System

Abstract

A system is provided.SOLUTION: A system includes a means for receiving acceleration data collected from a user, a means for analyzing the received acceleration data and estimating a road surface state, a means for generating an optimal route according to attribute information of the user on the basis of the estimated road surface state information, and a means for transmitting the generated optimal route information to a user terminal.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] Existing navigation systems have difficulty responding immediately to changes in road accessibility due to crustal movements or building collapses during disasters. Furthermore, existing services that visualize road accessibility often lack information that meets the needs of all user attributes (e.g., pedestrians, wheelchair users), resulting in the display of roads that are actually impassable to certain users. The present invention aims to solve these problems by grasping changing road accessibility information in real time and providing optimal routes tailored to the user's attributes. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for receiving acceleration data collected from a user; means for analyzing the received acceleration data and estimating road surface conditions; means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information; and means for transmitting the generated optimal route information to a user terminal. Specifically, the system analyzes the received acceleration data to estimate road surface conditions such as flat ground, steps, slopes, and stairs in real time, and provides an appropriate route based on the user's attribute information (pedestrian, wheelchair user, cyclist, etc.), enabling quick and efficient evacuation even in the event of a disaster when normal routes are unusable.

[0006] "User" refers to any individual or group of people who utilizes the system and requires a passable route.

[0007] "Acceleration Data" means data obtained from sensors on a user's smartphone or other device that indicates changes in acceleration during movement.

[0008] "Means for receiving" refers to a device or technical means for inputting acceleration data and attribute information transmitted from a user terminal into a server.

[0009] "Means for analyzing" means algorithms or software for processing received acceleration data and identifying or estimating road surface conditions.

[0010] "Estimating road surface conditions" refers to determining the current road condition (flat ground, bumps, slopes, stairs, etc.) from the analysis results of acceleration data.

[0011] "Attribute Information" means data that indicates a user's mode of transportation or physical characteristics (e.g., pedestrian, wheelchair user, cyclist, etc.).

[0012] "Means for generating an optimal route" refers to the technical means or algorithms that calculate the safest and most efficient route based on estimated road surface conditions and user attribute information.

[0013] "User terminal" means a smartphone, tablet, or other mobile device that a user has to use the system.

[0014] "Optimal route information" refers to route data calculated based on real-time analysis of road conditions and user attribute information.

[0015] "Means for sending" refers to the communication means or technology for sending the optimal route information generated by the server to the user's terminal.

[0016] This provides a clear definition of each element included in the claims. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road surface conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0039] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and starts collecting acceleration data. The collected acceleration data is then periodically sent from the device to a server.

[0040] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to a slope. This allows the server to estimate the current road surface condition (flat ground, steps, slope, stairs, etc.) with high accuracy.

[0041] Next, the user enters their own attribute information (e.g., wheelchair user) into the application. This attribute information is sent from the device to the server, and the server uses this information to generate the optimal route. Specifically, the estimated road surface condition information is combined with the user's attribute information to calculate a route that meets the individual needs of wheelchair users, such as prioritizing routes that avoid steps and stairs.

[0042] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. This route information is updated every time the server receives and analyzes new acceleration data, reflecting the latest passable information.

[0043] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone. The smartphone uses an acceleration sensor to collect data and sends it to a server. The server analyzes the acceleration data and estimates the current road surface conditions. After that, when the user indicates within the app that they are a wheelchair user, the server generates an optimal route that avoids steps and stairs and sends that information to the user's device. The user can evacuate safely by relying on the route displayed on the map.

[0044] This system allows for quick and efficient evacuation, by providing the optimal evacuation route based on the user's attributes in real time, even in the event of a disaster.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user launches the application installed on their smartphone, which prepares them to begin evacuation.

[0048] Step 2:

[0049] The device detects that the application has been launched and activates the smartphone's accelerometer, which starts collecting acceleration data about the user's movements.

[0050] Step 3:

[0051] The device sends the collected acceleration data at regular intervals (e.g., every second) to the server. This data includes a timestamp indicating the change in acceleration during movement and the device ID.

[0052] Step 4:

[0053] The server receives the acceleration data sent from the device, temporarily stores it in a database, and prepares it for analysis.

[0054] Step 5:

[0055] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0056] Step 6:

[0057] Users enter their demographic information (e.g., wheelchair user) within the application, which is important to address the specific needs of each individual user.

[0058] Step 7:

[0059] The device sends the attribute information entered by the user to the server, which then prepares to calculate the optimal route based on the user's attributes.

[0060] Step 8:

[0061] The server combines the estimated road surface conditions with the user's attribute information to generate the optimal route for the user. For example, it prioritizes routes that avoid steps and stairs for wheelchair users.

[0062] Step 9:

[0063] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0064] Step 10:

[0065] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0066] Step 11:

[0067] Users can evacuate safely by following the optimal route displayed on the map, with route information updated in real time.

[0068] Example 1

[0069] 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."

[0070] Conventional technologies have struggled to estimate road surface conditions in real time and provide optimal routes based on user attributes. This makes it particularly difficult to generate appropriate evacuation routes during disasters, as road surface conditions change rapidly. Furthermore, route calculations tailored to user attributes were also inadequate. This resulted in a lack of reliable information for safe and rapid evacuation, particularly for pedestrians and wheelchair users who experience frequent vibrations.

[0071] 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.

[0072] In this invention, the server includes means for periodically receiving acceleration data collected from a user, means for analyzing the received acceleration data in real time and estimating road surface conditions based on fluctuation patterns in the data, means for transmitting attribute information input by the user from a terminal to the server, means for generating an optimal route by combining the estimated road surface condition information and the user's attribute information, means for transmitting the generated optimal route information to the user terminal, and means for updating the route information each time the user terminal collects new acceleration data, thereby enabling the provision of an optimal route in real time according to the user's attributes.

[0073] "Acceleration data collected from the user" refers to acceleration information such as the user's movements and vibrations, which is acquired by the user's device using a built-in sensor.

[0074] "Receiving at regular intervals" means receiving data periodically at a certain interval.

[0075] "Analyzing in real time" means processing data immediately after receiving it and obtaining analysis results as current information.

[0076] The "data fluctuation pattern" refers to the tendency of changes over time in the collected acceleration data.

[0077] "Estimating road surface conditions" means determining the road surface conditions, such as flat ground, steps, slopes, and stairs, from the analysis results of acceleration data.

[0078] "Attribute information input by the user from the terminal" is information about the characteristics of the user (e.g., pedestrian, wheelchair user, etc.) that the user provides through the terminal.

[0079] "Generating an optimal route" means calculating the most suitable route for the user based on the estimated road surface conditions and the user's attribute information.

[0080] "Transmitting to a user terminal" means transferring calculated or generated information to a device held by the user via a network.

[0081] "Collecting new acceleration data" means re-obtaining acceleration information relating to the user's movements that has been updated over time.

[0082] "Updating route information" means reevaluating the optimal route based on newly acquired acceleration data and making changes as necessary.

[0083] This invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0084] Hardware and Software Configuration

[0085] The hardware used includes a smartphone (with an accelerometer) and a server, while the software includes an application installed on the smartphone and a data analysis algorithm running on the server.

[0086] Data collection

[0087] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and begins collecting acceleration data. Specifically, data collection begins the moment the user starts moving, recording minute vibrations and changes in tilt.

[0088] Sending data

[0089] The collected acceleration data is sent from the user's device to a server at regular intervals, using Wi-Fi or mobile data networks.

[0090] Data analysis

[0091] The server analyzes the received acceleration data in real time. The analysis algorithm identifies road surface conditions based on the fluctuation patterns of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface conditions (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0092] Entering user attribute information

[0093] Users input their attribute information (e.g., wheelchair user, visually impaired, etc.) within the application, and this information is sent from the user's device to the server.

[0094] Optimal route generation

[0095] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route. For example, for wheelchair users, it prioritizes routes that avoid steps and stairs. Based on this information, the server calculates the optimal route for each individual user.

[0096] Submitting and updating route information

[0097] The generated optimal route information is sent from the server to the user's device. The user's device displays this information on a map, allowing the user to check the optimal route in real time. The user's device also collects new acceleration data and sends it to the server, which then updates the route information in real time.

[0098] Specific examples

[0099] For example, consider a scenario in which an earthquake occurs and the user begins evacuation. The user launches an application installed on their smartphone and begins collecting data using the accelerometer. The collected data is sent from the device to a server, which analyzes the data and estimates the current road surface conditions. After that, if the user indicates within the app that they are a wheelchair user, the server generates an optimal evacuation route without steps or stairs and sends that information to the user's device. The user can then begin a safe evacuation by relying on the route displayed on the map.

[0100] Prompt Sentence Examples

[0101] "What is the best route to evacuate after an earthquake? I use a wheelchair."

[0102] "Please tell me the safest evacuation route from my current location. I would like to avoid stairs and steps."

[0103] This system will provide the optimal route based on the user's attributes in real time, even in the event of a disaster, enabling quick and safe evacuation.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The user launches an application installed on the smartphone.

[0107] Input: The user's action to launch the application.

[0108] Output: Application launch.

[0109] What happens: The user taps the application icon to launch the application.

[0110] Step 2:

[0111] The device will activate the smartphone's accelerometer and begin collecting acceleration data.

[0112] Input: The application startup state.

[0113] Output: Start collecting acceleration data.

[0114] Specific operation: After the application is launched, it automatically activates the smartphone's accelerometer and records acceleration data on the x-axis, y-axis, and z-axis in real time.

[0115] Step 3:

[0116] The device stores the collected acceleration data in a buffer at regular intervals and transmits it to the server.

[0117] Input: Collected acceleration data.

[0118] Output: Acceleration data sent to the server.

[0119] How it works: Every few seconds, the device collects data, stores it in a buffer, and then sends the data packets to the server via Wi-Fi or the mobile data network.

[0120] Step 4:

[0121] The server puts the received acceleration data into an analysis queue and starts analyzing it in real time.

[0122] Input: Acceleration data sent to the server.

[0123] Output: Estimated road surface conditions.

[0124] Specific operation: The server puts the received data into an analysis queue, applies a data analysis algorithm, and determines the road surface condition (flat ground, bumps, slopes, stairs, etc.) based on the fluctuation pattern of the data.

[0125] Step 5:

[0126] The user inputs their own attribute information within the application and sends it from the terminal to the server.

[0127] Input: User demographic information (e.g. wheelchair user, walking).

[0128] Output: The attribute information sent to the server.

[0129] Specific operation: The user accesses the application's settings screen and selects or enters their attribute information using drop-down menus and text boxes. This information is sent from the device to the server.

[0130] Step 6:

[0131] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route.

[0132] Input: Road surface condition information and user attribute information.

[0133] Output: Optimal route.

[0134] How it works: The server retrieves attribute information from the database, combines it with road surface condition data, and runs a route calculation algorithm to calculate the optimal route for individual needs, such as avoiding steps and stairs for wheelchair users.

[0135] Step 7:

[0136] The server transmits the generated optimal route information to the user terminal.

[0137] Input: Optimal route information.

[0138] Output: Route information sent to the user device.

[0139] Specific operation: The server compiles optimal route information into packets and sends them to the device. The device then displays the received route information in a map app or dedicated app.

[0140] Step 8:

[0141] Each time the user terminal collects new acceleration data, it updates the route information.

[0142] Input: New acceleration data.

[0143] Output: The updated route information.

[0144] What it does: The device sends newly collected acceleration data to the server. The server analyzes the new data and recalculates the route if necessary. The updated information is then sent back to the user's device, updating the route information on the map in real time.

[0145] In this way, the system can quickly respond to changes in the user's movements and environment, providing an always-up-to-date and safe route.

[0146] (Application example 1)

[0147] 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."

[0148] The present invention relates to the technical field of estimating road surface conditions by analyzing acceleration data collected from a user. In particular, it aims to provide a safe and comfortable route for an automated driving vehicle, and to solve the problem that it is difficult for current navigation systems to grasp road surface conditions in real time and to quickly change the route based on the detected road surface conditions.

[0149] 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.

[0150] In this invention, the server includes means for receiving acceleration data collected from a user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to user attribute information based on the estimated road surface condition information, means for transmitting the generated optimal route information to a user terminal, and means for controlling the vehicle's navigation system to automatically drive the vehicle based on the optimal route, thereby enabling fast and safe automatic driving based on real-time understanding of road surface conditions.

[0151] "User" refers to any person or entity that uses the system to provide acceleration data and receive optimized route information.

[0152] "Acceleration data" is information that indicates changes in movement measured by an acceleration sensor collected from a user's terminal or vehicle.

[0153] The "receiving means" is a communication means for inputting the acceleration data transmitted from the user's terminal into the server.

[0154] The "analyzing means" is a calculation means for processing the received acceleration data and estimating the road surface condition in real time.

[0155] "Road surface conditions" refers to the terrain and ground conditions, such as flat ground, steps, slopes, and stairs.

[0156] "Attribute information" is data that indicates the characteristics of a user (e.g., pedestrian, wheelchair user, bicycle user, self-driving vehicle user).

[0157] The "optimal route" is a safe and efficient route calculated based on estimated road surface condition information and user attribute information.

[0158] The "transmitting means" is a communication means for transferring the generated optimum route information to the user terminal.

[0159] A "vehicle navigation system" is a system that provides route information necessary for an autonomous vehicle to reach its destination and controls the vehicle's direction of travel and speed.

[0160] The present invention is a system that analyzes acceleration data collected from a user in real time, estimates road surface conditions, and provides an optimal route based on the user's attribute information. This system works in cooperation with the user's terminal, a server, and the vehicle's navigation system.

[0161] First, the user launches an application installed on their smartphone or vehicle-mounted device. The application activates the acceleration sensor and starts collecting acceleration data. The collected data is then periodically sent to a server.

[0162] The server uses a specific algorithm to analyze the received acceleration data in real time. This algorithm analyzes the fluctuation patterns of the acceleration data and estimates the road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy. For example, continuous high-frequency vibrations correspond to steps or stairs, while a gradual fluctuation pattern corresponds to a slope.

[0163] Next, the user enters their own attribute information (e.g., that they are a self-driving vehicle user) within the application. This attribute information is sent to the server, which then generates the optimal route based on this information. Specifically, the server prioritizes the safest and most comfortable route for the vehicle based on the estimated road surface conditions and the user's attribute information.

[0164] The generated optimal route information is sent from the server to the vehicle's navigation system. The navigation system controls the vehicle based on the received route information, providing autonomous driving functionality. The navigation system updates the route every time it receives new acceleration data, reflecting the latest road conditions and passability information.

[0165] Program processing

[0166] The user's terminal functions as a smartphone or a vehicle-mounted device and collects acceleration data using an acceleration sensor.

[0167] The server analyzes the acceleration data using programming languages ​​such as Python and R. Machine learning algorithms and data analysis libraries (e.g., scikit-learn, TensorFlow) are used for the analysis.

[0168] The vehicle's navigation system controls the vehicle based on the received route information. In addition to the usual navigation software, this system also has a function that reflects road surface conditions in real time.

[0169] Specific examples

[0170] For example, if a user is using a wheelchair-accessible autonomous vehicle, the user inputs attribute information into the application and sends road surface data via an acceleration sensor to the server. The server analyzes the data, generates the safest route for the wheelchair user, and sends it to the navigation system. The navigation system then controls the vehicle based on the received data, driving it to the destination along a route that avoids steps and stairs.

[0171] Prompt Sentence Examples

[0172] Design a system for a wheelchair-accessible autonomous vehicle that analyzes road conditions in real time based on acceleration data and provides the optimal route. Please include program code and detailed explanations for each step.

[0173] In this way, the present invention provides users of autonomous vehicles with optimal routes in real time, realizing a safe and comfortable driving environment.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user's device collects acceleration data using an acceleration sensor. Specifically, the user starts up their smartphone or vehicle-mounted device and starts acquiring acceleration data within the application. The input here is data from the device's acceleration sensor, and the output is the collected acceleration data.

[0177] Step 2:

[0178] The user's device periodically sends the collected acceleration data to the server. Specifically, the device sends data to the server using an HTTP POST request. The input is the collected acceleration data, and the output is the data sent to the server.

[0179] Step 3:

[0180] The server analyzes the received acceleration data in real time. Specifically, it uses machine learning algorithms and data analysis libraries based on the received data to estimate road surface conditions such as flat ground, steps, slopes, and stairs. The input is the acceleration data received by the server, and the output is estimated road surface condition information.

[0181] Step 4:

[0182] The user inputs attribute information within the application. Specifically, a UI is provided for inputting attribute information such as whether the user is an autonomous vehicle user, a pedestrian, or a wheelchair user. The input here is the user's attribute information, and the output is the attribute information sent to the server.

