traveling by following the voice guidance from the device, and the smartphone continues to periodically send GPS location information to the server. The server monitors the user's current location and surrounding conditions in real time, and recalculates the optimal route based on new traffic and weather information. For example, if a traffic accident occurs along the way, it immediately calculates a detour route and sends that information to the device. The device then provides new route guidance to the user via voice.

[0886] Using generative AI models

[0887] The system uses a generative AI model to generate prompts for directions to be provided to the user. These prompts are designed to be intuitively understandable to the user. For example, a prompt might be, "Please tell me the route to Shinjuku Station."

[0888] In this way, users can safely and efficiently receive optimal route guidance that is updated in real time.By combining technologies such as voice recognition, natural language processing, real-time information collection and analysis, and voice synthesis, this system can significantly improve users' travel experience.

[0889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0890] Step 1:

[0891] User input

[0892] The user uses the voice input function of the smartphone to input their destination and travel requests. Specifically, they input "Please tell me the route to Shinjuku Station." This voice data is sent to the device.

[0893] Step 2:

[0894] Voice recognition by device

[0895] The device uses a speech recognition system (for example, a speech recognition API) to convert the user's voice input into text data. The input is voice data, and the output is text data such as "Please tell me the route to Shinjuku Station." In this process, the voice waveform is converted into text.

[0896] Step 3:

[0897] Sending data from the device to the server

[0898] The device sends text data obtained by speech recognition and current location information obtained from GPS to the server. The input is the text data and current location information, and the output is a request containing this information sent to the server. This request is sent as JSON format data.

[0899] Step 4:

[0900] Server-based natural language processing and analysis

[0901] The server analyzes the received text data. Specifically, it uses a natural language processing algorithm (for example, Apache OpenNLP) to identify the user's destination. The input is the text data "Please tell me the route to Shinjuku Station," and the output is the identified destination, "Shinjuku Station." During this process, the text data is analyzed and semantic analysis is performed.

[0902] Step 5:

[0903] Server information collection

[0904] The server collects necessary data from weather information APIs and traffic information APIs based on the user's current location and the specified destination. The input is the current location and destination information, and the output is collected data such as weather information and traffic information. The server integrates this data to obtain real-time information useful for travel.

[0905] Step 6:

[0906] Optimal route calculation by server

[0907] The server calculates the optimal travel route based on the collected information. Specifically, it uses shortest path calculation algorithms such as Dijkstra's algorithm and A algorithm. The inputs are weather information, traffic information, and information on the current location and destination, and the output is the optimal travel route. For example, it calculates a specific route such as "The optimal route from the current location to Shinjuku Station is to take the train, change at station A, and get off at station B."

[0908] Step 7:

[0909] Sending data from the server to the device

[0910] The server sends the generated optimal route guidance data to the terminal. The input is the optimal travel route information, and the output is the JSON formatted guidance data sent to the terminal. This data includes specific route guidance and route information.

[0911] Step 8:

[0912] Device-based speech synthesis

[0913] The device uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the received route guidance data into speech. The input is route guidance data, and the output is voice guidance. For example, specific instructions such as "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C" are provided by voice.

[0914] Step 9:

[0915] User movement and real-time updates

[0916] The user starts moving according to the voice guidance from the device. During the movement, the device periodically sends GPS location information to the server. The input is the user's current location information, and the output is the real-time updated location information sent to the server.

[0917] Step 10:

[0918] Real-time updates from the server

[0919] The server periodically receives information about the user's current location and the latest weather and traffic information, and recalculates the optimal route. If there is a traffic accident or a sudden change in weather along the way, the server takes that information into account and sends a new optimal route to the user. The input is real-time information, and the output is updated guidance data for the optimal route. The device receives this data and again provides guidance using a voice synthesis system.

[0920] Through the above processing steps, the system provides users with an optimal travel route that is updated in real time, thereby improving the safety and efficiency of travel.

[0921] (Application example 1)

[0922] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0923] Modern self-driving vehicles often lack the ability to automatically correct routes based on traffic and weather information while allowing users to easily give voice instructions to their destinations and providing real-time guidance on optimal routes. This can compromise safety and efficiency during travel. A particular challenge is the inability to quickly respond to complex traffic situations and unpredictable weather changes.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0925] In this invention, the server includes a speech recognition means for converting speech input into text data, a natural language processing means for identifying the user's destination, and a means for collecting weather information, traffic information, and facility information for calculating the optimal travel route, thereby enabling the server to guide the user along the optimal route to the destination specified by speech and to modify the route in real time based on the latest information while traveling.

[0926] A "user" is a person who uses the system to give instructions for travel to a destination.

[0927] A "voice recognition means" is a device or system that converts a user's voice input into text data.

[0928] A "natural language processing means" is a device or algorithm that analyzes text data and identifies the user's destination.

[0929] "Weather information" is data about current and forecast weather.

[0930] "Traffic information" refers to data on road congestion, traffic accidents, construction work, etc.

[0931] "Facility information" is data on buildings and locations related to the destination and stops along the way.

[0932] The "optimal travel route" is the most efficient and safe route calculated based on weather information, traffic information, and facility information.

[0933] A "voice synthesis means" is a device or system that converts text data into voice and provides it to the user.

[0934] An "autonomous vehicle" is a motor vehicle that drives itself without driver intervention.

[0935] "Real-time monitoring" means constantly monitoring the user's current location and surrounding conditions as up-to-date information.

[0936] "Driving control" means managing the operation of an autonomous vehicle according to an optimal driving path.

[0937] This invention is a navigation system for autonomous vehicles that supports users' travel safely and efficiently. This system is composed of a combination of speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis technologies.

[0938] System Overview

[0939] Users use a mobile device such as a smartphone to input their destination and travel requests in voice or text format. The system operates as follows after receiving this input.

[0940] Hardware and software used

[0941] Hardware: smartphones, microphones, self-driving vehicles

[0942] Software: Python3, Google Speech Recognition API, Google Text-to-Speech (gTTS), Geopy library, OpenWeather API, HERE Traffic API

[0943] Data processing and calculation

[0944] 1. Speech Recognition:

[0945] The user inputs the destination by voice through the smartphone's microphone.

[0946] The Google Speech Recognition API in the smartphone converts the voice data into text data.

[0947] 2. Natural Language Processing:

[0948] The smartphone sends the acquired text data and the user's current location information (GPS data) to the server.

[0949] The server analyzes the text data using natural language processing means and identifies the user's destination.

[0950] 3. Real-time information collection and optimal route calculation:

[0951] Based on the destination and current location information, the server obtains weather information, traffic information, and facility information through various APIs.

[0952] Based on the collected information, the optimal travel route is calculated using libraries such as Geopy.

[0953] 4. Generate voice prompts:

[0954] The server sends the calculated optimal route information to the smartphone.

[0955] The smartphone uses Google Text-to-Speech (gTTS) to convert route information into voice guidance.

[0956] 5. Real-time information updates:

[0957] While moving, the server monitors the current location and surrounding conditions, updating the optimal route in real time.

[0958] If necessary, the server sends updated guidance data to the smartphone and provides new guidance via voice synthesis.

[0959] Specific examples

[0960] For example, if a user says, "Please tell me the route to Shibuya Station," this voice command is captured by the smartphone's microphone. The voice data is converted into text data (e.g., "To Shibuya Station") using the Google Speech Recognition API. The text data and current location information are then sent to the server, which uses natural language processing to identify the destination as "Shibuya Station."

[0961] Here, the server obtains weather and traffic information from the API and calculates the optimal route. For example, it takes into account the current traffic congestion and weather conditions and generates specific guidance such as "The optimal route from your current location to Shibuya Station is to take the train on Line A and get off at Station B."

[0962] This information is sent to the smartphone and provided to the user as voice guidance using Google Text-to-Speech (gTTS). While the user is moving, the server constantly monitors the current location and traffic conditions in real time, recalculating the optimal route based on the latest information as needed and providing new guidance.

[0963] Prompt Sentence Examples

[0964] "When a user says 'to Shibuya Station,' how can I calculate the optimal route to the destination and provide guidance incorporating real-time traffic and weather information? How can I implement this in Python?"

[0965] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0966] Step 1:

[0967] The user uses the smartphone's microphone to input their destination by voice. This voice data becomes the first input to the system. For example, they might say, "Please tell me the route to Shibuya Station."

[0968] Step 2:

[0969] The voice data acquired by the device is converted into text data using the Google Speech Recognition API. This is a process using speech recognition, where the input is raw voice data and the output is text data such as "To Shibuya Station."

[0970] Step 3:

[0971] The device sends the current location information obtained from GPS along with the text data to the server. The input here is the text data and GPS data, and the output is the data sent to the server.

[0972] Step 4:

[0973] The server analyzes the received text data using natural language processing and identifies the user's destination. The input is text data, and the output is the identified destination, such as "Shibuya Station."

[0974] Step 5:

[0975] The server uses APIs to collect weather, traffic, and facility information based on the user's current location and destination information. The input is GPS data and the identified destination, and the output is various real-time information.