[0183] Step 5:

[0184] The server generates the optimal route based on estimated road surface condition information and user attribute information. Specifically, it runs an algorithm that takes into account constraints based on road conditions and user attributes, analyzes map data, and calculates the optimal route. The input is estimated road surface condition information and attribute information, and the output is the generated optimal route information.

[0185] Step 6:

[0186] The server sends the generated optimal route information to the user's device. Specifically, an HTTP POST request is sent from the server to the user's device to transfer the route information. The input is the generated optimal route information, and the output is the optimal route information sent to the device.

[0187] Step 7:

[0188] The terminal provides the received optimal route information to the vehicle's navigation system. Specifically, the navigation software runs based on the received data and updates the vehicle's route guide. The input is the optimal route information sent to the terminal, and the output is the updated route information for the autonomous vehicle's navigation system.

[0189] Step 8:

[0190] The navigation system controls the vehicle's direction and speed based on the route information received. Specifically, it generates driving parameters according to real-time road conditions and controls various actuators in the vehicle. The input is the updated route information from the navigation system, and the output is the driving behavior of the autonomous vehicle.

[0191] 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.

[0192] The present invention is a system that provides more flexible optimal routes in the event of a disaster by combining a system that collects and analyzes acceleration data from users with an emotion engine that recognizes the emotional state of the user. This system operates in cooperation with the user's terminal, server, and emotion engine.

[0193] First, the user launches the application installed on their smartphone, which prepares them to begin evacuation. The device detects the launch of the application and activates the smartphone's accelerometer to begin collecting acceleration data. The collected acceleration data is then sent from the device to a server at regular intervals.

[0194] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. Specifically, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0195] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses the user's facial recognition data, voice data, or other biometric information to estimate the user's emotional state. If the user is feeling fear or anxiety, the emotion engine will detect this and send the information to the server.

[0196] The user inputs their attribute information (e.g., wheelchair user) within the application. Both this attribute information and emotional state information are sent from the device to the server. The server then generates the optimal route based on this information. For example, if the user is a wheelchair user and feels anxious, the server will prioritize routes that are as easy and safe as possible to evacuate.

[0197] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. If the user's emotional state changes, the server can recalculate a new route and update it in real time.

[0198] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone, which collects data using an acceleration sensor. The acceleration data is sent to a server, which analyzes it and estimates the current road surface conditions. If the user is a wheelchair user and the emotion engine detects anxiety, the server will prioritize generating a safe and easy route that avoids steps and stairs and send it to the device. The user can evacuate safely by relying on the route displayed on the map.

[0199] This system provides optimal evacuation routes in real time, taking into account the user's emotional state during a disaster, enabling quick and safe evacuation.

[0200] The processing flow will be explained below.

[0201] Step 1:

[0202] The user launches the application installed on their smartphone, which activates the emotion engine and accelerometer.

[0203] Step 2:

[0204] The device detects that the application has been launched and activates the smartphone's camera, microphone, and accelerometer, thereby starting to collect facial recognition data, voice data, and acceleration data.

[0205] Step 3:

[0206] The device processes facial recognition data and voice data collected in real time and uses an emotion engine to estimate the user's emotional state, for example, determining whether the user is feeling fear or anxiety based on their facial expression.

[0207] Step 4:

[0208] The device transmits the collected acceleration data and emotional state data to the server at regular intervals (e.g., every second). This data includes acceleration changes during movement, a timestamp, the device ID, and the emotional state.

[0209] Step 5:

[0210] The server receives the acceleration data and emotional state data sent from the device, temporarily stores the data in a database, and prepares it for analysis.

[0211] Step 6:

[0212] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0213] Step 7:

[0214] The server analyzes the user's emotional state data and takes into account, for example, if the user is feeling fear or anxiety, prioritizing the selection of a route that provides psychological security.

[0215] Step 8:

[0216] Users enter their personal information (e.g., wheelchair user) within the application, which is then sent to the server and used to respond to their individual needs.

[0217] Step 9:

[0218] The server combines the estimated road surface conditions with the user's attribute information and emotional state information to generate the optimal route for the user. For example, it may prioritize a route that avoids steps and stairs for wheelchair users, or provide an easy evacuation route that provides psychological security for users who are feeling anxious.

[0219] Step 10:

[0220] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0221] Step 11:

[0222] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0223] Step 12:

[0224] Users can evacuate safely and securely by following the optimal route displayed on the map, while referring to route information and emotional state updated in real time.

[0225] Example 2

[0226] 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."

[0227] Existing evacuation support systems focus on the user's physical state (e.g., road surface condition and user attributes), but they have the problem of not being able to consider the user's emotional state. Particularly during disasters, users' anxiety and fear have a strong influence on evacuation behavior, so comprehensive support that includes their emotional state is required.

[0228] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This enables evacuation support that takes the user's emotional state into consideration.

[0229] "User" refers to the entity that uses the system to receive evacuation assistance.

[0230] "Acceleration data" refers to data obtained from an acceleration sensor built into the user's device.

[0231] The "receiving means" refers to a communication means for inputting the acceleration data sent from the user's terminal into the server.

[0232] The "analyzing means" refers to an algorithm and its execution environment for analyzing the received acceleration data and estimating the road surface condition.

[0233] "Road surface conditions" refers to the condition of the ground, such as flat ground, steps, slopes, and stairs.

[0234] "Means for estimating" refers to technology for estimating road surface conditions based on acceleration data.

[0235] "Emotional state information" refers to data that indicates the user's emotional state, including states such as fear, anxiety, relief, etc.

[0236] "User attribute information" refers to data that indicates the physical characteristics of a user, including whether the user is a pedestrian, a wheelchair user, a cyclist, etc.

[0237] The "optimal route" refers to the evacuation route that is most suitable for the user, which is generated based on the user's attribute information and emotional state information.

[0238] "Means for generating" refers to methods and systems for calculating an optimal route based on road surface condition information and emotional state information.

[0239] "User terminal" refers to a mobile terminal such as a smartphone or tablet used by a user.

[0240] The "means for transmitting" refers to a communication method and device for transmitting the generated optimal route information from the server to the user terminal.

[0241] The present invention relates to a system that analyzes acceleration data collected from a user and estimates road surface conditions. The system aims to provide an optimal evacuation route that takes into account the user's emotional state. Specific embodiments are described below.

[0242] First, the user launches the evacuation support application installed on their smartphone. The user then enters their own attribute information (e.g., wheelchair user) into the application. The device detects that the application has been launched and activates its built-in acceleration sensor. This causes the device to start collecting acceleration data and send it to the server at regular intervals.

[0243] The server analyzes the received acceleration data in real time using an algorithm that distinguishes high-frequency vibrations from stairs and gradual fluctuations from slopes. For example, the server analyzes the amplitude and period of the data to estimate the current road surface condition with high accuracy.

[0244] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotional state. The user's device collects facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The emotion engine analyzes this data, estimates the user's emotional state (e.g., fear, anxiety, relief), and sends the information to the server.

[0245] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. For example, if a wheelchair user feels anxious, the server will prioritize providing a safe route that avoids steps and stairs. The generated optimal route information is sent from the server to the user's device.

[0246] The user's device displays the received optimal route information on a map. The user can check the evacuation route on the map in real time. If the user's emotional state or evacuation situation changes, the device collects new emotional data and sends it again to the emotion engine. The server recalculates the optimal route based on the user's latest state and updates it in real time. The user can continue to evacuate safely based on this information.

[0247] As a concrete example, consider a situation in which a user begins evacuation after an earthquake. The user launches a smartphone application and enters attribute information. The device collects data using an acceleration sensor and sends it to a server. The server analyzes this data and estimates the current road surface condition with high accuracy. Furthermore, the emotion engine detects whether the user is feeling anxious and sends this information to the server. The server then generates a safe route that avoids steps and stairs and sends it to the device. The user can then begin evacuation safely based on the displayed route.

[0248] Example prompt sentence:

[0249] After an earthquake, the user launches the smartphone application. The acceleration sensor collects data and sends it to the server. The server analyzes the data and estimates road surface conditions. The emotion engine detects that the user is feeling anxious and sends route information to the device, allowing the user to evacuate safely.

[0250] In this way, the system of the present invention can provide the optimal evacuation route in real time, taking into account the user's emotional state and physical attribute information, thereby realizing support for a quick and safe evacuation.

[0251] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0252] Step 1:

[0253] The user launches the evacuation support application installed on their smartphone. The input is the user's operation, and the output is the application's running status. This prepares the system for evacuation support.

[0254] Step 2:

[0255] The device detects the application launch and activates the built-in acceleration sensor. The input is the application launch detection signal, and the output is the activation of the acceleration sensor. The device uses this to start collecting acceleration data.

[0256] Step 3:

[0257] The device starts collecting acceleration data and sends it to the server at regular intervals. The input is data from the acceleration sensor, and the output is the acceleration data sent to the server. Specifically, the acceleration data is serialized and sent to the server via an HTTP POST request.

[0258] Step 4:

[0259] The server analyzes the received acceleration data in real time. The input is the acceleration data sent from the device, and the output is an estimation of the road surface condition. Specifically, an algorithm is used to distinguish high-frequency vibrations as stairs and gradual fluctuations as slopes, and the amplitude and period of the data are analyzed to determine the road surface condition.

[0260] Step 5:

[0261] The device collects the user's facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The input is biometric data obtained from the camera and microphone, and the output is data for estimating the user's emotions, which is sent to the emotion engine. Specifically, the device appropriately formats the collected data and sends it to the emotion engine.

[0262] Step 6:

[0263] The emotion engine estimates the user's emotional state (e.g., fear, anxiety, relief) and sends that information to the server. The input is the user's biometric data, and the output is estimated emotional state information. Specifically, it analyzes the biometric data, classifies the emotional state using an emotion model, and sends the information to the server.

[0264] Step 7:

[0265] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. The inputs are the user's attribute information, emotional state information, and road surface condition information, and the output is the optimal evacuation route information. Specifically, this information is integrated and an algorithm is applied to calculate the optimal route.

[0266] Step 8:

[0267] The server sends the generated optimal route information to the user's device. The input is the calculated optimal route information, and the output is the route information to be sent to the device. Specifically, the route information is serialized in JSON format and sent to the device via an HTTP POST request.

[0268] Step 9:

[0269] The user's device receives the optimal route information and displays it on a map. The input is the route information sent from the server, and the output is the route displayed in the map app. Specifically, the received route data is drawn on the map using the map API.

[0270] Step 10:

[0271] If the emotional state or evacuation situation changes, the device collects new emotional data and sends it to the emotion engine again. The input is new biometric data, and the output is updated emotional state information. This allows the server to recalculate based on the latest user information.

[0272] (Application example 2)

[0273] 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."

[0274] Conventional evacuation systems mainly generate evacuation routes based on the user's physical attribute information, but because they do not take into account the user's emotional state, they are unable to alleviate psychological anxiety during disasters. Furthermore, there is a lack of a means to provide optimal routes that reflect passengers' emotions in real time, especially when using autonomous vehicles.

[0275] The identification process by the identification 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 receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This makes it possible to provide a safer and more secure evacuation route that takes the emotional state into consideration.

[0276] "Acceleration data" is data that indicates the movement and speed changes of an object, collected by sensors on the user's device.

[0277] "Road surface conditions" refer to the physical characteristics of the travel route, such as flat ground, steps, slopes, and stairs.

[0278] The "emotional state" is a state of mind estimated based on biometric information such as the user's facial expression and voice, and includes emotions such as anxiety and fear.

[0279] "User attribute information" is information related to the physical characteristics and mode of transportation (e.g., pedestrian, wheelchair user, cyclist) entered by the user.

[0280] The "optimal route" is the best route that allows the user to evacuate safely and efficiently, calculated based on the collected data.

[0281] A "terminal" is an information processing device used by a user, and includes mobile devices such as smartphones equipped with a sensitivity sensor, a camera, and the like.

[0282] A "server" is a computer system that processes data on a network and sends analysis results and information to terminals.

[0283] The present invention has a configuration in which a user terminal, a server, and an autonomous vehicle system operate in cooperation with each other. A specific embodiment of this system will be described below.

[0284] System configuration

[0285] Hardware and Software

[0286] 1. User device: A smartphone is used. It is equipped with an accelerometer, camera, and microphone, and is used to collect acceleration data and emotion data.

[0287] 2. Server: A computer system that receives and analyzes data. It processes data using Python, OpenCV, and specific libraries (AccelerometerSensor, EmotionEngine).

[0288] 3. Autonomous vehicle systems: receive route information in real time and use it to control the vehicle.

[0289] Program processing

[0290] Acceleration data collection

[0291] The acceleration sensor on the user device (smartphone) is used to collect vehicle vibration data. This is done by an application running on the device. For example, the AccelerometerSensor library is used to obtain acceleration data in real time.

[0292] Recognition of emotional states

[0293] Using the camera and microphone on the user's device, the system recognizes the user's emotional state from their facial expressions and voice. Here, OpenCV and the EmotionEngine library are used to analyze image and audio data and estimate the user's emotions.

[0294] Data transmission and analysis

[0295] The collected acceleration data and emotion data are sent to a server at regular intervals. The server receives this data and analyzes it in real time. The acceleration data is used to estimate road surface conditions, and the emotion data is used to estimate the user's emotional state. The results are then combined to generate the optimal route.

[0296] Generate optimal routes

[0297] The server calculates the optimal evacuation route based on the estimated road surface condition information, emotional state information, and user attribute information (e.g., wheelchair user, pedestrian, cyclist). If the user is feeling particularly anxious, the server prioritizes generating a safe and secure route.

[0298] Route information presentation

[0299] The generated optimal route information is sent from the server to the user terminal and also to the autonomous vehicle system, which controls the vehicle to follow the route.

[0300] Specific examples

[0301] For example, consider a situation where a user begins to evacuate after an earthquake. The user launches an application on their smartphone, which collects data using the accelerometer and camera. The acceleration and emotion data are sent to a server, which analyzes the data and generates an optimal evacuation route. An autonomous vehicle follows the route and assists the user in a safe evacuation.

[0302] Prompt Sentence Examples

[0303] Instructions: Provide optimal routes for passengers in an autonomous vehicle to evacuate after an earthquake disaster. Use accelerometer and camera data and consider the passenger's emotional state. If the passenger feels anxious, prioritize the safe route.

[0304] This invention can provide a safer and more secure evacuation route by taking into consideration the emotional state of the user.

[0305] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0306] Step 1:

[0307] The user launches the smartphone application, which prepares for evacuation.

[0308] Step 2:

[0309] The device (smartphone) enables the accelerometer sensor and starts collecting acceleration data. Specifically, the device continuously acquires acceleration data using the AccelerometerSensor library. The input is data from the device's accelerometer sensor, and the output is the acquired acceleration data.

[0310] Step 3:

[0311] The device uses a camera and microphone to collect the user's facial expressions and voice to recognize their emotional state. Here, the OpenCV library is used to analyze image data, and the EmotionEngine library is used to estimate emotions. The input is video and audio data from the camera and microphone, and the output is the estimated emotional state.

[0312] Step 4:

[0313] The collected acceleration data and emotion data are sent to a server at regular intervals. The input is the acceleration data and emotion data, and the output is the result of the data transmission to the server.

[0314] Step 5:

[0315] The server analyzes the received acceleration data and estimates road surface conditions. Specifically, it identifies road surface conditions such as flat ground, steps, slopes, and stairs in real time based on the fluctuation patterns of the acceleration data. The input is acceleration data, and the output is estimated road surface condition information.

[0316] Step 6:

[0317] The server analyzes the received emotion data and estimates the user's emotional state. Specifically, it uses the EmotionEngine to recognize emotions from facial expressions and voice data. The input is emotion data, and the output is the estimated emotional state.

[0318] Step 7:

[0319] The server generates the optimal route based on the estimated road surface condition information, emotional state information, and user attribute information (previously entered information such as whether the user is a wheelchair user, pedestrian, or cyclist). The input is road surface condition information, emotional state information, and attribute information, and the output is the generated optimal route information.

[0320] Step 8:

[0321] The generated optimal route information is sent from the server to the user terminal. It is also sent simultaneously to the autonomous vehicle system. The input is the optimal route information, and the output is the sent route information.

[0322] Step 9:

[0323] The user device displays the received optimal route information on a map, allowing the user to check the optimal evacuation route in real time. Specifically, the route is visually displayed using a map application. The input is the received route information, and the output is the route display on the map.

[0324] Step 10:

[0325] The autonomous vehicle system controls the vehicle along the received optimal route and evacuates the user. The input is the optimal route information, and the output is the vehicle's driving path.

[0326] This system provides the optimal evacuation route in real time, taking into account the user's emotional state, enabling a quick and safe evacuation.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] [Second embodiment]

[0331] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0332] 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.

[0333] 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).

[0334] 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.

[0335] 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.

[0336] 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).

[0337] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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."

[0343] The present invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road surface conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0344] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and starts collecting acceleration data. The collected acceleration data is then periodically sent from the device to a server.

[0345] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to a slope. This allows the server to estimate the current road surface condition (flat ground, steps, slope, stairs, etc.) with high accuracy.

[0346] Next, the user enters their own attribute information (e.g., wheelchair user) into the application. This attribute information is sent from the device to the server, and the server uses this information to generate the optimal route. Specifically, the estimated road surface condition information is combined with the user's attribute information to calculate a route that meets the individual needs of wheelchair users, such as prioritizing routes that avoid steps and stairs.

[0347] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. This route information is updated every time the server receives and analyzes new acceleration data, reflecting the latest passable information.

[0348] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone. The smartphone uses an acceleration sensor to collect data and sends it to a server. The server analyzes the acceleration data and estimates the current road surface conditions. After that, when the user indicates within the app that they are a wheelchair user, the server generates an optimal route that avoids steps and stairs and sends that information to the user's device. The user can evacuate safely by relying on the route displayed on the map.

[0349] This system allows for quick and efficient evacuation, by providing the optimal evacuation route based on the user's attributes in real time, even in the event of a disaster.

[0350] The processing flow will be explained below.

[0351] Step 1:

[0352] The user launches the application installed on their smartphone, which prepares them to begin evacuation.

[0353] Step 2:

[0354] The device detects that the application has been launched and activates the smartphone's accelerometer, which starts collecting acceleration data about the user's movements.

[0355] Step 3:

[0356] The device sends the collected acceleration data at regular intervals (e.g., every second) to the server. This data includes a timestamp indicating the change in acceleration during movement and the device ID.

[0357] Step 4:

[0358] The server receives the acceleration data sent from the device, temporarily stores it in a database, and prepares it for analysis.

[0359] Step 5:

[0360] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0361] Step 6:

[0362] Users enter their demographic information (e.g., wheelchair user) within the application, which is important to address the specific needs of each individual user.

[0363] Step 7:

[0364] The device sends the attribute information entered by the user to the server, which then prepares to calculate the optimal route based on the user's attributes.

[0365] Step 8:

[0366] The server combines the estimated road surface conditions with the user's attribute information to generate the optimal route for the user. For example, it prioritizes routes that avoid steps and stairs for wheelchair users.

[0367] Step 9:

[0368] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0369] Step 10:

[0370] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0371] Step 11:

[0372] Users can evacuate safely by following the optimal route displayed on the map, with route information updated in real time.

[0373] Example 1

[0374] 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."

[0375] Conventional technologies have struggled to estimate road surface conditions in real time and provide optimal routes based on user attributes. This makes it particularly difficult to generate appropriate evacuation routes during disasters, as road surface conditions change rapidly. Furthermore, route calculations tailored to user attributes were also inadequate. This resulted in a lack of reliable information for safe and rapid evacuation, particularly for pedestrians and wheelchair users who experience frequent vibrations.

[0376] 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.

[0377] In this invention, the server includes means for periodically receiving acceleration data collected from a user, means for analyzing the received acceleration data in real time and estimating road surface conditions based on fluctuation patterns in the data, means for transmitting attribute information input by the user from a terminal to the server, means for generating an optimal route by combining the estimated road surface condition information and the user's attribute information, means for transmitting the generated optimal route information to the user terminal, and means for updating the route information each time the user terminal collects new acceleration data, thereby enabling the provision of an optimal route in real time according to the user's attributes.

[0378] "Acceleration data collected from the user" refers to acceleration information such as the user's movements and vibrations, which is acquired by the user's device using a built-in sensor.

[0379] "Receiving at regular intervals" means receiving data periodically at a certain interval.

[0380] "Analyzing in real time" means processing data immediately after receiving it and obtaining analysis results as current information.

[0381] The "data fluctuation pattern" refers to the tendency of changes over time in the collected acceleration data.

[0382] "Estimating road surface conditions" means determining the road surface conditions, such as flat ground, steps, slopes, and stairs, from the analysis results of acceleration data.

[0383] "Attribute information input by the user from the terminal" is information about the characteristics of the user (e.g., pedestrian, wheelchair user, etc.) that the user provides through the terminal.

[0384] "Generating an optimal route" means calculating the most suitable route for the user based on the estimated road surface conditions and the user's attribute information.

[0385] "Transmitting to a user terminal" means transferring calculated or generated information to a device held by the user via a network.

[0386] "Collecting new acceleration data" means re-obtaining acceleration information relating to the user's movements that has been updated over time.

[0387] "Updating route information" means reevaluating the optimal route based on newly acquired acceleration data and making changes as necessary.

[0388] This invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0389] Hardware and Software Configuration

[0390] The hardware used includes a smartphone (with an accelerometer) and a server, while the software includes an application installed on the smartphone and a data analysis algorithm running on the server.

[0391] Data collection

[0392] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and begins collecting acceleration data. Specifically, data collection begins the moment the user starts moving, recording minute vibrations and changes in tilt.

[0393] Sending data

[0394] The collected acceleration data is sent from the user's device to a server at regular intervals, using Wi-Fi or mobile data networks.

[0395] Data analysis

[0396] The server analyzes the received acceleration data in real time. The analysis algorithm identifies road surface conditions based on the fluctuation patterns of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface conditions (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0397] Entering user attribute information

[0398] Users input their attribute information (e.g., wheelchair user, visually impaired, etc.) within the application, and this information is sent from the user's device to the server.

[0399] Optimal route generation

[0400] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route. For example, for wheelchair users, it prioritizes routes that avoid steps and stairs. Based on this information, the server calculates the optimal route for each individual user.

[0401] Submitting and updating route information

[0402] The generated optimal route information is sent from the server to the user's device. The user's device displays this information on a map, allowing the user to check the optimal route in real time. The user's device also collects new acceleration data and sends it to the server, which then updates the route information in real time.

[0403] Specific examples

[0404] For example, consider a scenario in which an earthquake occurs and the user begins evacuation. The user launches an application installed on their smartphone and begins collecting data using the accelerometer. The collected data is sent from the device to a server, which analyzes the data and estimates the current road surface conditions. After that, if the user indicates within the app that they are a wheelchair user, the server generates an optimal evacuation route without steps or stairs and sends that information to the user's device. The user can then begin a safe evacuation by relying on the route displayed on the map.

[0405] Prompt Sentence Examples

[0406] "What is the best route to evacuate after an earthquake? I use a wheelchair."

[0407] "Please tell me the safest evacuation route from my current location. I would like to avoid stairs and steps."

[0408] This system will provide the optimal route based on the user's attributes in real time, even in the event of a disaster, enabling quick and safe evacuation.

[0409] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0410] Step 1:

[0411] The user launches an application installed on the smartphone.

[0412] Input: The user's action to launch the application.

[0413] Output: Application launch.

[0414] What happens: The user taps the application icon to launch the application.

[0415] Step 2:

[0416] The device will activate the smartphone's accelerometer and begin collecting acceleration data.

[0417] Input: The application startup state.

[0418] Output: Start collecting acceleration data.

[0419] Specific operation: After the application is launched, it automatically activates the smartphone's accelerometer and records acceleration data on the x-axis, y-axis, and z-axis in real time.

[0420] Step 3:

[0421] The device stores the collected acceleration data in a buffer at regular intervals and transmits it to the server.

[0422] Input: Collected acceleration data.

[0423] Output: Acceleration data sent to the server.

[0424] How it works: Every few seconds, the device collects data, stores it in a buffer, and then sends the data packets to the server via Wi-Fi or the mobile data network.

[0425] Step 4:

[0426] The server puts the received acceleration data into an analysis queue and starts analyzing it in real time.

[0427] Input: Acceleration data sent to the server.

[0428] Output: Estimated road surface conditions.

[0429] Specific operation: The server puts the received data into an analysis queue, applies a data analysis algorithm, and determines the road surface condition (flat ground, bumps, slopes, stairs, etc.) based on the fluctuation pattern of the data.

[0430] Step 5:

[0431] The user inputs their own attribute information within the application and sends it from the terminal to the server.

[0432] Input: User demographic information (e.g. wheelchair user, walking).

[0433] Output: The attribute information sent to the server.

[0434] Specific operation: The user accesses the application's settings screen and selects or enters their attribute information using drop-down menus and text boxes. This information is sent from the device to the server.

[0435] Step 6:

[0436] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route.

[0437] Input: Road surface condition information and user attribute information.

[0438] Output: Optimal route.

[0439] How it works: The server retrieves attribute information from the database, combines it with road surface condition data, and runs a route calculation algorithm to calculate the optimal route for individual needs, such as avoiding steps and stairs for wheelchair users.

[0440] Step 7:

[0441] The server transmits the generated optimal route information to the user terminal.

[0442] Input: Optimal route information.

[0443] Output: Route information sent to the user device.

[0444] Specific operation: The server compiles optimal route information into packets and sends them to the device. The device then displays the received route information in a map app or dedicated app.

[0445] Step 8:

[0446] Each time the user terminal collects new acceleration data, it updates the route information.

[0447] Input: New acceleration data.

[0448] Output: The updated route information.

[0449] What it does: The device sends newly collected acceleration data to the server. The server analyzes the new data and recalculates the route if necessary. The updated information is then sent back to the user's device, updating the route information on the map in real time.

[0450] In this way, the system can quickly respond to changes in the user's movements and environment, providing an always-up-to-date and safe route.

[0451] (Application example 1)

[0452] 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."

[0453] The present invention relates to the technical field of estimating road surface conditions by analyzing acceleration data collected from a user. In particular, it aims to provide a safe and comfortable route for an automated driving vehicle, and to solve the problem that it is difficult for current navigation systems to grasp road surface conditions in real time and to quickly change the route based on the detected road surface conditions.

[0454] 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.

[0455] In this invention, the server includes means for receiving acceleration data collected from a user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to user attribute information based on the estimated road surface condition information, means for transmitting the generated optimal route information to a user terminal, and means for controlling the vehicle's navigation system to automatically drive the vehicle based on the optimal route, thereby enabling fast and safe automatic driving based on real-time understanding of road surface conditions.

[0456] "User" refers to any person or entity that uses the system to provide acceleration data and receive optimized route information.

[0457] "Acceleration data" is information that indicates changes in movement measured by an acceleration sensor collected from a user's terminal or vehicle.

[0458] The "receiving means" is a communication means for inputting the acceleration data transmitted from the user's terminal into the server.

[0459] The "analyzing means" is a calculation means for processing the received acceleration data and estimating the road surface condition in real time.

[0460] "Road surface conditions" refers to the terrain and ground conditions, such as flat ground, steps, slopes, and stairs.

[0461] "Attribute information" is data that indicates the characteristics of a user (e.g., pedestrian, wheelchair user, bicycle user, self-driving vehicle user).

[0462] The "optimal route" is a safe and efficient route calculated based on estimated road surface condition information and user attribute information.

[0463] The "transmitting means" is a communication means for transferring the generated optimum route information to the user terminal.

[0464] A "vehicle navigation system" is a system that provides route information necessary for an autonomous vehicle to reach its destination and controls the vehicle's direction of travel and speed.

[0465] The present invention is a system that analyzes acceleration data collected from a user in real time, estimates road surface conditions, and provides an optimal route based on the user's attribute information. This system works in cooperation with the user's terminal, a server, and the vehicle's navigation system.

[0466] First, the user launches an application installed on their smartphone or vehicle-mounted device. The application activates the acceleration sensor and starts collecting acceleration data. The collected data is then periodically sent to a server.

[0467] The server uses a specific algorithm to analyze the received acceleration data in real time. This algorithm analyzes the fluctuation patterns of the acceleration data and estimates the road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy. For example, continuous high-frequency vibrations correspond to steps or stairs, while a gradual fluctuation pattern corresponds to a slope.

[0468] Next, the user enters their own attribute information (e.g., that they are a self-driving vehicle user) within the application. This attribute information is sent to the server, which then generates the optimal route based on this information. Specifically, the server prioritizes the safest and most comfortable route for the vehicle based on the estimated road surface conditions and the user's attribute information.

[0469] The generated optimal route information is sent from the server to the vehicle's navigation system. The navigation system controls the vehicle based on the received route information, providing autonomous driving functionality. The navigation system updates the route every time it receives new acceleration data, reflecting the latest road conditions and passability information.

[0470] Program processing

[0471] The user's terminal functions as a smartphone or a vehicle-mounted device and collects acceleration data using an acceleration sensor.

[0472] The server analyzes the acceleration data using programming languages ​​such as Python and R. Machine learning algorithms and data analysis libraries (e.g., scikit-learn, TensorFlow) are used for the analysis.

[0473] The vehicle's navigation system controls the vehicle based on the received route information. In addition to the usual navigation software, this system also has a function that reflects road surface conditions in real time.

[0474] Specific examples

[0475] For example, if a user is using a wheelchair-accessible autonomous vehicle, the user inputs attribute information into the application and sends road surface data via an acceleration sensor to the server. The server analyzes the data, generates the safest route for the wheelchair user, and sends it to the navigation system. The navigation system then controls the vehicle based on the received data, driving it to the destination along a route that avoids steps and stairs.

[0476] Prompt Sentence Examples

[0477] Design a system for a wheelchair-accessible autonomous vehicle that analyzes road conditions in real time based on acceleration data and provides the optimal route. Please include program code and detailed explanations for each step.

[0478] In this way, the present invention provides users of autonomous vehicles with optimal routes in real time, realizing a safe and comfortable driving environment.

[0479] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0480] Step 1:

[0481] The user's device collects acceleration data using an acceleration sensor. Specifically, the user starts up their smartphone or vehicle-mounted device and starts acquiring acceleration data within the application. The input here is data from the device's acceleration sensor, and the output is the collected acceleration data.

[0482] Step 2:

[0483] The user's device periodically sends the collected acceleration data to the server. Specifically, the device sends data to the server using an HTTP POST request. The input is the collected acceleration data, and the output is the data sent to the server.

[0484] Step 3:

[0485] The server analyzes the received acceleration data in real time. Specifically, it uses machine learning algorithms and data analysis libraries based on the received data to estimate road surface conditions such as flat ground, steps, slopes, and stairs. The input is the acceleration data received by the server, and the output is estimated road surface condition information.

[0486] Step 4:

[0487] The user inputs attribute information within the application. Specifically, a UI is provided for inputting attribute information such as whether the user is an autonomous vehicle user, a pedestrian, or a wheelchair user. The input here is the user's attribute information, and the output is the attribute information sent to the server.

[0488] Step 5:

[0489] The server generates the optimal route based on estimated road surface condition information and user attribute information. Specifically, it runs an algorithm that takes into account constraints based on road conditions and user attributes, analyzes map data, and calculates the optimal route. The input is estimated road surface condition information and attribute information, and the output is the generated optimal route information.

[0490] Step 6:

[0491] The server sends the generated optimal route information to the user's device. Specifically, an HTTP POST request is sent from the server to the user's device to transfer the route information. The input is the generated optimal route information, and the output is the optimal route information sent to the device.

[0492] Step 7:

[0493] The terminal provides the received optimal route information to the vehicle's navigation system. Specifically, the navigation software runs based on the received data and updates the vehicle's route guide. The input is the optimal route information sent to the terminal, and the output is the updated route information for the autonomous vehicle's navigation system.

[0494] Step 8:

[0495] The navigation system controls the vehicle's direction and speed based on the route information received. Specifically, it generates driving parameters according to real-time road conditions and controls various actuators in the vehicle. The input is the updated route information from the navigation system, and the output is the driving behavior of the autonomous vehicle.

[0496] 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.

[0497] The present invention is a system that provides more flexible optimal routes in the event of a disaster by combining a system that collects and analyzes acceleration data from users with an emotion engine that recognizes the emotional state of the user. This system operates in cooperation with the user's terminal, server, and emotion engine.

[0498] First, the user launches the application installed on their smartphone, which prepares them to begin evacuation. The device detects the launch of the application and activates the smartphone's accelerometer to begin collecting acceleration data. The collected acceleration data is then sent from the device to a server at regular intervals.

[0499] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. Specifically, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0500] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses the user's facial recognition data, voice data, or other biometric information to estimate the user's emotional state. If the user is feeling fear or anxiety, the emotion engine will detect this and send the information to the server.

[0501] The user inputs their attribute information (e.g., wheelchair user) within the application. Both this attribute information and emotional state information are sent from the device to the server. The server then generates the optimal route based on this information. For example, if the user is a wheelchair user and feels anxious, the server will prioritize routes that are as easy and safe as possible to evacuate.