[0976] Step 6:

[0977] The server calculates the optimal route using the collected real-time information. For example, using the Geopy library, the input is real-time information and current location / destination information, and the output is specific route guidance data.

[0978] Step 7:

[0979] The server then sends the generated route guidance data to the smartphone. The input is route guidance data, and the output is data transmission to the smartphone.

[0980] Step 8:

[0981] The device converts the received route guidance data into voice data using Google Text-to-Speech (gTTS) and provides it to the user as voice guidance. The input is text data for route guidance and the output is voice data, and this voice guidance is played back to the user.

[0982] Step 9:

[0983] While moving, the server monitors the current location and surrounding conditions in real time, and recalculates the optimal route based on new information as needed. The recalculated route is then sent back to the smartphone, which then provides the user with updated guidance via voice synthesis. Here, the input is real-time location information and surrounding information, and the output is updated route guidance data and its voice guidance.

[0984] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0985] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[0986] Overview of the embodiment

[0987] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific route guidance based on this travel route is generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides voice guidance to the user. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated appropriately based on the updated information. In addition, an emotion recognition means is used to grasp the user's emotional state, and route guidance is provided according to that emotion.

[0988] Program processing details

[0989] Receiving User Input

[0990] Using a smartphone, a user inputs their destination and travel requests by voice or text. For example, they might input "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system. Furthermore, the emotion recognition engine also analyzes the user's emotions when inputting the voice.

[0991] Data analysis on the server

[0992] The device sends the text data, current location information obtained from GPS, and emotion recognition results to the server. The server analyzes the received text data and identifies the destination through natural language processing. For example, the location "Shinjuku Station" may be identified. The user's emotion data is also reflected appropriately at this stage.

[0993] Gathering information and calculating the best route

[0994] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination. This allows the optimal travel route to be calculated, taking into account traffic congestion and weather. For example, if the user is feeling stressed, a route that takes an uncrowded train or passes through a relaxing stopover point will be selected.

[0995] Generating specific directions based on emotions

[0996] The server uses the results of the emotion recognition engine to reflect the user's emotional state in the generated route guidance. For example, if the user is feeling stressed, it will add a recommendation to take a break in a quiet place. Specific route guidance data might include instructions such as, "There is a park on the way to your destination, so it would be a good idea to take a short break."

[0997] Providing audio guidance

[0998] The server then sends the generated specific route guidance data to the smartphone. The smartphone then converts the received data into voice using a speech synthesis system. Voice guidance such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break there" is provided.

[0999] Real-time information updates

[1000] While moving, the server monitors the current location and surrounding conditions in real time. If new information or an emergency occurs, it responds immediately. For example, if there is a traffic jam on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[1001] The above is a description of a specific embodiment of the present invention. This series of processes allows the user to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making travel more stress-free and comfortable.

[1002] The processing flow will be explained below.

[1003] Step 1:

[1004] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[1005] Step 2:

[1006] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[1007] Step 3:

[1008] The device uses an emotion recognition engine to analyze the user's emotions during voice input. Emotional data is generated based on the user's tone of voice, speaking rate, pauses, etc. At this time, emotional states such as "the user is nervous" are identified.

[1009] Step 4:

[1010] The device sends the converted text data, emotion recognition data, and current location information obtained from GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station," the current location data, and the emotion recognition result (e.g., "I'm nervous") are sent.

[1011] Step 5:

[1012] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[1013] Step 6:

[1014] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[1015] Step 7:

[1016] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[1017] Step 8:

[1018] The server also takes into account the results of the emotion recognition engine to generate directions that reflect the user's emotional state. For example, if the user is feeling nervous, the server will select a route that is relatively relaxing and includes quiet stops. In this way, a route such as "Take Line A to Station B, then transfer to Line C to reach Shinjuku Station. Stopping at a park along the way will help you relax" will be generated.

[1019] Step 9:

[1020] The server then sends the generated specific route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by a park on the way to relax."

[1021] Step 10:

[1022] The device then converts the route guidance data received from the server into speech using a speech synthesis system. The text data is converted into speech, such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. You can also stop by a park on the way to relax."

[1023] Step 11:

[1024] The device will then provide the user with directions converted into audio, such as "Take the train on line A, which arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by the park on the way to relax."

[1025] Step 12:

[1026] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[1027] These are the specific processing steps of the system. This series of processes allows users to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making the journey itself more stress-free and comfortable.

[1028] Example 2

[1029] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1030] Conventional travel guidance systems provide routes without considering the user's emotional state, which can cause stress for the user. Furthermore, many systems are unable to respond to real-time changes in the situation, making it difficult for users to deal with congestion and unexpected situations. Therefore, there was a need for the development of a system that provides guidance that takes the user's psychological state into consideration and always guides them to the optimal route.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1032] In this invention, the server includes a natural language processing means, a means for grasping an emotional state, and a means for calculating an optimal travel route, which enables the server to analyze a user's voice input, provide optimal route guidance according to the user's emotional state, and respond to changes in the situation in real time.

[1033] "Speech recognition means" is a technology for converting a user's voice input into text data.

[1034] "Natural language processing means" is a technology for analyzing text data to identify the user's intentions and destination.

[1035] "Emotional state" is data that represents the psychological state of the user, and indicates emotions such as stress or joy that the user is feeling.

[1036] "Speech synthesis means" is a technology for outputting text data as voice.

[1037] An "optimal travel route" is a route that is determined to be the most efficient and safe way to reach a destination.

[1038] "Weather information" is data about current and future weather conditions.

[1039] "Traffic information" refers to data on congestion and delays on roads and public transportation.

[1040] "Facility information" is data relating to facilities on the travel route, and includes, for example, location information of restaurants, parks, and the like.

[1041] "Real-time monitoring" refers to monitoring the user's current location and surrounding conditions in real time.

[1042] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[1043] Receiving User Input

[1044] Users use mobile devices such as smartphones to input their destinations and travel requests in voice or text format. When a user types, "Please tell me the route to Shinjuku Station," the smartphone's voice recognition system (e.g., Google Voice Recognition) converts the voice data into text data. At this time, an emotion recognition engine (e.g., Amazon Rekognition) also analyzes the voice data in parallel to determine the user's emotional state.

[1045] Sending current location information and text data

[1046] The device sends the text data converted by the speech recognition system, the current location information obtained from GPS, and the emotion recognition results to the server. This data includes the user's current location, destination, and emotional state.

[1047] Data analysis and destination identification

[1048] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API) to identify the user's destination. The server also analyzes the emotional data to understand the user's psychological state. For example, if "Shinjuku Station" is identified as the destination, it may be determined that the user is feeling stressed.

[1049] Gathering necessary information

[1050] Based on the specified destination and current location, the server uses weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API) to collect the necessary data, including existing congestion and weather data.

[1051] Calculating the best route

[1052] The server uses the collected information to calculate the optimal route, taking the user's emotional state into account in the process. For example, if the user is feeling stressed, the server can choose a quieter, more relaxing route to avoid crowds.

[1053] Generating directions based on emotions

[1054] The server generates specific route guidance that reflects the results of the emotion recognition engine. For example, it might generate a guidance sentence such as, "There is a park on the way to your destination, so it would be a good idea to take a short break." This guidance takes the user's psychological state into consideration.

[1055] Providing guidance data

[1056] The server sends the generated route guidance data to the device. The device converts the received data into speech using a speech synthesis system (e.g., Google Text-to-Speech) and provides the user with audio guidance. For example, the device might say, "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[1057] Real-time information updates

[1058] While moving, the device sends its current location and surrounding conditions to the server in real time. The server uses this information to update the guidance as needed. For example, if there is traffic congestion on the route, the instructions will be instantly changed to, "The road to the right is clear, so please take that route."

[1059] Examples and prompts

[1060] Example of a user entering "Route to Shinjuku Station" by voice:

[1061] 1. The user types into their smartphone, "Please tell me the route to Shinjuku Station."

[1062] 2. The voice recognition system converts this into text, and the emotion recognition engine identifies it as "stress."

[1063] 3. The device sends this data to the server.

[1064] 4. The server determines the destination, "Shinjuku Station," and the user's emotional state.

[1065] 5. The server uses weather information API and traffic information API to calculate the optimal route and select a route that passes through a quiet park, etc.

[1066] 6. The server generates guidance data such as "There is a park on the way to your destination, so it would be a good idea to take a short break there," and sends it to the device.

[1067] 7. The terminal uses a speech synthesis system to convert the information into speech and provide guidance to the user.

[1068] 8. The server monitors the latest situation and updates the information.

[1069] Prompt Sentence Examples

[1070] "Please explain the program process flow for a system in which a user uses a smartphone to ask for directions by voice, converts that voice into text data, and performs emotion recognition. Furthermore, please state the specific steps in order in which the server calculates the optimal route based on the current location and destination information, and provides voice guidance according to the user's emotional state."

[1071] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1072] Step 1:

[1073] The user uses a smartphone to input their destination and travel requests in voice or text format. For example, they might say, "Please tell me the route to Shinjuku Station." The input voice is captured as voice data through the smartphone's microphone.