[0502] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. If the user's emotional state changes, the server can recalculate a new route and update it in real time.

[0503] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone, which collects data using an acceleration sensor. The acceleration data is sent to a server, which analyzes it and estimates the current road surface conditions. If the user is a wheelchair user and the emotion engine detects anxiety, the server will prioritize generating a safe and easy route that avoids steps and stairs and send it to the device. The user can evacuate safely by relying on the route displayed on the map.

[0504] This system provides optimal evacuation routes in real time, taking into account the user's emotional state during a disaster, enabling quick and safe evacuation.

[0505] The processing flow will be explained below.

[0506] Step 1:

[0507] The user launches the application installed on their smartphone, which activates the emotion engine and accelerometer.

[0508] Step 2:

[0509] The device detects that the application has been launched and activates the smartphone's camera, microphone, and accelerometer, thereby starting to collect facial recognition data, voice data, and acceleration data.

[0510] Step 3:

[0511] The device processes facial recognition data and voice data collected in real time and uses an emotion engine to estimate the user's emotional state, for example, determining whether the user is feeling fear or anxiety based on their facial expression.

[0512] Step 4:

[0513] The device transmits the collected acceleration data and emotional state data to the server at regular intervals (e.g., every second). This data includes acceleration changes during movement, a timestamp, the device ID, and the emotional state.

[0514] Step 5:

[0515] The server receives the acceleration data and emotional state data sent from the device, temporarily stores the data in a database, and prepares it for analysis.

[0516] Step 6:

[0517] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0518] Step 7:

[0519] The server analyzes the user's emotional state data and takes into account, for example, if the user is feeling fear or anxiety, prioritizing the selection of a route that provides psychological security.

[0520] Step 8:

[0521] Users enter their personal information (e.g., wheelchair user) within the application, which is then sent to the server and used to respond to their individual needs.

[0522] Step 9:

[0523] The server combines the estimated road surface conditions with the user's attribute information and emotional state information to generate the optimal route for the user. For example, it may prioritize a route that avoids steps and stairs for wheelchair users, or provide an easy evacuation route that provides psychological security for users who are feeling anxious.

[0524] Step 10:

[0525] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0526] Step 11:

[0527] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0528] Step 12:

[0529] Users can evacuate safely and securely by following the optimal route displayed on the map, while referring to route information and emotional state updated in real time.

[0530] Example 2

[0531] 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."

[0532] Existing evacuation support systems focus on the user's physical state (e.g., road surface condition and user attributes), but they have the problem of not being able to consider the user's emotional state. Particularly during disasters, users' anxiety and fear have a strong influence on evacuation behavior, so comprehensive support that includes their emotional state is required.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This enables evacuation support that takes the user's emotional state into consideration.

[0534] "User" refers to the entity that uses the system to receive evacuation assistance.

[0535] "Acceleration data" refers to data obtained from an acceleration sensor built into the user's device.

[0536] The "receiving means" refers to a communication means for inputting the acceleration data sent from the user's terminal into the server.

[0537] The "analyzing means" refers to an algorithm and its execution environment for analyzing the received acceleration data and estimating the road surface condition.

[0538] "Road surface conditions" refers to the condition of the ground, such as flat ground, steps, slopes, and stairs.

[0539] "Means for estimating" refers to technology for estimating road surface conditions based on acceleration data.

[0540] "Emotional state information" refers to data that indicates the user's emotional state, including states such as fear, anxiety, relief, etc.

[0541] "User attribute information" refers to data that indicates the physical characteristics of a user, including whether the user is a pedestrian, a wheelchair user, a cyclist, etc.

[0542] The "optimal route" refers to the evacuation route that is most suitable for the user, which is generated based on the user's attribute information and emotional state information.

[0543] "Means for generating" refers to methods and systems for calculating an optimal route based on road surface condition information and emotional state information.

[0544] "User terminal" refers to a mobile terminal such as a smartphone or tablet used by a user.

[0545] The "means for transmitting" refers to a communication method and device for transmitting the generated optimal route information from the server to the user terminal.

[0546] The present invention relates to a system that analyzes acceleration data collected from a user and estimates road surface conditions. The system aims to provide an optimal evacuation route that takes into account the user's emotional state. Specific embodiments are described below.

[0547] First, the user launches the evacuation support application installed on their smartphone. The user then enters their own attribute information (e.g., wheelchair user) into the application. The device detects that the application has been launched and activates its built-in acceleration sensor. This causes the device to start collecting acceleration data and send it to the server at regular intervals.

[0548] The server analyzes the received acceleration data in real time using an algorithm that distinguishes high-frequency vibrations from stairs and gradual fluctuations from slopes. For example, the server analyzes the amplitude and period of the data to estimate the current road surface condition with high accuracy.

[0549] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotional state. The user's device collects facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The emotion engine analyzes this data, estimates the user's emotional state (e.g., fear, anxiety, relief), and sends the information to the server.

[0550] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. For example, if a wheelchair user feels anxious, the server will prioritize providing a safe route that avoids steps and stairs. The generated optimal route information is sent from the server to the user's device.

[0551] The user's device displays the received optimal route information on a map. The user can check the evacuation route on the map in real time. If the user's emotional state or evacuation situation changes, the device collects new emotional data and sends it again to the emotion engine. The server recalculates the optimal route based on the user's latest state and updates it in real time. The user can continue to evacuate safely based on this information.

[0552] As a concrete example, consider a situation in which a user begins evacuation after an earthquake. The user launches a smartphone application and enters attribute information. The device collects data using an acceleration sensor and sends it to a server. The server analyzes this data and estimates the current road surface condition with high accuracy. Furthermore, the emotion engine detects whether the user is feeling anxious and sends this information to the server. The server then generates a safe route that avoids steps and stairs and sends it to the device. The user can then begin evacuation safely based on the displayed route.

[0553] Example prompt sentence:

[0554] After an earthquake, the user launches the smartphone application. The acceleration sensor collects data and sends it to the server. The server analyzes the data and estimates road surface conditions. The emotion engine detects that the user is feeling anxious and sends route information to the device, allowing the user to evacuate safely.

[0555] In this way, the system of the present invention can provide the optimal evacuation route in real time, taking into account the user's emotional state and physical attribute information, thereby realizing support for a quick and safe evacuation.

[0556] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0557] Step 1:

[0558] The user launches the evacuation support application installed on their smartphone. The input is the user's operation, and the output is the application's running status. This prepares the system for evacuation support.

[0559] Step 2:

[0560] The device detects the application launch and activates the built-in acceleration sensor. The input is the application launch detection signal, and the output is the activation of the acceleration sensor. The device uses this to start collecting acceleration data.

[0561] Step 3:

[0562] The device starts collecting acceleration data and sends it to the server at regular intervals. The input is data from the acceleration sensor, and the output is the acceleration data sent to the server. Specifically, the acceleration data is serialized and sent to the server via an HTTP POST request.

[0563] Step 4:

[0564] The server analyzes the received acceleration data in real time. The input is the acceleration data sent from the device, and the output is an estimation of the road surface condition. Specifically, an algorithm is used to distinguish high-frequency vibrations as stairs and gradual fluctuations as slopes, and the amplitude and period of the data are analyzed to determine the road surface condition.

[0565] Step 5:

[0566] The device collects the user's facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The input is biometric data obtained from the camera and microphone, and the output is data for estimating the user's emotions, which is sent to the emotion engine. Specifically, the device appropriately formats the collected data and sends it to the emotion engine.

[0567] Step 6:

[0568] The emotion engine estimates the user's emotional state (e.g., fear, anxiety, relief) and sends that information to the server. The input is the user's biometric data, and the output is estimated emotional state information. Specifically, it analyzes the biometric data, classifies the emotional state using an emotion model, and sends the information to the server.

[0569] Step 7:

[0570] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. The inputs are the user's attribute information, emotional state information, and road surface condition information, and the output is the optimal evacuation route information. Specifically, this information is integrated and an algorithm is applied to calculate the optimal route.

[0571] Step 8:

[0572] The server sends the generated optimal route information to the user's device. The input is the calculated optimal route information, and the output is the route information to be sent to the device. Specifically, the route information is serialized in JSON format and sent to the device via an HTTP POST request.

[0573] Step 9:

[0574] The user's device receives the optimal route information and displays it on a map. The input is the route information sent from the server, and the output is the route displayed in the map app. Specifically, the received route data is drawn on the map using the map API.

[0575] Step 10:

[0576] If the emotional state or evacuation situation changes, the device collects new emotional data and sends it to the emotion engine again. The input is new biometric data, and the output is updated emotional state information. This allows the server to recalculate based on the latest user information.

[0577] (Application example 2)

[0578] 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."

[0579] Conventional evacuation systems mainly generate evacuation routes based on the user's physical attribute information, but because they do not take into account the user's emotional state, they are unable to alleviate psychological anxiety during disasters. Furthermore, there is a lack of a means to provide optimal routes that reflect passengers' emotions in real time, especially when using autonomous vehicles.

[0580] The identification process by the identification 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 receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This makes it possible to provide a safer and more secure evacuation route that takes the emotional state into consideration.

[0581] "Acceleration data" is data that indicates the movement and speed changes of an object, collected by sensors on the user's device.

[0582] "Road surface conditions" refer to the physical characteristics of the travel route, such as flat ground, steps, slopes, and stairs.

[0583] The "emotional state" is a state of mind estimated based on biometric information such as the user's facial expression and voice, and includes emotions such as anxiety and fear.

[0584] "User attribute information" is information related to the physical characteristics and mode of transportation (e.g., pedestrian, wheelchair user, cyclist) entered by the user.

[0585] The "optimal route" is the best route that allows the user to evacuate safely and efficiently, calculated based on the collected data.

[0586] A "terminal" is an information processing device used by a user, and includes mobile devices such as smartphones equipped with a sensitivity sensor, a camera, and the like.

[0587] A "server" is a computer system that processes data on a network and sends analysis results and information to terminals.

[0588] The present invention has a configuration in which a user terminal, a server, and an autonomous vehicle system operate in cooperation with each other. A specific embodiment of this system will be described below.

[0589] System configuration

[0590] Hardware and Software

[0591] 1. User device: A smartphone is used. It is equipped with an accelerometer, camera, and microphone, and is used to collect acceleration data and emotion data.

[0592] 2. Server: A computer system that receives and analyzes data. It processes data using Python, OpenCV, and specific libraries (AccelerometerSensor, EmotionEngine).

[0593] 3. Autonomous vehicle systems: receive route information in real time and use it to control the vehicle.

[0594] Program processing

[0595] Acceleration data collection

[0596] The acceleration sensor on the user device (smartphone) is used to collect vehicle vibration data. This is done by an application running on the device. For example, the AccelerometerSensor library is used to obtain acceleration data in real time.

[0597] Recognition of emotional states

[0598] Using the camera and microphone on the user's device, the system recognizes the user's emotional state from their facial expressions and voice. Here, OpenCV and the EmotionEngine library are used to analyze image and audio data and estimate the user's emotions.

[0599] Data transmission and analysis

[0600] The collected acceleration data and emotion data are sent to a server at regular intervals. The server receives this data and analyzes it in real time. The acceleration data is used to estimate road surface conditions, and the emotion data is used to estimate the user's emotional state. The results are then combined to generate the optimal route.

[0601] Generate optimal routes

[0602] The server calculates the optimal evacuation route based on the estimated road surface condition information, emotional state information, and user attribute information (e.g., wheelchair user, pedestrian, cyclist). If the user is feeling particularly anxious, the server prioritizes generating a safe and secure route.

[0603] Route information presentation

[0604] The generated optimal route information is sent from the server to the user terminal and also to the autonomous vehicle system, which controls the vehicle to follow the route.

[0605] Specific examples

[0606] For example, consider a situation where a user begins to evacuate after an earthquake. The user launches an application on their smartphone, which collects data using the accelerometer and camera. The acceleration and emotion data are sent to a server, which analyzes the data and generates an optimal evacuation route. An autonomous vehicle follows the route and assists the user in a safe evacuation.

[0607] Prompt Sentence Examples

[0608] Instructions: Provide optimal routes for passengers in an autonomous vehicle to evacuate after an earthquake disaster. Use accelerometer and camera data and consider the passenger's emotional state. If the passenger feels anxious, prioritize the safe route.

[0609] This invention can provide a safer and more secure evacuation route by taking into consideration the emotional state of the user.

[0610] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0611] Step 1:

[0612] The user launches the smartphone application, which prepares for evacuation.

[0613] Step 2:

[0614] The device (smartphone) enables the accelerometer sensor and starts collecting acceleration data. Specifically, the device continuously acquires acceleration data using the AccelerometerSensor library. The input is data from the device's accelerometer sensor, and the output is the acquired acceleration data.

[0615] Step 3:

[0616] The device uses a camera and microphone to collect the user's facial expressions and voice to recognize their emotional state. Here, the OpenCV library is used to analyze image data, and the EmotionEngine library is used to estimate emotions. The input is video and audio data from the camera and microphone, and the output is the estimated emotional state.

[0617] Step 4:

[0618] The collected acceleration data and emotion data are sent to a server at regular intervals. The input is the acceleration data and emotion data, and the output is the result of the data transmission to the server.

[0619] Step 5:

[0620] The server analyzes the received acceleration data and estimates road surface conditions. Specifically, it identifies road surface conditions such as flat ground, steps, slopes, and stairs in real time based on the fluctuation patterns of the acceleration data. The input is acceleration data, and the output is estimated road surface condition information.

[0621] Step 6:

[0622] The server analyzes the received emotion data and estimates the user's emotional state. Specifically, it uses the EmotionEngine to recognize emotions from facial expressions and voice data. The input is emotion data, and the output is the estimated emotional state.

[0623] Step 7:

[0624] The server generates the optimal route based on the estimated road surface condition information, emotional state information, and user attribute information (previously entered information such as whether the user is a wheelchair user, pedestrian, or cyclist). The input is road surface condition information, emotional state information, and attribute information, and the output is the generated optimal route information.

[0625] Step 8:

[0626] The generated optimal route information is sent from the server to the user terminal. It is also sent simultaneously to the autonomous vehicle system. The input is the optimal route information, and the output is the sent route information.

[0627] Step 9:

[0628] The user device displays the received optimal route information on a map, allowing the user to check the optimal evacuation route in real time. Specifically, the route is visually displayed using a map application. The input is the received route information, and the output is the route display on the map.

[0629] Step 10:

[0630] The autonomous vehicle system controls the vehicle along the received optimal route and evacuates the user. The input is the optimal route information, and the output is the vehicle's driving path.

[0631] This system provides the optimal evacuation route in real time, taking into account the user's emotional state, enabling a quick and safe evacuation.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] [Third embodiment]

[0636] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0637] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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).

[0642] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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."

[0648] The present invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road surface conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0649] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and starts collecting acceleration data. The collected acceleration data is then periodically sent from the device to a server.

[0650] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to a slope. This allows the server to estimate the current road surface condition (flat ground, steps, slope, stairs, etc.) with high accuracy.

[0651] Next, the user enters their own attribute information (e.g., wheelchair user) into the application. This attribute information is sent from the device to the server, and the server uses this information to generate the optimal route. Specifically, the estimated road surface condition information is combined with the user's attribute information to calculate a route that meets the individual needs of wheelchair users, such as prioritizing routes that avoid steps and stairs.

[0652] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. This route information is updated every time the server receives and analyzes new acceleration data, reflecting the latest passable information.

[0653] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone. The smartphone uses an acceleration sensor to collect data and sends it to a server. The server analyzes the acceleration data and estimates the current road surface conditions. After that, when the user indicates within the app that they are a wheelchair user, the server generates an optimal route that avoids steps and stairs and sends that information to the user's device. The user can evacuate safely by relying on the route displayed on the map.

[0654] This system allows for quick and efficient evacuation, by providing the optimal evacuation route based on the user's attributes in real time, even in the event of a disaster.

[0655] The processing flow will be explained below.

[0656] Step 1:

[0657] The user launches the application installed on their smartphone, which prepares them to begin evacuation.

[0658] Step 2:

[0659] The device detects that the application has been launched and activates the smartphone's accelerometer, which starts collecting acceleration data about the user's movements.

[0660] Step 3:

[0661] The device sends the collected acceleration data at regular intervals (e.g., every second) to the server. This data includes a timestamp indicating the change in acceleration during movement and the device ID.

[0662] Step 4:

[0663] The server receives the acceleration data sent from the device, temporarily stores it in a database, and prepares it for analysis.

[0664] Step 5:

[0665] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0666] Step 6:

[0667] Users enter their demographic information (e.g., wheelchair user) within the application, which is important to address the specific needs of each individual user.