[1074] Input: User voice input

[1075] Output: Audio data

[1076] Step 2:

[1077] The device analyzes the acquired voice data using a voice recognition system (e.g., Google Voice Recognition), converts the voice into text data, and then analyzes the emotional state of the user at the time of voice input using an emotion recognition engine (e.g., Amazon Rekognition).

[1078] Input: Audio data

[1079] Output: Text data and emotional state data

[1080] Step 3:

[1081] The device sends the converted text data, current location information obtained from GPS, and emotional state data resulting from emotion recognition to the server.

[1082] Input: Text data, current location information, emotional state data

[1083] Output: The transmitted dataset (text data, current location information, emotional state data)

[1084] Step 4:

[1085] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API). In addition to identifying the destination, it also analyzes the emotional state data to understand the user's psychological state. For example, the destination "Shinjuku Station" is identified from the text data, and "stress" is identified from the emotional state data.

[1086] Input: Received dataset (text data, current location information, emotional state data)

[1087] Output: Destination information, user's emotional state

[1088] Step 5:

[1089] Based on the destination information and current location, the server collects the necessary external data using weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API). At this stage, congestion status and weather data are obtained.

[1090] Input: Destination information, current location information

[1091] Output: Collected weather and traffic information

[1092] Step 6:

[1093] The server calculates the optimal route based on collected weather and traffic information, as well as the user's emotional state. For example, if the user is feeling stressed, the server will select a quiet route to avoid crowds and suggest a route that includes a relaxing park along the way.

[1094] Input: Weather information, traffic information, user's emotional state, current location information, destination information

[1095] Output: Optimal travel route data

[1096] Step 7:

[1097] The server generates specific route guidance data based on the emotion recognition results, such as "On your way to Shinjuku Station, it might be a good idea to take a short break in a quiet park."

[1098] Input: Optimal travel route data, user emotional state

[1099] Output: Specific route guidance data

[1100] Step 8:

[1101] The server sends the generated route guidance data to the device. The device then converts the received route guidance data into voice using a speech synthesis system (e.g., Google Text-to-Speech). For example, the device might play a voice message such as, "Please board the train arriving in two minutes and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[1102] Input: Specific route data

[1103] Output: Voice guidance

[1104] Step 9:

[1105] While traveling, the device sends its current location and surrounding conditions to the server in real time, and the server updates the guidance based on the new information. For example, if there is a traffic jam on the route, the server will immediately provide instructions such as, "The road to the right is clear, so please take that road."

[1106] Input: Real-time location data, surrounding situation data

[1107] Output: Updated navigation data

[1108] This is the specific flow of the program processing for this system. As a result, users can travel comfortably thanks to the emotion recognition function and receive optimal guidance in real time.

[1109] (Application example 2)

[1110] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1111] Conventional mobility assistance systems often do not take into account the user's emotions or psychological state when providing route guidance to the user's destination, which means they are unable to reduce stress and anxiety during travel. Furthermore, they can sometimes have difficulty responding quickly to unexpected changes in traffic conditions. This makes it difficult for users to travel in a relaxed mood.

[1112] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and adjusting route guidance based on that emotional state, means for recommending relaxing places and scenery at specific points on the travel route, and means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on that location information. This reduces the user's stress and anxiety during travel and enables a comfortable travel experience.

[1113] "User" refers to a person who uses the system to receive travel guidance to a destination.

[1114] "Destination" refers to the location to which the user wishes to travel.

[1115] "Voice or text format" refers to one of the ways in which a user expresses their destination or travel needs to the system, such as voice input or text input.

[1116] "Speech recognition means" refers to technology that analyzes the voice input by the user and converts it into text data.

[1117] "Text data" refers to character string data converted from voice data by a voice recognition means.

[1118] "User's current location information" is information indicating the user's real-time geographical location, and is generally obtained using technology such as GPS.

[1119] "Server" refers to the central computing device that analyzes and processes data sent by users and generates optimal travel routes and guidance information.

[1120] "Natural language processing means" refers to technology that analyzes text data, understands its meaning, and identifies the user's intention (destination).

[1121] The "optimal travel route" refers to the optimal route for a user to reach a destination safely and efficiently.

[1122] "Weather information, traffic information, and facility information" refers to external data required for calculating travel routes, and refers to information about weather conditions, traffic congestion, and surrounding facilities.

[1123] "Speech synthesis means" refers to a technology that converts the generated route guidance data into a voice format and provides it to the user.

[1124] "Emotional state" is the result of analyzing the user's psychological state, and indicates a state such as stress, relaxation, or excitement.

[1125] "Relaxing places and scenery" refers to quiet places and beautiful scenery recommended to reduce the user's stress and anxiety.

[1126] This invention is a system that supports users' safe and efficient travel and provides optimal guidance according to the user's psychological state through emotion recognition. This system is installed in an in-vehicle display or in-vehicle infotainment system and is designed for self-driving vehicles.

[1127] Hardware and Software Configuration

[1128] In-vehicle system configuration

[1129] Users input their destination and travel requests in voice form using a microphone inside the car. The in-car system includes the following main components:

[1130] Microphone: A device for receiving a user's voice input.

[1131] Speech Recognition API: Analyzes user voice input and converts it into text data. For example, Google Cloud Speech-to-Text is used.

[1132] On-board GPS module: Obtains the user's current location information.

[1133] On-board CPU: Processes data and performs calculations, and communicates with the server via an internet connection.

[1134] In-car speaker: Provides voice-synthesized directions to the user.

[1135] Server Configuration

[1136] On the server side, the main components include:

[1137] Natural language processing engine: Analyzes the received text data and identifies the destination. For example, various natural language processing libraries are used.

[1138] Emotion recognition engine: Analyzes user voice input and video data from in-car cameras to identify the user's emotional state. For example, AWS Comprehend or IBM Watson can be used.

[1139] Data Collection API: Quickly retrieve weather, traffic, and facility information. Examples include OpenWeatherMap and Google Maps APIs.

[1140] Route calculation algorithm: Calculates the optimal travel path and generates route guidance based on the user's emotional state.

[1141] Speech synthesis engine: Converts the generated route guidance data into speech format, for example, using Amazon Polly or IBM Watson TTS.

[1142] Specific examples of processing steps

[1143] User Input and Sentiment Analysis

[1144] The user speaks to the in-car system, saying, "Please take me to Shinjuku Station." The in-car system's voice recognition API converts this into text data and sends it to the server. At the same time, the user's emotional data is collected from the in-car camera and additional sensors and analyzed by the emotion recognition engine.

[1145] Data analysis and route calculation on the server

[1146] The server analyzes the received text data (to Shinjuku Station) using a natural language processing engine to identify the destination. Next, it uses a data collection API to collect real-time weather and traffic information and calculates the optimal route based on this. If the user wants to relax, it could guide them to a scenic route that avoids crowds.

[1147] Providing audio guidance

[1148] Specific route guidance data generated by the server (e.g., "Turn right at the next stop, go through the park, then go straight") is converted into voice data by a speech synthesis engine and sent to the in-vehicle system. Voice guidance is provided to the user through the in-vehicle speaker.

[1149] Real-time monitoring and guidance updates

[1150] The server obtains the user's current location information in real time from the GPS module and updates the route accordingly. For example, if a sudden traffic jam occurs, the server calculates an alternative route and provides updated guidance to the user.

[1151] Prompt Sentence Examples

[1152] "When a user asks for a recommended route to Shinjuku Station, please suggest the optimal route taking into account real-time traffic information and emotional data. Also, please generate guidance that will help the user relax."

[1153] This system allows users to travel safely and efficiently, and its emotion recognition functionality ensures a stress-free travel experience.

[1154] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1155] Step 1:

[1156] The user uses the in-car microphone to input their destination by voice. In this case, the user says, "Please take me to Shinjuku Station." The in-car system's voice recognition API (e.g., Google Cloud Speech-to-Text) captures this voice data and converts it into text data. The input data is voice data, and the output data is text data.

[1157] Step 2:

[1158] The converted text data and the user's current location information are sent from the in-vehicle system to the server. The in-vehicle system uses a GPS module to obtain real-time location information and transmits this location information at the same time. The input data is the text data and current location information, and the output data is the data sent to the server.

[1159] Step 3:

[1160] The server analyzes the received text data using a natural language processing engine and identifies the user's destination. For example, the destination "Shinjuku Station" is identified. The input data is text data, and the output data is destination information.

[1161] Step 4:

[1162] The server performs emotion recognition using the user's voice input and video data from the in-car camera. Using an emotion recognition engine (e.g., AWS Comprehend), it analyzes whether the user needs to relax, for example. The input data are the voice and video data, and the output data are the emotion recognition results.

[1163] Step 5:

[1164] The server obtains weather, traffic, and facility information using data collection APIs (e.g., OpenWeatherMap, Google Maps API). Then, it calculates the optimal travel route based on this information. The input data are destination information, current location information, emotion recognition results, and information from external APIs, and the output data is the optimal travel route.