[0668] Step 7:

[0669] The device sends the attribute information entered by the user to the server, which then prepares to calculate the optimal route based on the user's attributes.

[0670] Step 8:

[0671] The server combines the estimated road surface conditions with the user's attribute information to generate the optimal route for the user. For example, it prioritizes routes that avoid steps and stairs for wheelchair users.

[0672] Step 9:

[0673] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0674] Step 10:

[0675] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0676] Step 11:

[0677] Users can evacuate safely by following the optimal route displayed on the map, with route information updated in real time.

[0678] Example 1

[0679] 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."

[0680] Conventional technologies have struggled to estimate road surface conditions in real time and provide optimal routes based on user attributes. This makes it particularly difficult to generate appropriate evacuation routes during disasters, as road surface conditions change rapidly. Furthermore, route calculations tailored to user attributes were also inadequate. This resulted in a lack of reliable information for safe and rapid evacuation, particularly for pedestrians and wheelchair users who experience frequent vibrations.

[0681] 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.

[0682] In this invention, the server includes means for periodically receiving acceleration data collected from a user, means for analyzing the received acceleration data in real time and estimating road surface conditions based on fluctuation patterns in the data, means for transmitting attribute information input by the user from a terminal to the server, means for generating an optimal route by combining the estimated road surface condition information and the user's attribute information, means for transmitting the generated optimal route information to the user terminal, and means for updating the route information each time the user terminal collects new acceleration data, thereby enabling the provision of an optimal route in real time according to the user's attributes.

[0683] "Acceleration data collected from the user" refers to acceleration information such as the user's movements and vibrations, which is acquired by the user's device using a built-in sensor.

[0684] "Receiving at regular intervals" means receiving data periodically at a certain interval.

[0685] "Analyzing in real time" means processing data immediately after receiving it and obtaining analysis results as current information.

[0686] The "data fluctuation pattern" refers to the tendency of changes over time in the collected acceleration data.

[0687] "Estimating road surface conditions" means determining the road surface conditions, such as flat ground, steps, slopes, and stairs, from the analysis results of acceleration data.

[0688] "Attribute information input by the user from the terminal" is information about the characteristics of the user (e.g., pedestrian, wheelchair user, etc.) that the user provides through the terminal.

[0689] "Generating an optimal route" means calculating the most suitable route for the user based on the estimated road surface conditions and the user's attribute information.

[0690] "Transmitting to a user terminal" means transferring calculated or generated information to a device held by the user via a network.

[0691] "Collecting new acceleration data" means re-obtaining acceleration information relating to the user's movements that has been updated over time.

[0692] "Updating route information" means reevaluating the optimal route based on newly acquired acceleration data and making changes as necessary.

[0693] This invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0694] Hardware and Software Configuration

[0695] The hardware used includes a smartphone (with an accelerometer) and a server, while the software includes an application installed on the smartphone and a data analysis algorithm running on the server.

[0696] Data collection

[0697] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and begins collecting acceleration data. Specifically, data collection begins the moment the user starts moving, recording minute vibrations and changes in tilt.

[0698] Sending data

[0699] The collected acceleration data is sent from the user's device to a server at regular intervals, using Wi-Fi or mobile data networks.

[0700] Data analysis

[0701] The server analyzes the received acceleration data in real time. The analysis algorithm identifies road surface conditions based on the fluctuation patterns of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface conditions (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0702] Entering user attribute information

[0703] Users input their attribute information (e.g., wheelchair user, visually impaired, etc.) within the application, and this information is sent from the user's device to the server.

[0704] Optimal route generation

[0705] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route. For example, for wheelchair users, it prioritizes routes that avoid steps and stairs. Based on this information, the server calculates the optimal route for each individual user.

[0706] Submitting and updating route information

[0707] The generated optimal route information is sent from the server to the user's device. The user's device displays this information on a map, allowing the user to check the optimal route in real time. The user's device also collects new acceleration data and sends it to the server, which then updates the route information in real time.

[0708] Specific examples

[0709] For example, consider a scenario in which an earthquake occurs and the user begins evacuation. The user launches an application installed on their smartphone and begins collecting data using the accelerometer. The collected data is sent from the device to a server, which analyzes the data and estimates the current road surface conditions. After that, if the user indicates within the app that they are a wheelchair user, the server generates an optimal evacuation route without steps or stairs and sends that information to the user's device. The user can then begin a safe evacuation by relying on the route displayed on the map.

[0710] Prompt Sentence Examples

[0711] "What is the best route to evacuate after an earthquake? I use a wheelchair."

[0712] "Please tell me the safest evacuation route from my current location. I would like to avoid stairs and steps."

[0713] This system will provide the optimal route based on the user's attributes in real time, even in the event of a disaster, enabling quick and safe evacuation.

[0714] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0715] Step 1:

[0716] The user launches an application installed on the smartphone.

[0717] Input: The user's action to launch the application.

[0718] Output: Application launch.

[0719] What happens: The user taps the application icon to launch the application.

[0720] Step 2:

[0721] The device will activate the smartphone's accelerometer and begin collecting acceleration data.

[0722] Input: The application startup state.

[0723] Output: Start collecting acceleration data.

[0724] Specific operation: After the application is launched, it automatically activates the smartphone's accelerometer and records acceleration data on the x-axis, y-axis, and z-axis in real time.

[0725] Step 3:

[0726] The device stores the collected acceleration data in a buffer at regular intervals and transmits it to the server.

[0727] Input: Collected acceleration data.

[0728] Output: Acceleration data sent to the server.

[0729] How it works: Every few seconds, the device collects data, stores it in a buffer, and then sends the data packets to the server via Wi-Fi or the mobile data network.

[0730] Step 4:

[0731] The server puts the received acceleration data into an analysis queue and starts analyzing it in real time.

[0732] Input: Acceleration data sent to the server.

[0733] Output: Estimated road surface conditions.

[0734] Specific operation: The server puts the received data into an analysis queue, applies a data analysis algorithm, and determines the road surface condition (flat ground, bumps, slopes, stairs, etc.) based on the fluctuation pattern of the data.

[0735] Step 5:

[0736] The user inputs their own attribute information within the application and sends it from the terminal to the server.

[0737] Input: User demographic information (e.g. wheelchair user, walking).

[0738] Output: The attribute information sent to the server.

[0739] Specific operation: The user accesses the application's settings screen and selects or enters their attribute information using drop-down menus and text boxes. This information is sent from the device to the server.

[0740] Step 6:

[0741] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route.

[0742] Input: Road surface condition information and user attribute information.

[0743] Output: Optimal route.

[0744] How it works: The server retrieves attribute information from the database, combines it with road surface condition data, and runs a route calculation algorithm to calculate the optimal route for individual needs, such as avoiding steps and stairs for wheelchair users.

[0745] Step 7:

[0746] The server transmits the generated optimal route information to the user terminal.

[0747] Input: Optimal route information.

[0748] Output: Route information sent to the user device.

[0749] Specific operation: The server compiles optimal route information into packets and sends them to the device. The device then displays the received route information in a map app or dedicated app.

[0750] Step 8:

[0751] Each time the user terminal collects new acceleration data, it updates the route information.

[0752] Input: New acceleration data.

[0753] Output: The updated route information.

[0754] What it does: The device sends newly collected acceleration data to the server. The server analyzes the new data and recalculates the route if necessary. The updated information is then sent back to the user's device, updating the route information on the map in real time.

[0755] In this way, the system can quickly respond to changes in the user's movements and environment, providing an always-up-to-date and safe route.

[0756] (Application example 1)

[0757] 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."

[0758] The present invention relates to the technical field of estimating road surface conditions by analyzing acceleration data collected from a user. In particular, it aims to provide a safe and comfortable route for an automated driving vehicle, and to solve the problem that it is difficult for current navigation systems to grasp road surface conditions in real time and to quickly change the route based on the detected road surface conditions.

[0759] 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.

[0760] In this invention, the server includes means for receiving acceleration data collected from a user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to user attribute information based on the estimated road surface condition information, means for transmitting the generated optimal route information to a user terminal, and means for controlling the vehicle's navigation system to automatically drive the vehicle based on the optimal route, thereby enabling fast and safe automatic driving based on real-time understanding of road surface conditions.

[0761] "User" refers to any person or entity that uses the system to provide acceleration data and receive optimized route information.

[0762] "Acceleration data" is information that indicates changes in movement measured by an acceleration sensor collected from a user's terminal or vehicle.

[0763] The "receiving means" is a communication means for inputting the acceleration data transmitted from the user's terminal into the server.

[0764] The "analyzing means" is a calculation means for processing the received acceleration data and estimating the road surface condition in real time.

[0765] "Road surface conditions" refers to the terrain and ground conditions, such as flat ground, steps, slopes, and stairs.

[0766] "Attribute information" is data that indicates the characteristics of a user (e.g., pedestrian, wheelchair user, bicycle user, self-driving vehicle user).

[0767] The "optimal route" is a safe and efficient route calculated based on estimated road surface condition information and user attribute information.

[0768] The "transmitting means" is a communication means for transferring the generated optimum route information to the user terminal.

[0769] A "vehicle navigation system" is a system that provides route information necessary for an autonomous vehicle to reach its destination and controls the vehicle's direction of travel and speed.

[0770] The present invention is a system that analyzes acceleration data collected from a user in real time, estimates road surface conditions, and provides an optimal route based on the user's attribute information. This system works in cooperation with the user's terminal, a server, and the vehicle's navigation system.

[0771] First, the user launches an application installed on their smartphone or vehicle-mounted device. The application activates the acceleration sensor and starts collecting acceleration data. The collected data is then periodically sent to a server.

[0772] The server uses a specific algorithm to analyze the received acceleration data in real time. This algorithm analyzes the fluctuation patterns of the acceleration data and estimates the road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy. For example, continuous high-frequency vibrations correspond to steps or stairs, while a gradual fluctuation pattern corresponds to a slope.

[0773] Next, the user enters their own attribute information (e.g., that they are a self-driving vehicle user) within the application. This attribute information is sent to the server, which then generates the optimal route based on this information. Specifically, the server prioritizes the safest and most comfortable route for the vehicle based on the estimated road surface conditions and the user's attribute information.

[0774] The generated optimal route information is sent from the server to the vehicle's navigation system. The navigation system controls the vehicle based on the received route information, providing autonomous driving functionality. The navigation system updates the route every time it receives new acceleration data, reflecting the latest road conditions and passability information.

[0775] Program processing

[0776] The user's terminal functions as a smartphone or a vehicle-mounted device and collects acceleration data using an acceleration sensor.

[0777] The server analyzes the acceleration data using programming languages ​​such as Python and R. Machine learning algorithms and data analysis libraries (e.g., scikit-learn, TensorFlow) are used for the analysis.

[0778] The vehicle's navigation system controls the vehicle based on the received route information. In addition to the usual navigation software, this system also has a function that reflects road surface conditions in real time.

[0779] Specific examples

[0780] For example, if a user is using a wheelchair-accessible autonomous vehicle, the user inputs attribute information into the application and sends road surface data via an acceleration sensor to the server. The server analyzes the data, generates the safest route for the wheelchair user, and sends it to the navigation system. The navigation system then controls the vehicle based on the received data, driving it to the destination along a route that avoids steps and stairs.

[0781] Prompt Sentence Examples

[0782] Design a system for a wheelchair-accessible autonomous vehicle that analyzes road conditions in real time based on acceleration data and provides the optimal route. Please include program code and detailed explanations for each step.

[0783] In this way, the present invention provides users of autonomous vehicles with optimal routes in real time, realizing a safe and comfortable driving environment.

[0784] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0785] Step 1:

[0786] The user's device collects acceleration data using an acceleration sensor. Specifically, the user starts up their smartphone or vehicle-mounted device and starts acquiring acceleration data within the application. The input here is data from the device's acceleration sensor, and the output is the collected acceleration data.

[0787] Step 2:

[0788] The user's device periodically sends the collected acceleration data to the server. Specifically, the device sends data to the server using an HTTP POST request. The input is the collected acceleration data, and the output is the data sent to the server.

[0789] Step 3:

[0790] The server analyzes the received acceleration data in real time. Specifically, it uses machine learning algorithms and data analysis libraries based on the received data to estimate road surface conditions such as flat ground, steps, slopes, and stairs. The input is the acceleration data received by the server, and the output is estimated road surface condition information.

[0791] Step 4:

[0792] The user inputs attribute information within the application. Specifically, a UI is provided for inputting attribute information such as whether the user is an autonomous vehicle user, a pedestrian, or a wheelchair user. The input here is the user's attribute information, and the output is the attribute information sent to the server.

[0793] Step 5:

[0794] The server generates the optimal route based on estimated road surface condition information and user attribute information. Specifically, it runs an algorithm that takes into account constraints based on road conditions and user attributes, analyzes map data, and calculates the optimal route. The input is estimated road surface condition information and attribute information, and the output is the generated optimal route information.

[0795] Step 6:

[0796] The server sends the generated optimal route information to the user's device. Specifically, an HTTP POST request is sent from the server to the user's device to transfer the route information. The input is the generated optimal route information, and the output is the optimal route information sent to the device.

[0797] Step 7:

[0798] The terminal provides the received optimal route information to the vehicle's navigation system. Specifically, the navigation software runs based on the received data and updates the vehicle's route guide. The input is the optimal route information sent to the terminal, and the output is the updated route information for the autonomous vehicle's navigation system.

[0799] Step 8:

[0800] The navigation system controls the vehicle's direction and speed based on the route information received. Specifically, it generates driving parameters according to real-time road conditions and controls various actuators in the vehicle. The input is the updated route information from the navigation system, and the output is the driving behavior of the autonomous vehicle.

[0801] 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.

[0802] The present invention is a system that provides more flexible optimal routes in the event of a disaster by combining a system that collects and analyzes acceleration data from users with an emotion engine that recognizes the emotional state of the user. This system operates in cooperation with the user's terminal, server, and emotion engine.

[0803] First, the user launches the application installed on their smartphone, which prepares them to begin evacuation. The device detects the launch of the application and activates the smartphone's accelerometer to begin collecting acceleration data. The collected acceleration data is then sent from the device to a server at regular intervals.

[0804] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. Specifically, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[0805] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses the user's facial recognition data, voice data, or other biometric information to estimate the user's emotional state. If the user is feeling fear or anxiety, the emotion engine will detect this and send the information to the server.

[0806] The user inputs their attribute information (e.g., wheelchair user) within the application. Both this attribute information and emotional state information are sent from the device to the server. The server then generates the optimal route based on this information. For example, if the user is a wheelchair user and feels anxious, the server will prioritize routes that are as easy and safe as possible to evacuate.

[0807] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. If the user's emotional state changes, the server can recalculate a new route and update it in real time.

[0808] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone, which collects data using an acceleration sensor. The acceleration data is sent to a server, which analyzes it and estimates the current road surface conditions. If the user is a wheelchair user and the emotion engine detects anxiety, the server will prioritize generating a safe and easy route that avoids steps and stairs and send it to the device. The user can evacuate safely by relying on the route displayed on the map.

[0809] This system provides optimal evacuation routes in real time, taking into account the user's emotional state during a disaster, enabling quick and safe evacuation.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] The user launches the application installed on their smartphone, which activates the emotion engine and accelerometer.

[0813] Step 2:

[0814] The device detects that the application has been launched and activates the smartphone's camera, microphone, and accelerometer, thereby starting to collect facial recognition data, voice data, and acceleration data.

[0815] Step 3:

[0816] The device processes facial recognition data and voice data collected in real time and uses an emotion engine to estimate the user's emotional state, for example, determining whether the user is feeling fear or anxiety based on their facial expression.

[0817] Step 4:

[0818] The device transmits the collected acceleration data and emotional state data to the server at regular intervals (e.g., every second). This data includes acceleration changes during movement, a timestamp, the device ID, and the emotional state.

[0819] Step 5:

[0820] The server receives the acceleration data and emotional state data sent from the device, temporarily stores the data in a database, and prepares it for analysis.

[0821] Step 6:

[0822] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0823] Step 7:

[0824] The server analyzes the user's emotional state data and takes into account, for example, if the user is feeling fear or anxiety, prioritizing the selection of a route that provides psychological security.

[0825] Step 8:

[0826] Users enter their personal information (e.g., wheelchair user) within the application, which is then sent to the server and used to respond to their individual needs.

[0827] Step 9:

[0828] The server combines the estimated road surface conditions with the user's attribute information and emotional state information to generate the optimal route for the user. For example, it may prioritize a route that avoids steps and stairs for wheelchair users, or provide an easy evacuation route that provides psychological security for users who are feeling anxious.