[1165] Step 6:

[1166] The server generates guidance based on the user's emotional state, recommending relaxing places and scenery along the route. For example, it generates guidance such as "Turn right next time, pass through the park, then go straight." The input data are the optimal route and emotion recognition results, and the output data are specific route guidance.

[1167] Step 7:

[1168] The generated specific route guidance data is converted into speech by a speech synthesis engine (e.g., Amazon Polly). The server sends this speech data to the in-vehicle system. The input data is the route guidance data, and the output data is speech data.

[1169] Step 8:

[1170] The in-vehicle system receives the voice data and provides it to the user through the in-vehicle speaker. The user receives the guidance, "Turn right at the next stop, through the park, then go straight." The input data is the voice data, and the output data is the voice guidance to the user.

[1171] Step 9:

[1172] The server monitors the user's current location and traffic conditions in real time, and updates the route accordingly if new information becomes available. For example, if a sudden traffic jam occurs, the server calculates an alternative route and immediately provides updated guidance to the user. The input data is real-time location information and new traffic information, and the output data is the updated route guidance.

[1173] 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.

[1174] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1175] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1176] [Fourth embodiment]

[1177] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1178] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1179] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1180] 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.

[1181] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1182] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1183] 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. 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.

[1184] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1185] 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.

[1186] 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 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.

[1187] 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.

[1188] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1189] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1190] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. The following describes a specific embodiment of the system.

[1191] Overview of the embodiment

[1192] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific directions based on this travel route are generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides the user with voice guidance. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated as appropriate based on the updated information.

[1193] Program processing details

[1194] Receiving User Input

[1195] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they might say, "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system.

[1196] Data analysis on the server

[1197] The device sends the text data and current location information obtained from GPS to the server. The server analyzes the received text data and identifies the destination by natural language processing. For example, the location "Shinjuku Station" is identified.

[1198] Gathering information and calculating the best route

[1199] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination information. This allows the optimal travel route to be calculated taking into account traffic congestion and weather. For example, a route such as "The optimal route from the current location to Shinjuku Station is to take the train, transfer at Station A, and get off at Station B" may be generated.

[1200] Providing audio guidance

[1201] The server then sends the generated route guidance data to the smartphone, which then converts the received data into voice using a speech synthesis system and provides directions to the user. For example, the smartphone provides voice instructions such as, "Take the train on line A that arrives in two minutes, get off at station B, and transfer to line C."

[1202] Real-time information updates

[1203] While traveling, the server monitors the user's current location and surrounding conditions in real time, and responds immediately if new information or emergencies arise. For example, if the traffic congestion situation on the road to the destination changes, the server will recalculate the optimal route in real time and provide new guidance. This allows the user to travel safely and efficiently based on the latest information.

[1204] The above is a description of a specific embodiment of the present invention. This eliminates the need for map reading skills, allowing users to safely reach their destination without constantly looking at their smartphone. Furthermore, by flexibly responding to changes in weather and traffic conditions, users can travel efficiently along the optimal route.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[1208] Step 2:

[1209] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[1210] Step 3:

[1211] The device sends the converted text data and the current location information obtained from the GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station" and the current location data (latitude and longitude) are sent.

[1212] Step 4:

[1213] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[1214] Step 5:

[1215] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[1216] Step 6:

[1217] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[1218] Step 7:

[1219] The server then sends the generated route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[1220] Step 8:

[1221] The device converts the route guidance data received from the server into speech using a speech synthesis system. The text data, such as "Take the train on line A arriving in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station," is converted into speech.

[1222] Step 9:

[1223] The device will then provide the user with directions converted into voice, such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station."

[1224] Step 10:

[1225] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road on the right is clear, so please proceed that way" are provided in real time.

[1226] The above are the specific processing steps of the system. This series of processes allows the user to reach their destination safely and efficiently through voice guidance.

[1227] Example 1

[1228] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1229] Currently, in order to reach their destination efficiently, users are required to have map reading skills and to be aware of traffic information in advance. In particular, it is difficult to flexibly respond to changing traffic and weather conditions in real time, and there are limited ways to receive appropriate guidance while traveling, which can cause additional stress and risk to users. Furthermore, when using voice input, the recognition accuracy and analysis capabilities are often insufficient, making it difficult to provide accurate guidance.

[1230] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1231] In this invention, the server includes means for inputting a destination and travel requests from a user in voice or text format, speech recognition means for converting the voice input into text data, means for transmitting the text data and the user's current location information to the server, natural language processing means, means for collecting weather information, traffic information, and location information to calculate an optimal travel route, and means for providing the user with navigation prompts generated via a generative AI model, thereby enabling the user to safely and efficiently receive optimal route guidance that is updated in real time.

[1232] A "destination" is a final destination to which a user wishes to travel.

[1233] "Travel requests" refer to specific requests about how the user wants to travel, such as arriving in the shortest time possible or taking a specific route.

[1234] "Means for input in voice or text form" refers to an interface that allows a user to input instructions or questions by voice or text using the terminal.

[1235] "Speech recognition means" refers to technology that analyzes a user's voice data and converts it into corresponding text data.

[1236] "Text data" refers to character string data converted by speech recognition means.

[1237] "Server" refers to a computer system that receives data sent from a user's terminal via a network and analyzes and processes it.

[1238] "Current location information" refers to real-time location data obtained by a user's device using location information technology such as GPS.

[1239] "Natural language processing means" refers to technology for analyzing text data, understanding natural human language, and identifying intent.

[1240] "Weather information" refers to weather data related to the user's current location and travel route.

[1241] "Traffic information" refers to movement-related dynamic data such as congestion on roads and public transportation and accident information.

[1242] "Location information" refers to geographic data such as landmarks and facilities related to a travel route.

[1243] "Means for calculating optimal travel routes" refers to algorithms or technologies that calculate routes that will allow users to reach their destinations efficiently based on collected information.

[1244] "Route guidance data" refers to data that includes specific travel instructions and route information provided to a user.

[1245] "Speech synthesis means" refers to a technology that converts text data into speech and provides the user with voice guidance.

[1246] "Real-time monitoring" refers to constantly monitoring the user's current location and surrounding conditions, and collecting and updating information.

[1247] "Generative AI Model" refers to the artificial intelligence technology used to generate the driving prompts that are provided to the user.

[1248] "Prompt sentence" refers to text data generated by a generative AI model and provided to the user, including travel routes and operation instructions.

[1249] The present invention is a system for supporting user mobility safely and efficiently. This system is configured by combining multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis. Specific embodiments of this system are described in detail below.

[1250] User input

[1251] A user uses a mobile device such as a smartphone to input their destination and travel requests in voice or text format. For example, the user might say, "Please tell me the route to Shinjuku Station." This input is made via a user interface.

[1252] Voice recognition by device

[1253] The device uses a speech recognition system to convert voice input into text data. Specifically, it uses a speech recognition API to obtain the voice content as text data. The device stores this converted text data internally.

[1254] Sending data from the device to the server

[1255] The device sends text data and current location information to the server. The current location information is obtained using location information technology such as GPS. This data is sent to the server using a REST API.

[1256] Server-based natural language processing and analysis

[1257] The server analyzes the received text data and performs natural language processing to identify the user's destination. For natural language processing, for example, Apache OpenNLP or a cloud-based natural language processing API is used. This allows the server to identify a specific destination, such as "Shinjuku Station."

[1258] Server information collection

[1259] The server utilizes weather information APIs and traffic information APIs to collect the necessary information based on the user's current location and the specified destination. For example, OpenWeatherMap API and Google Maps API are used in this step. The server integrates and processes the data obtained from these APIs.

[1260] Optimal route calculation by server

[1261] The server calculates the optimal travel route based on the collected information. This calculation uses algorithms such as Dijkstra's algorithm and A algorithm. The server generates the calculated travel route as route guidance data. For example, it generates a detailed route such as "The optimal route from your current location to Shinjuku Station is to take the train, transfer at station A, and get off at station B."

[1262] Sending data from the server to the device

[1263] The server then sends the generated route guidance data to the device in JSON format using a REST API. The device then converts the data into speech using a speech synthesis system.

[1264] Voice guidance via terminal

[1265] The device uses a speech synthesis system, such as the Google Text-to-Speech API, to convert the received route guidance data into audio. For example, the device might provide instructions such as, "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C."

[1266] User movement and real-time updates

[1267] The user begins traveling by following the voice guidance from the device, and the smartphone continues to periodically send GPS location information to the server. The server monitors the user's current location and surrounding conditions in real time, and recalculates the optimal route based on new traffic and weather information. For example, if a traffic accident occurs along the way, it immediately calculates a detour route and sends that information to the device. The device then provides new route guidance to the user via voice.

[1268] Using generative AI models

[1269] The system uses a generative AI model to generate prompts for directions to be provided to the user. These prompts are designed to be intuitively understandable to the user. For example, a prompt might be, "Please tell me the route to Shinjuku Station."