[0829] Step 10:

[0830] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0831] Step 11:

[0832] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0833] Step 12:

[0834] Users can evacuate safely and securely by following the optimal route displayed on the map, while referring to route information and emotional state updated in real time.

[0835] Example 2

[0836] 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."

[0837] Existing evacuation support systems focus on the user's physical state (e.g., road surface condition and user attributes), but they have the problem of not being able to consider the user's emotional state. Particularly during disasters, users' anxiety and fear have a strong influence on evacuation behavior, so comprehensive support that includes their emotional state is required.

[0838] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This enables evacuation support that takes the user's emotional state into consideration.

[0839] "User" refers to the entity that uses the system to receive evacuation assistance.

[0840] "Acceleration data" refers to data obtained from an acceleration sensor built into the user's device.

[0841] The "receiving means" refers to a communication means for inputting the acceleration data sent from the user's terminal into the server.

[0842] The "analyzing means" refers to an algorithm and its execution environment for analyzing the received acceleration data and estimating the road surface condition.

[0843] "Road surface conditions" refers to the condition of the ground, such as flat ground, steps, slopes, and stairs.

[0844] "Means for estimating" refers to technology for estimating road surface conditions based on acceleration data.

[0845] "Emotional state information" refers to data that indicates the user's emotional state, including states such as fear, anxiety, relief, etc.

[0846] "User attribute information" refers to data that indicates the physical characteristics of a user, including whether the user is a pedestrian, a wheelchair user, a cyclist, etc.

[0847] The "optimal route" refers to the evacuation route that is most suitable for the user, which is generated based on the user's attribute information and emotional state information.

[0848] "Means for generating" refers to methods and systems for calculating an optimal route based on road surface condition information and emotional state information.

[0849] "User terminal" refers to a mobile terminal such as a smartphone or tablet used by a user.

[0850] The "means for transmitting" refers to a communication method and device for transmitting the generated optimal route information from the server to the user terminal.

[0851] The present invention relates to a system that analyzes acceleration data collected from a user and estimates road surface conditions. The system aims to provide an optimal evacuation route that takes into account the user's emotional state. Specific embodiments are described below.

[0852] First, the user launches the evacuation support application installed on their smartphone. The user then enters their own attribute information (e.g., wheelchair user) into the application. The device detects that the application has been launched and activates its built-in acceleration sensor. This causes the device to start collecting acceleration data and send it to the server at regular intervals.

[0853] The server analyzes the received acceleration data in real time using an algorithm that distinguishes high-frequency vibrations from stairs and gradual fluctuations from slopes. For example, the server analyzes the amplitude and period of the data to estimate the current road surface condition with high accuracy.

[0854] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotional state. The user's device collects facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The emotion engine analyzes this data, estimates the user's emotional state (e.g., fear, anxiety, relief), and sends the information to the server.

[0855] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. For example, if a wheelchair user feels anxious, the server will prioritize providing a safe route that avoids steps and stairs. The generated optimal route information is sent from the server to the user's device.

[0856] The user's device displays the received optimal route information on a map. The user can check the evacuation route on the map in real time. If the user's emotional state or evacuation situation changes, the device collects new emotional data and sends it again to the emotion engine. The server recalculates the optimal route based on the user's latest state and updates it in real time. The user can continue to evacuate safely based on this information.

[0857] As a concrete example, consider a situation in which a user begins evacuation after an earthquake. The user launches a smartphone application and enters attribute information. The device collects data using an acceleration sensor and sends it to a server. The server analyzes this data and estimates the current road surface condition with high accuracy. Furthermore, the emotion engine detects whether the user is feeling anxious and sends this information to the server. The server then generates a safe route that avoids steps and stairs and sends it to the device. The user can then begin evacuation safely based on the displayed route.

[0858] Example prompt sentence:

[0859] After an earthquake, the user launches the smartphone application. The acceleration sensor collects data and sends it to the server. The server analyzes the data and estimates road surface conditions. The emotion engine detects that the user is feeling anxious and sends route information to the device, allowing the user to evacuate safely.

[0860] In this way, the system of the present invention can provide the optimal evacuation route in real time, taking into account the user's emotional state and physical attribute information, thereby realizing support for a quick and safe evacuation.

[0861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] The user launches the evacuation support application installed on their smartphone. The input is the user's operation, and the output is the application's running status. This prepares the system for evacuation support.

[0864] Step 2:

[0865] The device detects the application launch and activates the built-in acceleration sensor. The input is the application launch detection signal, and the output is the activation of the acceleration sensor. The device uses this to start collecting acceleration data.

[0866] Step 3:

[0867] The device starts collecting acceleration data and sends it to the server at regular intervals. The input is data from the acceleration sensor, and the output is the acceleration data sent to the server. Specifically, the acceleration data is serialized and sent to the server via an HTTP POST request.

[0868] Step 4:

[0869] The server analyzes the received acceleration data in real time. The input is the acceleration data sent from the device, and the output is an estimation of the road surface condition. Specifically, an algorithm is used to distinguish high-frequency vibrations as stairs and gradual fluctuations as slopes, and the amplitude and period of the data are analyzed to determine the road surface condition.

[0870] Step 5:

[0871] The device collects the user's facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The input is biometric data obtained from the camera and microphone, and the output is data for estimating the user's emotions, which is sent to the emotion engine. Specifically, the device appropriately formats the collected data and sends it to the emotion engine.

[0872] Step 6:

[0873] The emotion engine estimates the user's emotional state (e.g., fear, anxiety, relief) and sends that information to the server. The input is the user's biometric data, and the output is estimated emotional state information. Specifically, it analyzes the biometric data, classifies the emotional state using an emotion model, and sends the information to the server.

[0874] Step 7:

[0875] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. The inputs are the user's attribute information, emotional state information, and road surface condition information, and the output is the optimal evacuation route information. Specifically, this information is integrated and an algorithm is applied to calculate the optimal route.

[0876] Step 8:

[0877] The server sends the generated optimal route information to the user's device. The input is the calculated optimal route information, and the output is the route information to be sent to the device. Specifically, the route information is serialized in JSON format and sent to the device via an HTTP POST request.

[0878] Step 9:

[0879] The user's device receives the optimal route information and displays it on a map. The input is the route information sent from the server, and the output is the route displayed in the map app. Specifically, the received route data is drawn on the map using the map API.

[0880] Step 10:

[0881] If the emotional state or evacuation situation changes, the device collects new emotional data and sends it to the emotion engine again. The input is new biometric data, and the output is updated emotional state information. This allows the server to recalculate based on the latest user information.

[0882] (Application example 2)

[0883] 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."

[0884] Conventional evacuation systems mainly generate evacuation routes based on the user's physical attribute information, but because they do not take into account the user's emotional state, they are unable to alleviate psychological anxiety during disasters. Furthermore, there is a lack of a means to provide optimal routes that reflect passengers' emotions in real time, especially when using autonomous vehicles.

[0885] The identification process by the identification 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 receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This makes it possible to provide a safer and more secure evacuation route that takes the emotional state into consideration.

[0886] "Acceleration data" is data that indicates the movement and speed changes of an object, collected by sensors on the user's device.

[0887] "Road surface conditions" refer to the physical characteristics of the travel route, such as flat ground, steps, slopes, and stairs.

[0888] The "emotional state" is a state of mind estimated based on biometric information such as the user's facial expression and voice, and includes emotions such as anxiety and fear.

[0889] "User attribute information" is information related to the physical characteristics and mode of transportation (e.g., pedestrian, wheelchair user, cyclist) entered by the user.

[0890] The "optimal route" is the best route that allows the user to evacuate safely and efficiently, calculated based on the collected data.

[0891] A "terminal" is an information processing device used by a user, and includes mobile devices such as smartphones equipped with a sensitivity sensor, a camera, and the like.

[0892] A "server" is a computer system that processes data on a network and sends analysis results and information to terminals.

[0893] The present invention has a configuration in which a user terminal, a server, and an autonomous vehicle system operate in cooperation with each other. A specific embodiment of this system will be described below.

[0894] System configuration

[0895] Hardware and Software

[0896] 1. User device: A smartphone is used. It is equipped with an accelerometer, camera, and microphone, and is used to collect acceleration data and emotion data.

[0897] 2. Server: A computer system that receives and analyzes data. It processes data using Python, OpenCV, and specific libraries (AccelerometerSensor, EmotionEngine).

[0898] 3. Autonomous vehicle systems: receive route information in real time and use it to control the vehicle.

[0899] Program processing

[0900] Acceleration data collection

[0901] The acceleration sensor on the user device (smartphone) is used to collect vehicle vibration data. This is done by an application running on the device. For example, the AccelerometerSensor library is used to obtain acceleration data in real time.

[0902] Recognition of emotional states

[0903] Using the camera and microphone on the user's device, the system recognizes the user's emotional state from their facial expressions and voice. Here, OpenCV and the EmotionEngine library are used to analyze image and audio data and estimate the user's emotions.

[0904] Data transmission and analysis

[0905] The collected acceleration data and emotion data are sent to a server at regular intervals. The server receives this data and analyzes it in real time. The acceleration data is used to estimate road surface conditions, and the emotion data is used to estimate the user's emotional state. The results are then combined to generate the optimal route.

[0906] Generate optimal routes

[0907] The server calculates the optimal evacuation route based on the estimated road surface condition information, emotional state information, and user attribute information (e.g., wheelchair user, pedestrian, cyclist). If the user is feeling particularly anxious, the server prioritizes generating a safe and secure route.

[0908] Route information presentation

[0909] The generated optimal route information is sent from the server to the user terminal and also to the autonomous vehicle system, which controls the vehicle to follow the route.

[0910] Specific examples

[0911] For example, consider a situation where a user begins to evacuate after an earthquake. The user launches an application on their smartphone, which collects data using the accelerometer and camera. The acceleration and emotion data are sent to a server, which analyzes the data and generates an optimal evacuation route. An autonomous vehicle follows the route and assists the user in a safe evacuation.

[0912] Prompt Sentence Examples

[0913] Instructions: Provide optimal routes for passengers in an autonomous vehicle to evacuate after an earthquake disaster. Use accelerometer and camera data and consider the passenger's emotional state. If the passenger feels anxious, prioritize the safe route.

[0914] This invention can provide a safer and more secure evacuation route by taking into consideration the emotional state of the user.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The user launches the smartphone application, which prepares for evacuation.

[0918] Step 2:

[0919] The device (smartphone) enables the accelerometer sensor and starts collecting acceleration data. Specifically, the device continuously acquires acceleration data using the AccelerometerSensor library. The input is data from the device's accelerometer sensor, and the output is the acquired acceleration data.

[0920] Step 3:

[0921] The device uses a camera and microphone to collect the user's facial expressions and voice to recognize their emotional state. Here, the OpenCV library is used to analyze image data, and the EmotionEngine library is used to estimate emotions. The input is video and audio data from the camera and microphone, and the output is the estimated emotional state.

[0922] Step 4:

[0923] The collected acceleration data and emotion data are sent to a server at regular intervals. The input is the acceleration data and emotion data, and the output is the result of the data transmission to the server.

[0924] Step 5:

[0925] The server analyzes the received acceleration data and estimates road surface conditions. Specifically, it identifies road surface conditions such as flat ground, steps, slopes, and stairs in real time based on the fluctuation patterns of the acceleration data. The input is acceleration data, and the output is estimated road surface condition information.

[0926] Step 6:

[0927] The server analyzes the received emotion data and estimates the user's emotional state. Specifically, it uses the EmotionEngine to recognize emotions from facial expressions and voice data. The input is emotion data, and the output is the estimated emotional state.

[0928] Step 7:

[0929] The server generates the optimal route based on the estimated road surface condition information, emotional state information, and user attribute information (previously entered information such as whether the user is a wheelchair user, pedestrian, or cyclist). The input is road surface condition information, emotional state information, and attribute information, and the output is the generated optimal route information.

[0930] Step 8:

[0931] The generated optimal route information is sent from the server to the user terminal. It is also sent simultaneously to the autonomous vehicle system. The input is the optimal route information, and the output is the sent route information.

[0932] Step 9:

[0933] The user device displays the received optimal route information on a map, allowing the user to check the optimal evacuation route in real time. Specifically, the route is visually displayed using a map application. The input is the received route information, and the output is the route display on the map.

[0934] Step 10:

[0935] The autonomous vehicle system controls the vehicle along the received optimal route and evacuates the user. The input is the optimal route information, and the output is the vehicle's driving path.

[0936] This system provides the optimal evacuation route in real time, taking into account the user's emotional state, enabling a quick and safe evacuation.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] [Fourth embodiment]

[0941] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0942] 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.

[0943] 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).

[0944] 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.

[0945] 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.

[0946] 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).

[0947] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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."

[0954] The present invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road surface conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[0955] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and starts collecting acceleration data. The collected acceleration data is then periodically sent from the device to a server.

[0956] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to a slope. This allows the server to estimate the current road surface condition (flat ground, steps, slope, stairs, etc.) with high accuracy.

[0957] Next, the user enters their own attribute information (e.g., wheelchair user) into the application. This attribute information is sent from the device to the server, and the server uses this information to generate the optimal route. Specifically, the estimated road surface condition information is combined with the user's attribute information to calculate a route that meets the individual needs of wheelchair users, such as prioritizing routes that avoid steps and stairs.

[0958] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. This route information is updated every time the server receives and analyzes new acceleration data, reflecting the latest passable information.

[0959] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone. The smartphone uses an acceleration sensor to collect data and sends it to a server. The server analyzes the acceleration data and estimates the current road surface conditions. After that, when the user indicates within the app that they are a wheelchair user, the server generates an optimal route that avoids steps and stairs and sends that information to the user's device. The user can evacuate safely by relying on the route displayed on the map.

[0960] This system allows for quick and efficient evacuation, by providing the optimal evacuation route based on the user's attributes in real time, even in the event of a disaster.

[0961] The processing flow will be explained below.

[0962] Step 1:

[0963] The user launches the application installed on their smartphone, which prepares them to begin evacuation.

[0964] Step 2:

[0965] The device detects that the application has been launched and activates the smartphone's accelerometer, which starts collecting acceleration data about the user's movements.

[0966] Step 3:

[0967] The device sends the collected acceleration data at regular intervals (e.g., every second) to the server. This data includes a timestamp indicating the change in acceleration during movement and the device ID.

[0968] Step 4:

[0969] The server receives the acceleration data sent from the device, temporarily stores it in a database, and prepares it for analysis.

[0970] Step 5:

[0971] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[0972] Step 6:

[0973] Users enter their demographic information (e.g., wheelchair user) within the application, which is important to address the specific needs of each individual user.

[0974] Step 7:

[0975] The device sends the attribute information entered by the user to the server, which then prepares to calculate the optimal route based on the user's attributes.

[0976] Step 8:

[0977] The server combines the estimated road surface conditions with the user's attribute information to generate the optimal route for the user. For example, it prioritizes routes that avoid steps and stairs for wheelchair users.

[0978] Step 9:

[0979] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[0980] Step 10:

[0981] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[0982] Step 11:

[0983] Users can evacuate safely by following the optimal route displayed on the map, with route information updated in real time.

[0984] Example 1

[0985] 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."

[0986] Conventional technologies have struggled to estimate road surface conditions in real time and provide optimal routes based on user attributes. This makes it particularly difficult to generate appropriate evacuation routes during disasters, as road surface conditions change rapidly. Furthermore, route calculations tailored to user attributes were also inadequate. This resulted in a lack of reliable information for safe and rapid evacuation, particularly for pedestrians and wheelchair users who experience frequent vibrations.

[0987] 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.

[0988] In this invention, the server includes means for periodically receiving acceleration data collected from a user, means for analyzing the received acceleration data in real time and estimating road surface conditions based on fluctuation patterns in the data, means for transmitting attribute information input by the user from a terminal to the server, means for generating an optimal route by combining the estimated road surface condition information and the user's attribute information, means for transmitting the generated optimal route information to the user terminal, and means for updating the route information each time the user terminal collects new acceleration data, thereby enabling the provision of an optimal route in real time according to the user's attributes.

[0989] "Acceleration data collected from the user" refers to acceleration information such as the user's movements and vibrations, which is acquired by the user's device using a built-in sensor.

[0990] "Receiving at regular intervals" means receiving data periodically at a certain interval.

[0991] "Analyzing in real time" means processing data immediately after receiving it and obtaining analysis results as current information.

[0992] The "data fluctuation pattern" refers to the tendency of changes over time in the collected acceleration data.

[0993] "Estimating road surface conditions" means determining the road surface conditions, such as flat ground, steps, slopes, and stairs, from the analysis results of acceleration data.

[0994] "Attribute information input by the user from the terminal" is information about the characteristics of the user (e.g., pedestrian, wheelchair user, etc.) that the user provides through the terminal.