[1270] In this way, users can safely and efficiently receive optimal route guidance that is updated in real time.By combining technologies such as voice recognition, natural language processing, real-time information collection and analysis, and voice synthesis, this system can significantly improve users' travel experience.

[1271] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] User input

[1274] The user uses the voice input function of the smartphone to input their destination and travel requests. Specifically, they input "Please tell me the route to Shinjuku Station." This voice data is sent to the device.

[1275] Step 2:

[1276] Voice recognition by device

[1277] The device uses a speech recognition system (for example, a speech recognition API) to convert the user's voice input into text data. The input is voice data, and the output is text data such as "Please tell me the route to Shinjuku Station." In this process, the voice waveform is converted into text.

[1278] Step 3:

[1279] Sending data from the device to the server

[1280] The device sends text data obtained by speech recognition and current location information obtained from GPS to the server. The input is the text data and current location information, and the output is a request containing this information sent to the server. This request is sent as JSON format data.

[1281] Step 4:

[1282] Server-based natural language processing and analysis

[1283] The server analyzes the received text data. Specifically, it uses a natural language processing algorithm (for example, Apache OpenNLP) to identify the user's destination. The input is the text data "Please tell me the route to Shinjuku Station," and the output is the identified destination, "Shinjuku Station." During this process, the text data is analyzed and semantic analysis is performed.

[1284] Step 5:

[1285] Server information collection

[1286] The server collects necessary data from weather information APIs and traffic information APIs based on the user's current location and the specified destination. The input is the current location and destination information, and the output is collected data such as weather information and traffic information. The server integrates this data to obtain real-time information useful for travel.

[1287] Step 6:

[1288] Optimal route calculation by server

[1289] The server calculates the optimal travel route based on the collected information. Specifically, it uses shortest path calculation algorithms such as Dijkstra's algorithm and A algorithm. The inputs are weather information, traffic information, and information on the current location and destination, and the output is the optimal travel route. For example, it calculates a specific route such as "The optimal route from the current location to Shinjuku Station is to take the train, change at station A, and get off at station B."

[1290] Step 7:

[1291] Sending data from the server to the device

[1292] The server sends the generated optimal route guidance data to the terminal. The input is the optimal travel route information, and the output is the JSON formatted guidance data sent to the terminal. This data includes specific route guidance and route information.

[1293] Step 8:

[1294] Device-based speech synthesis

[1295] The device uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the received route guidance data into speech. The input is route guidance data, and the output is voice guidance. For example, specific instructions such as "Take the train on line A, which arrives in two minutes, get off at station B, and transfer to line C" are provided by voice.

[1296] Step 9:

[1297] User movement and real-time updates

[1298] The user starts moving according to the voice guidance from the device. During the movement, the device periodically sends GPS location information to the server. The input is the user's current location information, and the output is the real-time updated location information sent to the server.

[1299] Step 10:

[1300] Real-time updates from the server

[1301] The server periodically receives information about the user's current location and the latest weather and traffic information, and recalculates the optimal route. If there is a traffic accident or a sudden change in weather along the way, the server takes that information into account and sends a new optimal route to the user. The input is real-time information, and the output is updated guidance data for the optimal route. The device receives this data and again provides guidance using a voice synthesis system.

[1302] Through the above processing steps, the system provides users with an optimal travel route that is updated in real time, thereby improving the safety and efficiency of travel.

[1303] (Application example 1)

[1304] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1305] Modern self-driving vehicles often lack the ability to automatically correct routes based on traffic and weather information while allowing users to easily give voice instructions to their destinations and providing real-time guidance on optimal routes. This can compromise safety and efficiency during travel. A particular challenge is the inability to quickly respond to complex traffic situations and unpredictable weather changes.

[1306] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1307] In this invention, the server includes a speech recognition means for converting speech input into text data, a natural language processing means for identifying the user's destination, and a means for collecting weather information, traffic information, and facility information for calculating the optimal travel route, thereby enabling the server to guide the user along the optimal route to the destination specified by speech and to modify the route in real time based on the latest information while traveling.

[1308] A "user" is a person who uses the system to give instructions for travel to a destination.

[1309] A "voice recognition means" is a device or system that converts a user's voice input into text data.

[1310] A "natural language processing means" is a device or algorithm that analyzes text data and identifies the user's destination.

[1311] "Weather information" is data about current and forecast weather.

[1312] "Traffic information" refers to data on road congestion, traffic accidents, construction work, etc.

[1313] "Facility information" is data on buildings and locations related to the destination and stops along the way.

[1314] The "optimal travel route" is the most efficient and safe route calculated based on weather information, traffic information, and facility information.

[1315] A "voice synthesis means" is a device or system that converts text data into voice and provides it to the user.

[1316] An "autonomous vehicle" is a motor vehicle that drives itself without driver intervention.

[1317] "Real-time monitoring" means constantly monitoring the user's current location and surrounding conditions as up-to-date information.

[1318] "Driving control" means managing the operation of an autonomous vehicle according to an optimal driving path.

[1319] This invention is a navigation system for autonomous vehicles that supports users' travel safely and efficiently. This system is composed of a combination of speech recognition, natural language processing, real-time information collection and analysis, and speech synthesis technologies.

[1320] System Overview

[1321] Users use a mobile device such as a smartphone to input their destination and travel requests in voice or text format. The system operates as follows after receiving this input.

[1322] Hardware and software used

[1323] Hardware: smartphones, microphones, self-driving vehicles

[1324] Software: Python3, Google Speech Recognition API, Google Text-to-Speech (gTTS), Geopy library, OpenWeather API, HERE Traffic API

[1325] Data processing and calculation

[1326] 1. Speech Recognition:

[1327] The user inputs the destination by voice through the smartphone's microphone.

[1328] The Google Speech Recognition API in the smartphone converts the voice data into text data.

[1329] 2. Natural Language Processing:

[1330] The smartphone sends the acquired text data and the user's current location information (GPS data) to the server.

[1331] The server analyzes the text data using natural language processing means and identifies the user's destination.

[1332] 3. Real-time information collection and optimal route calculation:

[1333] Based on the destination and current location information, the server obtains weather information, traffic information, and facility information through various APIs.

[1334] Based on the collected information, the optimal travel route is calculated using libraries such as Geopy.

[1335] 4. Generate voice prompts:

[1336] The server sends the calculated optimal route information to the smartphone.

[1337] The smartphone uses Google Text-to-Speech (gTTS) to convert route information into voice guidance.

[1338] 5. Real-time information updates:

[1339] While moving, the server monitors the current location and surrounding conditions, updating the optimal route in real time.

[1340] If necessary, the server sends updated guidance data to the smartphone and provides new guidance via voice synthesis.

[1341] Specific examples

[1342] For example, if a user says, "Please tell me the route to Shibuya Station," this voice command is captured by the smartphone's microphone. The voice data is converted into text data (e.g., "To Shibuya Station") using the Google Speech Recognition API. The text data and current location information are then sent to the server, which uses natural language processing to identify the destination as "Shibuya Station."

[1343] Here, the server obtains weather and traffic information from the API and calculates the optimal route. For example, it takes into account the current traffic congestion and weather conditions and generates specific guidance such as "The optimal route from your current location to Shibuya Station is to take the train on Line A and get off at Station B."

[1344] This information is sent to the smartphone and provided to the user as voice guidance using Google Text-to-Speech (gTTS). While the user is moving, the server constantly monitors the current location and traffic conditions in real time, recalculating the optimal route based on the latest information as needed and providing new guidance.

[1345] Prompt Sentence Examples

[1346] "When a user says 'to Shibuya Station,' how can I calculate the optimal route to the destination and provide guidance incorporating real-time traffic and weather information? How can I implement this in Python?"

[1347] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1348] Step 1:

[1349] The user uses the smartphone's microphone to input their destination by voice. This voice data becomes the first input to the system. For example, they might say, "Please tell me the route to Shibuya Station."

[1350] Step 2:

[1351] The voice data acquired by the device is converted into text data using the Google Speech Recognition API. This is a process using speech recognition, where the input is raw voice data and the output is text data such as "To Shibuya Station."

[1352] Step 3:

[1353] The device sends the current location information obtained from GPS along with the text data to the server. The input here is the text data and GPS data, and the output is the data sent to the server.

[1354] Step 4:

[1355] The server analyzes the received text data using natural language processing and identifies the user's destination. The input is text data, and the output is the identified destination, such as "Shibuya Station."

[1356] Step 5:

[1357] The server uses APIs to collect weather, traffic, and facility information based on the user's current location and destination information. The input is GPS data and the identified destination, and the output is various real-time information.

[1358] Step 6:

[1359] The server calculates the optimal route using the collected real-time information. For example, using the Geopy library, the input is real-time information and current location / destination information, and the output is specific route guidance data.

[1360] Step 7:

[1361] The server then sends the generated route guidance data to the smartphone. The input is route guidance data, and the output is data transmission to the smartphone.

[1362] Step 8:

[1363] The device converts the received route guidance data into voice data using Google Text-to-Speech (gTTS) and provides it to the user as voice guidance. The input is text data for route guidance and the output is voice data, and this voice guidance is played back to the user.

[1364] Step 9:

[1365] While moving, the server monitors the current location and surrounding conditions in real time, and recalculates the optimal route based on new information as needed. The recalculated route is then sent back to the smartphone, which then provides the user with updated guidance via voice synthesis. Here, the input is real-time location information and surrounding information, and the output is updated route guidance data and its voice guidance.

[1366] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1367] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[1368] Overview of the embodiment

[1369] A user uses a mobile device such as a smartphone to input their destination and travel requests. This input can be in the form of voice or text. In the case of voice input, a voice recognition means converts the voice data into text data. The smartphone sends this text data and current location information to a server. The server analyzes the transmitted information and performs natural language processing to identify the user's destination. The server then collects necessary data such as weather information, traffic information, and facility information, and calculates the optimal travel route. Specific route guidance based on this travel route is generated and sent to the smartphone. The smartphone then converts the received data into voice using a voice synthesis means, and provides voice guidance to the user. The current location and surrounding conditions are monitored in real time even while traveling, and the optimal route is updated appropriately based on the updated information. In addition, an emotion recognition means is used to grasp the user's emotional state, and route guidance is provided according to that emotion.

[1370] Program processing details

[1371] Receiving User Input

[1372] Using a smartphone, a user inputs their destination and travel requests by voice or text. For example, they might input "Please tell me the route to Shinjuku Station." This voice data is converted into text data by the smartphone's voice recognition system. Furthermore, the emotion recognition engine also analyzes the user's emotions when inputting the voice.

[1373] Data analysis on the server

[1374] The device sends the text data, current location information obtained from GPS, and emotion recognition results to the server. The server analyzes the received text data and identifies the destination through natural language processing. For example, the location "Shinjuku Station" may be identified. The user's emotion data is also reflected appropriately at this stage.

[1375] Gathering information and calculating the best route

[1376] The server uses weather information APIs, traffic information APIs, etc. to collect necessary data based on the user's current location and destination. This allows the optimal travel route to be calculated, taking into account traffic congestion and weather. For example, if the user is feeling stressed, a route that takes an uncrowded train or passes through a relaxing stopover point will be selected.

[1377] Generating specific directions based on emotions

[1378] The server uses the results of the emotion recognition engine to reflect the user's emotional state in the generated route guidance. For example, if the user is feeling stressed, it will add a recommendation to take a break in a quiet place. Specific route guidance data might include instructions such as, "There is a park on the way to your destination, so it would be a good idea to take a short break."

[1379] Providing audio guidance

[1380] The server then sends the generated specific route guidance data to the smartphone. The smartphone then converts the received data into voice using a speech synthesis system. Voice guidance such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break there" is provided.

[1381] Real-time information updates

[1382] While moving, the server monitors the current location and surrounding conditions in real time. If new information or an emergency occurs, it responds immediately. For example, if there is a traffic jam on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[1383] The above is a description of a specific embodiment of the present invention. This series of processes allows the user to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making travel more stress-free and comfortable.

[1384] The processing flow will be explained below.

[1385] Step 1:

[1386] The user uses a smartphone to input their destination and travel requests by voice or text. For example, they can input "Please tell me the route to Shinjuku Station."

[1387] Step 2:

[1388] The device receives voice input from the user and converts it into text data using a voice recognition system. Voice data such as "Please tell me the route to Shinjuku Station" is converted into text data such as "Please tell me the route to Shinjuku Station."

[1389] Step 3:

[1390] The device uses an emotion recognition engine to analyze the user's emotions during voice input. Emotional data is generated based on the user's tone of voice, speaking rate, pauses, etc. At this time, emotional states such as "the user is nervous" are identified.

[1391] Step 4:

[1392] The device sends the converted text data, emotion recognition data, and current location information obtained from GPS to the server. At this time, the text data "Please tell me the route to Shinjuku Station," the current location data, and the emotion recognition result (e.g., "I'm nervous") are sent.

[1393] Step 5:

[1394] The server analyzes the received text data and identifies the user's destination using natural language processing. For example, the location "Shinjuku Station" is identified.

[1395] Step 6:

[1396] The server collects weather, traffic, and facility information based on the current location information to calculate the optimal route, including the use of weather and traffic information APIs.

[1397] Step 7:

[1398] The server runs an algorithm to calculate the optimal route based on the collected information. For example, it takes into account rainfall forecasts from weather information and the congestion level of the next arriving train from traffic information, and generates a route such as "take Line A to Station B, then transfer to Line C to reach Shinjuku Station."

[1399] Step 8:

[1400] The server also takes into account the results of the emotion recognition engine to generate directions that reflect the user's emotional state. For example, if the user is feeling nervous, the server will select a route that is relatively relaxing and includes quiet stops. In this way, a route such as "Take Line A to Station B, then transfer to Line C to reach Shinjuku Station. Stopping at a park along the way will help you relax" will be generated.

[1401] Step 9:

[1402] The server then sends the generated specific route guidance data to the user's device, including detailed instructions such as "Take the train on line A that arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by a park on the way to relax."

[1403] Step 10:

[1404] The device then converts the route guidance data received from the server into speech using a speech synthesis system. The text data is converted into speech, such as "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. You can also stop by a park on the way to relax."

[1405] Step 11:

[1406] The device will then provide the user with directions converted into audio, such as "Take the train on line A, which arrives in two minutes, get off at station B, transfer to line C, and get off at Shinjuku station. You can also stop by the park on the way to relax."

[1407] Step 12:

[1408] The server monitors the user's current location and surrounding conditions in real time while they are moving. This allows it to instantly recalculate the optimal route and provide new guidance if new information or an emergency occurs. For example, if there is traffic congestion on the route, instructions such as "The road to the right is clear, so please proceed that way" are provided in real time.

[1409] These are the specific processing steps of the system. This series of processes allows users to reach their destination safely and efficiently through voice guidance. In addition, the emotion recognition function provides guidance that takes into account the user's psychological state, making the journey itself more stress-free and comfortable.

[1410] Example 2

[1411] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1412] Conventional travel guidance systems provide routes without considering the user's emotional state, which can cause stress for the user. Furthermore, many systems are unable to respond to real-time changes in the situation, making it difficult for users to deal with congestion and unexpected situations. Therefore, there was a need for the development of a system that provides guidance that takes the user's psychological state into consideration and always guides them to the optimal route.

[1413] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1414] In this invention, the server includes a natural language processing means, a means for grasping an emotional state, and a means for calculating an optimal travel route, which enables the server to analyze a user's voice input, provide optimal route guidance according to the user's emotional state, and respond to changes in the situation in real time.

[1415] "Speech recognition means" is a technology for converting a user's voice input into text data.

[1416] "Natural language processing means" is a technology for analyzing text data to identify the user's intentions and destination.

[1417] "Emotional state" is data that represents the psychological state of the user, and indicates emotions such as stress or joy that the user is feeling.

[1418] "Speech synthesis means" is a technology for outputting text data as voice.

[1419] An "optimal travel route" is a route that is determined to be the most efficient and safe way to reach a destination.

[1420] "Weather information" is data about current and future weather conditions.

[1421] "Traffic information" refers to data on congestion and delays on roads and public transportation.

[1422] "Facility information" is data relating to facilities on the travel route, and includes, for example, location information of restaurants, parks, and the like.

[1423] "Real-time monitoring" refers to monitoring the user's current location and surrounding conditions in real time.

[1424] The present invention is a system that supports a user's travel safely and efficiently while providing optimal guidance according to the user's psychological state through emotion recognition. This system is composed of a combination of multiple technologies, including speech recognition, natural language processing, real-time information collection and analysis, speech synthesis, and an emotion recognition engine. The following describes a specific embodiment of the system.

[1425] Receiving User Input

[1426] Users use mobile devices such as smartphones to input their destinations and travel requests in voice or text format. When a user types, "Please tell me the route to Shinjuku Station," the smartphone's voice recognition system (e.g., Google Voice Recognition) converts the voice data into text data. At this time, an emotion recognition engine (e.g., Amazon Rekognition) also analyzes the voice data in parallel to determine the user's emotional state.

[1427] Sending current location information and text data

[1428] The device sends the text data converted by the speech recognition system, the current location information obtained from GPS, and the emotion recognition results to the server. This data includes the user's current location, destination, and emotional state.

[1429] Data analysis and destination identification

[1430] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API) to identify the user's destination. The server also analyzes the emotional data to understand the user's psychological state. For example, if "Shinjuku Station" is identified as the destination, it may be determined that the user is feeling stressed.

[1431] Gathering necessary information

[1432] Based on the specified destination and current location, the server uses weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API) to collect the necessary data, including existing congestion and weather data.

[1433] Calculating the best route

[1434] The server uses the collected information to calculate the optimal route, taking the user's emotional state into account in the process. For example, if the user is feeling stressed, the server can choose a quieter, more relaxing route to avoid crowds.