[0995] "Generating an optimal route" means calculating the most suitable route for the user based on the estimated road surface conditions and the user's attribute information.

[0996] "Transmitting to a user terminal" means transferring calculated or generated information to a device held by the user via a network.

[0997] "Collecting new acceleration data" means re-obtaining acceleration information relating to the user's movements that has been updated over time.

[0998] "Updating route information" means reevaluating the optimal route based on newly acquired acceleration data and making changes as necessary.

[0999] This invention is a system that receives acceleration data collected from users, analyzes the data to estimate real-time road conditions, and generates an optimal route based on the user's attribute information. This system operates in cooperation with the user's terminal, a server, and the user.

[1000] Hardware and Software Configuration

[1001] The hardware used includes a smartphone (with an accelerometer) and a server, while the software includes an application installed on the smartphone and a data analysis algorithm running on the server.

[1002] Data collection

[1003] First, a user launches an application installed on a smartphone or other mobile device. The application activates the smartphone's accelerometer and begins collecting acceleration data. Specifically, data collection begins the moment the user starts moving, recording minute vibrations and changes in tilt.

[1004] Sending data

[1005] The collected acceleration data is sent from the user's device to a server at regular intervals, using Wi-Fi or mobile data networks.

[1006] Data analysis

[1007] The server analyzes the received acceleration data in real time. The analysis algorithm identifies road surface conditions based on the fluctuation patterns of the acceleration data. For example, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface conditions (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[1008] Entering user attribute information

[1009] Users input their attribute information (e.g., wheelchair user, visually impaired, etc.) within the application, and this information is sent from the user's device to the server.

[1010] Optimal route generation

[1011] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route. For example, for wheelchair users, it prioritizes routes that avoid steps and stairs. Based on this information, the server calculates the optimal route for each individual user.

[1012] Submitting and updating route information

[1013] The generated optimal route information is sent from the server to the user's device. The user's device displays this information on a map, allowing the user to check the optimal route in real time. The user's device also collects new acceleration data and sends it to the server, which then updates the route information in real time.

[1014] Specific examples

[1015] For example, consider a scenario in which an earthquake occurs and the user begins evacuation. The user launches an application installed on their smartphone and begins collecting data using the accelerometer. The collected data is sent from the device to a server, which analyzes the data and estimates the current road surface conditions. After that, if the user indicates within the app that they are a wheelchair user, the server generates an optimal evacuation route without steps or stairs and sends that information to the user's device. The user can then begin a safe evacuation by relying on the route displayed on the map.

[1016] Prompt Sentence Examples

[1017] "What is the best route to evacuate after an earthquake? I use a wheelchair."

[1018] "Please tell me the safest evacuation route from my current location. I would like to avoid stairs and steps."

[1019] This system will provide the optimal route based on the user's attributes in real time, even in the event of a disaster, enabling quick and safe evacuation.

[1020] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1021] Step 1:

[1022] The user launches an application installed on the smartphone.

[1023] Input: The user's action to launch the application.

[1024] Output: Application launch.

[1025] What happens: The user taps the application icon to launch the application.

[1026] Step 2:

[1027] The device will activate the smartphone's accelerometer and begin collecting acceleration data.

[1028] Input: The application startup state.

[1029] Output: Start collecting acceleration data.

[1030] Specific operation: After the application is launched, it automatically activates the smartphone's accelerometer and records acceleration data on the x-axis, y-axis, and z-axis in real time.

[1031] Step 3:

[1032] The device stores the collected acceleration data in a buffer at regular intervals and transmits it to the server.

[1033] Input: Collected acceleration data.

[1034] Output: Acceleration data sent to the server.

[1035] How it works: Every few seconds, the device collects data, stores it in a buffer, and then sends the data packets to the server via Wi-Fi or the mobile data network.

[1036] Step 4:

[1037] The server puts the received acceleration data into an analysis queue and starts analyzing it in real time.

[1038] Input: Acceleration data sent to the server.

[1039] Output: Estimated road surface conditions.

[1040] Specific operation: The server puts the received data into an analysis queue, applies a data analysis algorithm, and determines the road surface condition (flat ground, bumps, slopes, stairs, etc.) based on the fluctuation pattern of the data.

[1041] Step 5:

[1042] The user inputs their own attribute information within the application and sends it from the terminal to the server.

[1043] Input: User demographic information (e.g. wheelchair user, walking).

[1044] Output: The attribute information sent to the server.

[1045] Specific operation: The user accesses the application's settings screen and selects or enters their attribute information using drop-down menus and text boxes. This information is sent from the device to the server.

[1046] Step 6:

[1047] The server combines the estimated road surface condition information with the user's attribute information to generate the optimal route.

[1048] Input: Road surface condition information and user attribute information.

[1049] Output: Optimal route.

[1050] How it works: The server retrieves attribute information from the database, combines it with road surface condition data, and runs a route calculation algorithm to calculate the optimal route for individual needs, such as avoiding steps and stairs for wheelchair users.

[1051] Step 7:

[1052] The server transmits the generated optimal route information to the user terminal.

[1053] Input: Optimal route information.

[1054] Output: Route information sent to the user device.

[1055] Specific operation: The server compiles optimal route information into packets and sends them to the device. The device then displays the received route information in a map app or dedicated app.

[1056] Step 8:

[1057] Each time the user terminal collects new acceleration data, it updates the route information.

[1058] Input: New acceleration data.

[1059] Output: The updated route information.

[1060] What it does: The device sends newly collected acceleration data to the server. The server analyzes the new data and recalculates the route if necessary. The updated information is then sent back to the user's device, updating the route information on the map in real time.

[1061] In this way, the system can quickly respond to changes in the user's movements and environment, providing an always-up-to-date and safe route.

[1062] (Application example 1)

[1063] 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."

[1064] The present invention relates to the technical field of estimating road surface conditions by analyzing acceleration data collected from a user. In particular, it aims to provide a safe and comfortable route for an automated driving vehicle, and to solve the problem that it is difficult for current navigation systems to grasp road surface conditions in real time and to quickly change the route based on the detected road surface conditions.

[1065] 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.

[1066] In this invention, the server includes means for receiving acceleration data collected from a user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to user attribute information based on the estimated road surface condition information, means for transmitting the generated optimal route information to a user terminal, and means for controlling the vehicle's navigation system to automatically drive the vehicle based on the optimal route, thereby enabling fast and safe automatic driving based on real-time understanding of road surface conditions.

[1067] "User" refers to any person or entity that uses the system to provide acceleration data and receive optimized route information.

[1068] "Acceleration data" is information that indicates changes in movement measured by an acceleration sensor collected from a user's terminal or vehicle.

[1069] The "receiving means" is a communication means for inputting the acceleration data transmitted from the user's terminal into the server.

[1070] The "analyzing means" is a calculation means for processing the received acceleration data and estimating the road surface condition in real time.

[1071] "Road surface conditions" refers to the terrain and ground conditions, such as flat ground, steps, slopes, and stairs.

[1072] "Attribute information" is data that indicates the characteristics of a user (e.g., pedestrian, wheelchair user, bicycle user, self-driving vehicle user).

[1073] The "optimal route" is a safe and efficient route calculated based on estimated road surface condition information and user attribute information.

[1074] The "transmitting means" is a communication means for transferring the generated optimum route information to the user terminal.

[1075] A "vehicle navigation system" is a system that provides route information necessary for an autonomous vehicle to reach its destination and controls the vehicle's direction of travel and speed.

[1076] The present invention is a system that analyzes acceleration data collected from a user in real time, estimates road surface conditions, and provides an optimal route based on the user's attribute information. This system works in cooperation with the user's terminal, a server, and the vehicle's navigation system.

[1077] First, the user launches an application installed on their smartphone or vehicle-mounted device. The application activates the acceleration sensor and starts collecting acceleration data. The collected data is then periodically sent to a server.

[1078] The server uses a specific algorithm to analyze the received acceleration data in real time. This algorithm analyzes the fluctuation patterns of the acceleration data and estimates the road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy. For example, continuous high-frequency vibrations correspond to steps or stairs, while a gradual fluctuation pattern corresponds to a slope.

[1079] Next, the user enters their own attribute information (e.g., that they are a self-driving vehicle user) within the application. This attribute information is sent to the server, which then generates the optimal route based on this information. Specifically, the server prioritizes the safest and most comfortable route for the vehicle based on the estimated road surface conditions and the user's attribute information.

[1080] The generated optimal route information is sent from the server to the vehicle's navigation system. The navigation system controls the vehicle based on the received route information, providing autonomous driving functionality. The navigation system updates the route every time it receives new acceleration data, reflecting the latest road conditions and passability information.

[1081] Program processing

[1082] The user's terminal functions as a smartphone or a vehicle-mounted device and collects acceleration data using an acceleration sensor.

[1083] The server analyzes the acceleration data using programming languages ​​such as Python and R. Machine learning algorithms and data analysis libraries (e.g., scikit-learn, TensorFlow) are used for the analysis.

[1084] The vehicle's navigation system controls the vehicle based on the received route information. In addition to the usual navigation software, this system also has a function that reflects road surface conditions in real time.

[1085] Specific examples

[1086] For example, if a user is using a wheelchair-accessible autonomous vehicle, the user inputs attribute information into the application and sends road surface data via an acceleration sensor to the server. The server analyzes the data, generates the safest route for the wheelchair user, and sends it to the navigation system. The navigation system then controls the vehicle based on the received data, driving it to the destination along a route that avoids steps and stairs.

[1087] Prompt Sentence Examples

[1088] Design a system for a wheelchair-accessible autonomous vehicle that analyzes road conditions in real time based on acceleration data and provides the optimal route. Please include program code and detailed explanations for each step.

[1089] In this way, the present invention provides users of autonomous vehicles with optimal routes in real time, realizing a safe and comfortable driving environment.

[1090] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1091] Step 1:

[1092] The user's device collects acceleration data using an acceleration sensor. Specifically, the user starts up their smartphone or vehicle-mounted device and starts acquiring acceleration data within the application. The input here is data from the device's acceleration sensor, and the output is the collected acceleration data.

[1093] Step 2:

[1094] The user's device periodically sends the collected acceleration data to the server. Specifically, the device sends data to the server using an HTTP POST request. The input is the collected acceleration data, and the output is the data sent to the server.

[1095] Step 3:

[1096] The server analyzes the received acceleration data in real time. Specifically, it uses machine learning algorithms and data analysis libraries based on the received data to estimate road surface conditions such as flat ground, steps, slopes, and stairs. The input is the acceleration data received by the server, and the output is estimated road surface condition information.

[1097] Step 4:

[1098] The user inputs attribute information within the application. Specifically, a UI is provided for inputting attribute information such as whether the user is an autonomous vehicle user, a pedestrian, or a wheelchair user. The input here is the user's attribute information, and the output is the attribute information sent to the server.

[1099] Step 5:

[1100] The server generates the optimal route based on estimated road surface condition information and user attribute information. Specifically, it runs an algorithm that takes into account constraints based on road conditions and user attributes, analyzes map data, and calculates the optimal route. The input is estimated road surface condition information and attribute information, and the output is the generated optimal route information.

[1101] Step 6:

[1102] The server sends the generated optimal route information to the user's device. Specifically, an HTTP POST request is sent from the server to the user's device to transfer the route information. The input is the generated optimal route information, and the output is the optimal route information sent to the device.

[1103] Step 7:

[1104] The terminal provides the received optimal route information to the vehicle's navigation system. Specifically, the navigation software runs based on the received data and updates the vehicle's route guide. The input is the optimal route information sent to the terminal, and the output is the updated route information for the autonomous vehicle's navigation system.

[1105] Step 8:

[1106] The navigation system controls the vehicle's direction and speed based on the route information received. Specifically, it generates driving parameters according to real-time road conditions and controls various actuators in the vehicle. The input is the updated route information from the navigation system, and the output is the driving behavior of the autonomous vehicle.

[1107] 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.

[1108] The present invention is a system that provides more flexible optimal routes in the event of a disaster by combining a system that collects and analyzes acceleration data from users with an emotion engine that recognizes the emotional state of the user. This system operates in cooperation with the user's terminal, server, and emotion engine.

[1109] First, the user launches the application installed on their smartphone, which prepares them to begin evacuation. The device detects the launch of the application and activates the smartphone's accelerometer to begin collecting acceleration data. The collected acceleration data is then sent from the device to a server at regular intervals.

[1110] The server analyzes the received acceleration data in real time. The analysis uses an algorithm that identifies the road surface condition based on the fluctuation pattern of the acceleration data. Specifically, continuous high-frequency vibrations correspond to stairs, while gradual fluctuations correspond to slopes. This allows the server to estimate the current road surface condition (flat ground, steps, slopes, stairs, etc.) with high accuracy.

[1111] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses the user's facial recognition data, voice data, or other biometric information to estimate the user's emotional state. If the user is feeling fear or anxiety, the emotion engine will detect this and send the information to the server.

[1112] The user inputs their attribute information (e.g., wheelchair user) within the application. Both this attribute information and emotional state information are sent from the device to the server. The server then generates the optimal route based on this information. For example, if the user is a wheelchair user and feels anxious, the server will prioritize routes that are as easy and safe as possible to evacuate.

[1113] The generated optimal route information is sent from the server to the user's device. The device displays this information on a map, allowing the user to check the optimal evacuation route in real time. If the user's emotional state changes, the server can recalculate a new route and update it in real time.

[1114] As a concrete example, consider a scenario in which a user begins evacuation after an earthquake. The user launches an application installed on their smartphone, which collects data using an acceleration sensor. The acceleration data is sent to a server, which analyzes it and estimates the current road surface conditions. If the user is a wheelchair user and the emotion engine detects anxiety, the server will prioritize generating a safe and easy route that avoids steps and stairs and send it to the device. The user can evacuate safely by relying on the route displayed on the map.

[1115] This system provides optimal evacuation routes in real time, taking into account the user's emotional state during a disaster, enabling quick and safe evacuation.

[1116] The processing flow will be explained below.

[1117] Step 1:

[1118] The user launches the application installed on their smartphone, which activates the emotion engine and accelerometer.

[1119] Step 2:

[1120] The device detects that the application has been launched and activates the smartphone's camera, microphone, and accelerometer, thereby starting to collect facial recognition data, voice data, and acceleration data.

[1121] Step 3:

[1122] The device processes facial recognition data and voice data collected in real time and uses an emotion engine to estimate the user's emotional state, for example, determining whether the user is feeling fear or anxiety based on their facial expression.

[1123] Step 4:

[1124] The device transmits the collected acceleration data and emotional state data to the server at regular intervals (e.g., every second). This data includes acceleration changes during movement, a timestamp, the device ID, and the emotional state.

[1125] Step 5:

[1126] The server receives the acceleration data and emotional state data sent from the device, temporarily stores the data in a database, and prepares it for analysis.

[1127] Step 6:

[1128] The server analyzes the received acceleration data in real time and estimates the road surface condition by using an algorithm that distinguishes between flat ground, steps, slopes, stairs, etc. from the fluctuation patterns of the acceleration data.

[1129] Step 7:

[1130] The server analyzes the user's emotional state data and takes into account, for example, if the user is feeling fear or anxiety, prioritizing the selection of a route that provides psychological security.

[1131] Step 8:

[1132] Users enter their personal information (e.g., wheelchair user) within the application, which is then sent to the server and used to respond to their individual needs.

[1133] Step 9:

[1134] The server combines the estimated road surface conditions with the user's attribute information and emotional state information to generate the optimal route for the user. For example, it may prioritize a route that avoids steps and stairs for wheelchair users, or provide an easy evacuation route that provides psychological security for users who are feeling anxious.

[1135] Step 10:

[1136] The server transmits the generated optimal route information to the user's device, allowing the device to provide the user with the latest road availability information.

[1137] Step 11:

[1138] The device displays the optimal route information on a map, allowing users to visually confirm the safest evacuation route.

[1139] Step 12:

[1140] Users can evacuate safely and securely by following the optimal route displayed on the map, while referring to route information and emotional state updated in real time.

[1141] Example 2

[1142] 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."

[1143] Existing evacuation support systems focus on the user's physical state (e.g., road surface condition and user attributes), but they have the problem of not being able to consider the user's emotional state. Particularly during disasters, users' anxiety and fear have a strong influence on evacuation behavior, so comprehensive support that includes their emotional state is required.

[1144] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This enables evacuation support that takes the user's emotional state into consideration.

[1145] "User" refers to the entity that uses the system to receive evacuation assistance.

[1146] "Acceleration data" refers to data obtained from an acceleration sensor built into the user's device.

[1147] The "receiving means" refers to a communication means for inputting the acceleration data sent from the user's terminal into the server.