[1435] Generating directions based on emotions

[1436] The server generates specific route guidance that reflects the results of the emotion recognition engine. For example, it might generate a guidance sentence such as, "There is a park on the way to your destination, so it would be a good idea to take a short break." This guidance takes the user's psychological state into consideration.

[1437] Providing guidance data

[1438] The server sends the generated route guidance data to the device. The device converts the received data into speech using a speech synthesis system (e.g., Google Text-to-Speech) and provides the user with audio guidance. For example, the device might say, "Take the train on Line A, which arrives in two minutes, get off at Station B, transfer to Line C, and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[1439] Real-time information updates

[1440] While moving, the device sends its current location and surrounding conditions to the server in real time. The server uses this information to update the guidance as needed. For example, if there is traffic congestion on the route, the instructions will be instantly changed to, "The road to the right is clear, so please take that route."

[1441] Examples and prompts

[1442] Example of a user entering "Route to Shinjuku Station" by voice:

[1443] 1. The user types into their smartphone, "Please tell me the route to Shinjuku Station."

[1444] 2. The voice recognition system converts this into text, and the emotion recognition engine identifies it as "stress."

[1445] 3. The device sends this data to the server.

[1446] 4. The server determines the destination, "Shinjuku Station," and the user's emotional state.

[1447] 5. The server uses weather information API and traffic information API to calculate the optimal route and select a route that passes through a quiet park, etc.

[1448] 6. The server generates guidance data such as "There is a park on the way to your destination, so it would be a good idea to take a short break there," and sends it to the device.

[1449] 7. The terminal uses a speech synthesis system to convert the information into speech and provide guidance to the user.

[1450] 8. The server monitors the latest situation and updates the information.

[1451] Prompt Sentence Examples

[1452] "Please explain the program process flow for a system in which a user uses a smartphone to ask for directions by voice, converts that voice into text data, and performs emotion recognition. Furthermore, please state the specific steps in order in which the server calculates the optimal route based on the current location and destination information, and provides voice guidance according to the user's emotional state."

[1453] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1454] Step 1:

[1455] The user uses a smartphone to input their destination and travel requests in voice or text format. For example, they might say, "Please tell me the route to Shinjuku Station." The input voice is captured as voice data through the smartphone's microphone.

[1456] Input: User voice input

[1457] Output: Audio data

[1458] Step 2:

[1459] The device analyzes the acquired voice data using a voice recognition system (e.g., Google Voice Recognition), converts the voice into text data, and then analyzes the emotional state of the user at the time of voice input using an emotion recognition engine (e.g., Amazon Rekognition).

[1460] Input: Audio data

[1461] Output: Text data and emotional state data

[1462] Step 3:

[1463] The device sends the converted text data, current location information obtained from GPS, and emotional state data resulting from emotion recognition to the server.

[1464] Input: Text data, current location information, emotional state data

[1465] Output: The transmitted dataset (text data, current location information, emotional state data)

[1466] Step 4:

[1467] The server analyzes the received text data using natural language processing (e.g., Google Cloud Natural Language API). In addition to identifying the destination, it also analyzes the emotional state data to understand the user's psychological state. For example, the destination "Shinjuku Station" is identified from the text data, and "stress" is identified from the emotional state data.

[1468] Input: Received dataset (text data, current location information, emotional state data)

[1469] Output: Destination information, user's emotional state

[1470] Step 5:

[1471] Based on the destination information and current location, the server collects the necessary external data using weather information APIs (e.g., OpenWeatherMap) and traffic information APIs (e.g., Google Maps Traffic API). At this stage, congestion status and weather data are obtained.

[1472] Input: Destination information, current location information

[1473] Output: Collected weather and traffic information

[1474] Step 6:

[1475] The server calculates the optimal route based on collected weather and traffic information, as well as the user's emotional state. For example, if the user is feeling stressed, the server will select a quiet route to avoid crowds and suggest a route that includes a relaxing park along the way.

[1476] Input: Weather information, traffic information, user's emotional state, current location information, destination information

[1477] Output: Optimal travel route data

[1478] Step 7:

[1479] The server generates specific route guidance data based on the emotion recognition results, such as "On your way to Shinjuku Station, it might be a good idea to take a short break in a quiet park."

[1480] Input: Optimal travel route data, user emotional state

[1481] Output: Specific route guidance data

[1482] Step 8:

[1483] The server sends the generated route guidance data to the device. The device then converts the received route guidance data into voice using a speech synthesis system (e.g., Google Text-to-Speech). For example, the device might play a voice message such as, "Please board the train arriving in two minutes and get off at Shinjuku Station. There is also a park on the way to your destination, so it would be a good idea to take a short break."

[1484] Input: Specific route data

[1485] Output: Voice guidance

[1486] Step 9:

[1487] While traveling, the device sends its current location and surrounding conditions to the server in real time, and the server updates the guidance based on the new information. For example, if there is a traffic jam on the route, the server will immediately provide instructions such as, "The road to the right is clear, so please take that road."

[1488] Input: Real-time location data, surrounding situation data

[1489] Output: Updated navigation data

[1490] This is the specific flow of the program processing for this system. As a result, users can travel comfortably thanks to the emotion recognition function and receive optimal guidance in real time.

[1491] (Application example 2)

[1492] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1493] Conventional mobility assistance systems often do not take into account the user's emotions or psychological state when providing route guidance to the user's destination, which means they are unable to reduce stress and anxiety during travel. Furthermore, they can sometimes have difficulty responding quickly to unexpected changes in traffic conditions. This makes it difficult for users to travel in a relaxed mood.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and adjusting route guidance based on that emotional state, means for recommending relaxing places and scenery at specific points on the travel route, and means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on that location information. This reduces the user's stress and anxiety during travel and enables a comfortable travel experience.

[1495] "User" refers to a person who uses the system to receive travel guidance to a destination.

[1496] "Destination" refers to the location to which the user wishes to travel.

[1497] "Voice or text format" refers to one of the ways in which a user expresses their destination or travel needs to the system, such as voice input or text input.

[1498] "Speech recognition means" refers to technology that analyzes the voice input by the user and converts it into text data.

[1499] "Text data" refers to character string data converted from voice data by a voice recognition means.

[1500] "User's current location information" is information indicating the user's real-time geographical location, and is generally obtained using technology such as GPS.

[1501] "Server" refers to the central computing device that analyzes and processes data sent by users and generates optimal travel routes and guidance information.

[1502] "Natural language processing means" refers to technology that analyzes text data, understands its meaning, and identifies the user's intention (destination).

[1503] The "optimal travel route" refers to the optimal route for a user to reach a destination safely and efficiently.

[1504] "Weather information, traffic information, and facility information" refers to external data required for calculating travel routes, and refers to information about weather conditions, traffic congestion, and surrounding facilities.

[1505] "Speech synthesis means" refers to a technology that converts the generated route guidance data into a voice format and provides it to the user.

[1506] "Emotional state" is the result of analyzing the user's psychological state, and indicates a state such as stress, relaxation, or excitement.

[1507] "Relaxing places and scenery" refers to quiet places and beautiful scenery recommended to reduce the user's stress and anxiety.

[1508] This invention is a system that supports users' safe and efficient travel and provides optimal guidance according to the user's psychological state through emotion recognition. This system is installed in an in-vehicle display or in-vehicle infotainment system and is designed for self-driving vehicles.

[1509] Hardware and Software Configuration

[1510] In-vehicle system configuration

[1511] Users input their destination and travel requests in voice form using a microphone inside the car. The in-car system includes the following main components:

[1512] Microphone: A device for receiving a user's voice input.

[1513] Speech Recognition API: Analyzes user voice input and converts it into text data. For example, Google Cloud Speech-to-Text is used.

[1514] On-board GPS module: Obtains the user's current location information.

[1515] On-board CPU: Processes data and performs calculations, and communicates with the server via an internet connection.

[1516] In-car speaker: Provides voice-synthesized directions to the user.

[1517] Server Configuration

[1518] On the server side, the main components include:

[1519] Natural language processing engine: Analyzes the received text data and identifies the destination. For example, various natural language processing libraries are used.

[1520] Emotion recognition engine: Analyzes user voice input and video data from in-car cameras to identify the user's emotional state. For example, AWS Comprehend or IBM Watson can be used.

[1521] Data Collection API: Quickly retrieve weather, traffic, and facility information. Examples include OpenWeatherMap and Google Maps APIs.

[1522] Route calculation algorithm: Calculates the optimal travel path and generates route guidance based on the user's emotional state.

[1523] Speech synthesis engine: Converts the generated route guidance data into speech format, for example, using Amazon Polly or IBM Watson TTS.

[1524] Specific examples of processing steps

[1525] User Input and Sentiment Analysis

[1526] The user speaks to the in-car system, saying, "Please take me to Shinjuku Station." The in-car system's voice recognition API converts this into text data and sends it to the server. At the same time, the user's emotional data is collected from the in-car camera and additional sensors and analyzed by the emotion recognition engine.

[1527] Data analysis and route calculation on the server

[1528] The server analyzes the received text data (to Shinjuku Station) using a natural language processing engine to identify the destination. Next, it uses a data collection API to collect real-time weather and traffic information and calculates the optimal route based on this. If the user wants to relax, it could guide them to a scenic route that avoids crowds.

[1529] Providing audio guidance

[1530] Specific route guidance data generated by the server (e.g., "Turn right at the next stop, go through the park, then go straight") is converted into voice data by a speech synthesis engine and sent to the in-vehicle system. Voice guidance is provided to the user through the in-vehicle speaker.

[1531] Real-time monitoring and guidance updates

[1532] The server obtains the user's current location information in real time from the GPS module and updates the route accordingly. For example, if a sudden traffic jam occurs, the server calculates an alternative route and provides updated guidance to the user.

[1533] Prompt Sentence Examples

[1534] "When a user asks for a recommended route to Shinjuku Station, please suggest the optimal route taking into account real-time traffic information and emotional data. Also, please generate guidance that will help the user relax."

[1535] This system allows users to travel safely and efficiently, and its emotion recognition functionality ensures a stress-free travel experience.

[1536] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1537] Step 1:

[1538] The user uses the in-car microphone to input their destination by voice. In this case, the user says, "Please take me to Shinjuku Station." The in-car system's voice recognition API (e.g., Google Cloud Speech-to-Text) captures this voice data and converts it into text data. The input data is voice data, and the output data is text data.

[1539] Step 2:

[1540] The converted text data and the user's current location information are sent from the in-vehicle system to the server. The in-vehicle system uses a GPS module to obtain real-time location information and transmits this location information at the same time. The input data is the text data and current location information, and the output data is the data sent to the server.

[1541] Step 3:

[1542] The server analyzes the received text data using a natural language processing engine and identifies the user's destination. For example, the destination "Shinjuku Station" is identified. The input data is text data, and the output data is destination information.

[1543] Step 4:

[1544] The server performs emotion recognition using the user's voice input and video data from the in-car camera. Using an emotion recognition engine (e.g., AWS Comprehend), it analyzes whether the user needs to relax, for example. The input data are the voice and video data, and the output data are the emotion recognition results.

[1545] Step 5:

[1546] The server obtains weather, traffic, and facility information using data collection APIs (e.g., OpenWeatherMap, Google Maps API). Then, it calculates the optimal travel route based on this information. The input data are destination information, current location information, emotion recognition results, and information from external APIs, and the output data is the optimal travel route.

[1547] Step 6:

[1548] The server generates guidance based on the user's emotional state, recommending relaxing places and scenery along the route. For example, it generates guidance such as "Turn right next time, pass through the park, then go straight." The input data are the optimal route and emotion recognition results, and the output data are specific route guidance.

[1549] Step 7:

[1550] The generated specific route guidance data is converted into speech by a speech synthesis engine (e.g., Amazon Polly). The server sends this speech data to the in-vehicle system. The input data is the route guidance data, and the output data is speech data.

[1551] Step 8:

[1552] The in-vehicle system receives the voice data and provides it to the user through the in-vehicle speaker. The user receives the guidance, "Turn right at the next stop, through the park, then go straight." The input data is the voice data, and the output data is the voice guidance to the user.

[1553] Step 9:

[1554] The server monitors the user's current location and traffic conditions in real time, and updates the route accordingly if new information becomes available. For example, if a sudden traffic jam occurs, the server calculates an alternative route and immediately provides updated guidance to the user. The input data is real-time location information and new traffic information, and the output data is the updated route guidance.

[1555] 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.

[1556] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1557] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1558] 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.

[1559] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1560] 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.

[1561] 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).

[1562] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1563] 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."

[1564] 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.

[1565] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1566] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1571] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] The following is further disclosed regarding the above embodiment.

[1577] (Claim 1)

[1578] means for inputting a destination and travel requests from a user in voice or text format;

[1579] a speech recognition means for converting the speech input into text data;

[1580] means for transmitting the text data and the user's current location information to a server;

[1581] natural language processing means for analyzing the text data in the server and identifying the user's destination;

[1582] means for collecting weather, traffic, and facility information for calculating an optimal travel route;

[1583] A means for calculating an optimal travel route based on the collected information and generating specific route guidance;

[1584] means for transmitting the generated route guidance data to a user's terminal;

[1585] a voice synthesis means for converting the route guidance data received at the terminal into voice;

[1586] means for providing the voice-synthesized directions to a user;

[1587] A method to monitor the user's current location and surrounding conditions in real time while moving, and to appropriately update the optimal route based on the updated information.

[1588] Including system.

[1589] (Claim 2)

[1590] 10. The system of claim 1, further comprising means for analyzing the speech input and performing natural language processing based on the analysis results.

[1591] (Claim 3)

[1592] 10. The system of claim 1, further comprising means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on the location information.

[1593] "Example 1"

[1594] (Claim 1)

[1595] means for inputting a destination and travel requests from a user in voice or text format;

[1596] a speech recognition means for converting the speech input into text data;

[1597] means for transmitting the text data and the user's current location information to a server;

[1598] natural language processing means for analyzing the text data in the server and identifying the user's destination;

[1599] means for collecting weather, traffic and location information for calculating an optimal travel route;

[1600] A means for calculating an optimal travel route based on the collected information and generating specific route guidance;

[1601] means for transmitting the generated route guidance data to a user's terminal;

[1602] a voice synthesis means for converting the route guidance data received at the terminal into voice;

[1603] means for providing the voice-synthesized directions to a user;

[1604] A means for monitoring the user's current location and surrounding conditions in real time while the user is moving, and for appropriately updating the optimal route based on the updated information;

[1605] A means to provide users with navigation prompts generated via a generative AI model.

[1606] Including system.

[1607] (Claim 2)

[1608] 10. The system of claim 1, further comprising means for analyzing the speech input and performing natural language processing based on the analysis results.

[1609] (Claim 3)

[1610] 10. The system of claim 1, further comprising means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on the location information.

[1611] "Application Example 1"

[1612] (Claim 1)

[1613] means for inputting a destination and travel requests from a user in voice or text format;

[1614] a speech recognition means for converting the speech input into text data;

[1615] means for transmitting the text data and the user's current location information to a server;

[1616] natural language processing means for analyzing the text data in the server and identifying the user's destination;

[1617] means for collecting weather, traffic, and facility information for calculating an optimal travel route;

[1618] A means for calculating an optimal travel route based on the collected information and generating specific route guidance;

[1619] means for transmitting the generated route guidance data to a user's terminal;

[1620] a voice synthesis means for converting the route guidance data received at the terminal into voice;

[1621] means for providing the voice-synthesized directions to a user;

[1622] A means for monitoring the user's current location and surrounding conditions in real time while the user is moving, and for appropriately updating the optimal route based on the updated information;

[1623] In an autonomous vehicle, a means for guiding an optimal route to a destination specified by a user through voice and correcting the route in real time while the vehicle is traveling;

[1624] A means for optimizing a driving route and controlling the driving of a vehicle based on the collected weather information and traffic information;

[1625] A system including:

[1626] (Claim 2)

[1627] 10. The system of claim 1, further comprising means for analyzing the speech input and performing natural language processing based on the analysis results.

[1628] (Claim 3)

[1629] 10. The system of claim 1, further comprising means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on the location information.

[1630] "Example 2: Combining Emotion Engines"

[1631] (Claim 1)

[1632] means for inputting a destination and travel requests from a user in voice or text format;

[1633] a speech recognition means for converting the speech input into text data;

[1634] means for transmitting the text data and the user's current location information and emotional state to a server;

[1635] natural language processing means for analyzing the text data in the server and identifying the user's destination;

[1636] a means for understanding a psychological state of a user by auxiliary use of the emotion data;

[1637] means for collecting weather, traffic, and facility information for calculating an optimal travel route;

[1638] A means for calculating an optimal travel route based on the collected information and generating specific route guidance according to the emotional state of the user;

[1639] means for transmitting the generated route guidance data to a user's terminal;

[1640] a voice synthesis means for converting the route guidance data received at the terminal into voice;

[1641] means for providing the voice-synthesized directions to a user;

[1642] A method to monitor the user's current location and surrounding conditions in real time while moving, and to appropriately update the optimal route based on the updated information.

[1643] ...

Claims

1. means for inputting a destination and travel requests from a user in voice or text format; a speech recognition means for converting the speech input into text data; means for transmitting the text data and the user's current location information to a server; natural language processing means for analyzing the text data in the server and identifying the user's destination; means for collecting weather, traffic, and facility information for calculating an optimal travel route; A means for calculating an optimal travel route based on the collected information and generating specific route guidance; means for transmitting the generated route guidance data to a user's terminal; a voice synthesis means for converting the route guidance data received at the terminal into voice; means for providing the voice-synthesized directions to a user; A method to monitor the user's current location and surrounding conditions in real time while moving, and to appropriately update the optimal route based on the updated information. Including system.

2. The system of claim 1 further comprising means for analyzing the speech input and performing natural language processing based on the analysis results.

3. 2. The system of claim 1, further comprising means for tracking the user's current location information in real time and dynamically updating optimal route guidance based on the location information.

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