[1148] The "analyzing means" refers to an algorithm and its execution environment for analyzing the received acceleration data and estimating the road surface condition.

[1149] "Road surface conditions" refers to the condition of the ground, such as flat ground, steps, slopes, and stairs.

[1150] "Means for estimating" refers to technology for estimating road surface conditions based on acceleration data.

[1151] "Emotional state information" refers to data that indicates the user's emotional state, including states such as fear, anxiety, relief, etc.

[1152] "User attribute information" refers to data that indicates the physical characteristics of a user, including whether the user is a pedestrian, a wheelchair user, a cyclist, etc.

[1153] The "optimal route" refers to the evacuation route that is most suitable for the user, which is generated based on the user's attribute information and emotional state information.

[1154] "Means for generating" refers to methods and systems for calculating an optimal route based on road surface condition information and emotional state information.

[1155] "User terminal" refers to a mobile terminal such as a smartphone or tablet used by a user.

[1156] The "means for transmitting" refers to a communication method and device for transmitting the generated optimal route information from the server to the user terminal.

[1157] The present invention relates to a system that analyzes acceleration data collected from a user and estimates road surface conditions. The system aims to provide an optimal evacuation route that takes into account the user's emotional state. Specific embodiments are described below.

[1158] First, the user launches the evacuation support application installed on their smartphone. The user then enters their own attribute information (e.g., wheelchair user) into the application. The device detects that the application has been launched and activates its built-in acceleration sensor. This causes the device to start collecting acceleration data and send it to the server at regular intervals.

[1159] The server analyzes the received acceleration data in real time using an algorithm that distinguishes high-frequency vibrations from stairs and gradual fluctuations from slopes. For example, the server analyzes the amplitude and period of the data to estimate the current road surface condition with high accuracy.

[1160] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotional state. The user's device collects facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The emotion engine analyzes this data, estimates the user's emotional state (e.g., fear, anxiety, relief), and sends the information to the server.

[1161] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. For example, if a wheelchair user feels anxious, the server will prioritize providing a safe route that avoids steps and stairs. The generated optimal route information is sent from the server to the user's device.

[1162] The user's device displays the received optimal route information on a map. The user can check the evacuation route on the map in real time. If the user's emotional state or evacuation situation changes, the device collects new emotional data and sends it again to the emotion engine. The server recalculates the optimal route based on the user's latest state and updates it in real time. The user can continue to evacuate safely based on this information.

[1163] As a concrete example, consider a situation in which a user begins evacuation after an earthquake. The user launches a smartphone application and enters attribute information. The device collects data using an acceleration sensor and sends it to a server. The server analyzes this data and estimates the current road surface condition with high accuracy. Furthermore, the emotion engine detects whether the user is feeling anxious and sends this information to the server. The server then generates a safe route that avoids steps and stairs and sends it to the device. The user can then begin evacuation safely based on the displayed route.

[1164] Example prompt sentence:

[1165] After an earthquake, the user launches the smartphone application. The acceleration sensor collects data and sends it to the server. The server analyzes the data and estimates road surface conditions. The emotion engine detects that the user is feeling anxious and sends route information to the device, allowing the user to evacuate safely.

[1166] In this way, the system of the present invention can provide the optimal evacuation route in real time, taking into account the user's emotional state and physical attribute information, thereby realizing support for a quick and safe evacuation.

[1167] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1168] Step 1:

[1169] The user launches the evacuation support application installed on their smartphone. The input is the user's operation, and the output is the application's running status. This prepares the system for evacuation support.

[1170] Step 2:

[1171] The device detects the application launch and activates the built-in acceleration sensor. The input is the application launch detection signal, and the output is the activation of the acceleration sensor. The device uses this to start collecting acceleration data.

[1172] Step 3:

[1173] The device starts collecting acceleration data and sends it to the server at regular intervals. The input is data from the acceleration sensor, and the output is the acceleration data sent to the server. Specifically, the acceleration data is serialized and sent to the server via an HTTP POST request.

[1174] Step 4:

[1175] The server analyzes the received acceleration data in real time. The input is the acceleration data sent from the device, and the output is an estimation of the road surface condition. Specifically, an algorithm is used to distinguish high-frequency vibrations as stairs and gradual fluctuations as slopes, and the amplitude and period of the data are analyzed to determine the road surface condition.

[1176] Step 5:

[1177] The device collects the user's facial recognition data, voice data, and other biometric information and sends it to the emotion engine. The input is biometric data obtained from the camera and microphone, and the output is data for estimating the user's emotions, which is sent to the emotion engine. Specifically, the device appropriately formats the collected data and sends it to the emotion engine.

[1178] Step 6:

[1179] The emotion engine estimates the user's emotional state (e.g., fear, anxiety, relief) and sends that information to the server. The input is the user's biometric data, and the output is estimated emotional state information. Specifically, it analyzes the biometric data, classifies the emotional state using an emotion model, and sends the information to the server.

[1180] Step 7:

[1181] The server generates the optimal evacuation route based on the user's attribute information and emotional state information. The inputs are the user's attribute information, emotional state information, and road surface condition information, and the output is the optimal evacuation route information. Specifically, this information is integrated and an algorithm is applied to calculate the optimal route.

[1182] Step 8:

[1183] The server sends the generated optimal route information to the user's device. The input is the calculated optimal route information, and the output is the route information to be sent to the device. Specifically, the route information is serialized in JSON format and sent to the device via an HTTP POST request.

[1184] Step 9:

[1185] The user's device receives the optimal route information and displays it on a map. The input is the route information sent from the server, and the output is the route displayed in the map app. Specifically, the received route data is drawn on the map using the map API.

[1186] Step 10:

[1187] If the emotional state or evacuation situation changes, the device collects new emotional data and sends it to the emotion engine again. The input is new biometric data, and the output is updated emotional state information. This allows the server to recalculate based on the latest user information.

[1188] (Application example 2)

[1189] 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."

[1190] Conventional evacuation systems mainly generate evacuation routes based on the user's physical attribute information, but because they do not take into account the user's emotional state, they are unable to alleviate psychological anxiety during disasters. Furthermore, there is a lack of a means to provide optimal routes that reflect passengers' emotions in real time, especially when using autonomous vehicles.

[1191] The identification process by the identification 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 receiving acceleration data collected from the user, means for analyzing the received acceleration data and estimating road surface conditions, means for generating an optimal route tailored to the user's attribute information based on the estimated road surface condition information and the user's emotional state information, and means for transmitting the generated optimal route information to the user terminal. This makes it possible to provide a safer and more secure evacuation route that takes the emotional state into consideration.

[1192] "Acceleration data" is data that indicates the movement and speed changes of an object, collected by sensors on the user's device.

[1193] "Road surface conditions" refer to the physical characteristics of the travel route, such as flat ground, steps, slopes, and stairs.

[1194] The "emotional state" is a state of mind estimated based on biometric information such as the user's facial expression and voice, and includes emotions such as anxiety and fear.

[1195] "User attribute information" is information related to the physical characteristics and mode of transportation (e.g., pedestrian, wheelchair user, cyclist) entered by the user.

[1196] The "optimal route" is the best route that allows the user to evacuate safely and efficiently, calculated based on the collected data.

[1197] A "terminal" is an information processing device used by a user, and includes mobile devices such as smartphones equipped with a sensitivity sensor, a camera, and the like.

[1198] A "server" is a computer system that processes data on a network and sends analysis results and information to terminals.

[1199] The present invention has a configuration in which a user terminal, a server, and an autonomous vehicle system operate in cooperation with each other. A specific embodiment of this system will be described below.

[1200] System configuration

[1201] Hardware and Software

[1202] 1. User device: A smartphone is used. It is equipped with an accelerometer, camera, and microphone, and is used to collect acceleration data and emotion data.

[1203] 2. Server: A computer system that receives and analyzes data. It processes data using Python, OpenCV, and specific libraries (AccelerometerSensor, EmotionEngine).

[1204] 3. Autonomous vehicle systems: receive route information in real time and use it to control the vehicle.

[1205] Program processing

[1206] Acceleration data collection

[1207] The acceleration sensor on the user device (smartphone) is used to collect vehicle vibration data. This is done by an application running on the device. For example, the AccelerometerSensor library is used to obtain acceleration data in real time.

[1208] Recognition of emotional states

[1209] Using the camera and microphone on the user's device, the system recognizes the user's emotional state from their facial expressions and voice. Here, OpenCV and the EmotionEngine library are used to analyze image and audio data and estimate the user's emotions.

[1210] Data transmission and analysis

[1211] The collected acceleration data and emotion data are sent to a server at regular intervals. The server receives this data and analyzes it in real time. The acceleration data is used to estimate road surface conditions, and the emotion data is used to estimate the user's emotional state. The results are then combined to generate the optimal route.

[1212] Generate optimal routes

[1213] The server calculates the optimal evacuation route based on the estimated road surface condition information, emotional state information, and user attribute information (e.g., wheelchair user, pedestrian, cyclist). If the user is feeling particularly anxious, the server prioritizes generating a safe and secure route.

[1214] Route information presentation

[1215] The generated optimal route information is sent from the server to the user terminal and also to the autonomous vehicle system, which controls the vehicle to follow the route.

[1216] Specific examples

[1217] For example, consider a situation where a user begins to evacuate after an earthquake. The user launches an application on their smartphone, which collects data using the accelerometer and camera. The acceleration and emotion data are sent to a server, which analyzes the data and generates an optimal evacuation route. An autonomous vehicle follows the route and assists the user in a safe evacuation.

[1218] Prompt Sentence Examples

[1219] Instructions: Provide optimal routes for passengers in an autonomous vehicle to evacuate after an earthquake disaster. Use accelerometer and camera data and consider the passenger's emotional state. If the passenger feels anxious, prioritize the safe route.

[1220] This invention can provide a safer and more secure evacuation route by taking into consideration the emotional state of the user.

[1221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1222] Step 1:

[1223] The user launches the smartphone application, which prepares for evacuation.

[1224] Step 2:

[1225] The device (smartphone) enables the accelerometer sensor and starts collecting acceleration data. Specifically, the device continuously acquires acceleration data using the AccelerometerSensor library. The input is data from the device's accelerometer sensor, and the output is the acquired acceleration data.

[1226] Step 3:

[1227] The device uses a camera and microphone to collect the user's facial expressions and voice to recognize their emotional state. Here, the OpenCV library is used to analyze image data, and the EmotionEngine library is used to estimate emotions. The input is video and audio data from the camera and microphone, and the output is the estimated emotional state.

[1228] Step 4:

[1229] The collected acceleration data and emotion data are sent to a server at regular intervals. The input is the acceleration data and emotion data, and the output is the result of the data transmission to the server.

[1230] Step 5:

[1231] The server analyzes the received acceleration data and estimates road surface conditions. Specifically, it identifies road surface conditions such as flat ground, steps, slopes, and stairs in real time based on the fluctuation patterns of the acceleration data. The input is acceleration data, and the output is estimated road surface condition information.

[1232] Step 6:

[1233] The server analyzes the received emotion data and estimates the user's emotional state. Specifically, it uses the EmotionEngine to recognize emotions from facial expressions and voice data. The input is emotion data, and the output is the estimated emotional state.

[1234] Step 7:

[1235] The server generates the optimal route based on the estimated road surface condition information, emotional state information, and user attribute information (previously entered information such as whether the user is a wheelchair user, pedestrian, or cyclist). The input is road surface condition information, emotional state information, and attribute information, and the output is the generated optimal route information.

[1236] Step 8:

[1237] The generated optimal route information is sent from the server to the user terminal. It is also sent simultaneously to the autonomous vehicle system. The input is the optimal route information, and the output is the sent route information.

[1238] Step 9:

[1239] The user device displays the received optimal route information on a map, allowing the user to check the optimal evacuation route in real time. Specifically, the route is visually displayed using a map application. The input is the received route information, and the output is the route display on the map.

[1240] Step 10:

[1241] The autonomous vehicle system controls the vehicle along the received optimal route and evacuates the user. The input is the optimal route information, and the output is the vehicle's driving path.

[1242] This system provides the optimal evacuation route in real time, taking into account the user's emotional state, enabling a quick and safe evacuation.

[1243] 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.

[1244] 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.

[1245] 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 robot 414.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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).

[1250] 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, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is 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.

[1251] 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."

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] The following is further disclosed regarding the above embodiment.

[1265] (Claim 1)

[1266] means for receiving collected acceleration data from a user;

[1267] means for analyzing the received acceleration data and estimating road surface conditions;

[1268] A means for generating an optimal route based on the estimated road surface condition information and adapted to the user's attribute information;

[1269] means for transmitting the generated optimal route information to a user terminal;

[1270] A system including:

[1271] (Claim 2)

[1272] The system according to claim 1, wherein the estimated road surface condition is detected as being one of flat ground, a step, a slope, and stairs.

[1273] (Claim 3)

[1274] 2. The system according to claim 1, wherein the user's attribute information detects whether the user is a pedestrian, a wheelchair user, or a bicycle user.

[1275] "Example 1"

[1276] (Claim 1)

[1277] means for receiving acceleration data collected from a user at regular intervals;

[1278] A means for analyzing the received acceleration data in real time and estimating road surface conditions based on fluctuation patterns of the data;

[1279] means for transmitting attribute information input by a user from a terminal to a server;

[1280] A means for generating an optimal route by combining the estimated road surface condition information and user attribute information;

[1281] means for transmitting the generated optimal route information to a user terminal;

[1282] means for updating the route information each time the user terminal collects new acceleration data;

[1283] A system including:

[1284] (Claim 2)

[1285] 2. The system according to claim 1, wherein the estimated road surface condition is determined to be one of a plurality of conditions including flat ground, a step, a slope, and stairs.

[1286] (Claim 3)

[1287] 2. The system according to claim 1, wherein the user's attribute information is determined to be one of a plurality of attributes including a pedestrian, a wheelchair user, and a bicycle user.

[1288] "Application Example 1"

[1289] (Claim 1)

[1290] means for receiving collected acceleration data from a user;

[1291] means for analyzing the received acceleration data and estimating road surface conditions;

[1292] A means for generating an optimal route based on the estimated road surface condition information and adapted to the user's attribute information;

[1293] means for transmitting the generated optimal route information to a user terminal;

[1294] a means for controlling a navigation system of the vehicle and automatically driving the vehicle based on the optimum route;

[1295] A system including:

[1296] (Claim 2)

[1297] The system according to claim 1, wherein the estimated road surface condition is detected as being one of flat ground, a step, a slope, and stairs.

[1298] (Claim 3)

[1299] The system according to claim 1, wherein the user's attribute information is detected as being one of a pedestrian, a wheelchair user, a bicycle user, and an autonomous vehicle user.

[1300] "Example 2: Combining Emotion Engines"

[1301] (Claim 1)

[1302] means for receiving collected acceleration data from a user;

[1303] means for analyzing the received acceleration data and estimating road surface conditions;

[1304] A means for generating an optimal route based on the estimated road surface condition information and the user's emotional state information, and the optimal route is generated in accordance with the user's attribute information;

[1305] means for transmitting the generated optimal route information to a user terminal;

[1306] A system including:

[1307] (Claim 2)

[1308] The system according to claim 1, wherein the estimated road surface condition is detected as being one of flat ground, a step, a slope, and stairs.

[1309] (Claim 3)

[1310] 2. The system according to claim 1, wherein the user's attribute information detects whether the user is a pedestrian, a wheelchair user, or a bicycle user.

[1311] (Claim 4)

[1312] 10. The system of claim 1, wherein the system recognizes that the user's emotional state is one of fear, anxiety, or relief.

[1313] "Application example 2 when combining emotion engines"

[1314] (Claim 1)

[1315] means for receiving collected acceleration data from a user;

[1316] means for analyzing the received acceleration data and estimating road surface conditions;

[1317] A means for generating an optimal route based on the estimated road surface condition information and the user's emotional state information, and the optimal route is generated in accordance with the user's attribute information;

[1318] means for transmitting the generated optimal route information to a user terminal;

[1319] A system including:

[1320] (Claim 2)

[1321] The system according to claim 1, wherein the estimated road surface condition is detected as being one of flat ground, a step, a slope, and stairs.

[1322] (Claim 3)

[1323] 2. The system according to claim 1, wherein the user's attribute information detects whether the user is a pedestrian, a wheelchair user, or a bicycle user. [Explanation of symbols]

[1324] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving collected acceleration data from a user; means for analyzing the received acceleration data and estimating road surface conditions; A means for generating an optimal route based on the estimated road surface condition information and adapted to the user's attribute information; means for transmitting the generated optimal route information to a user terminal; A system including:

2. The system according to claim 1, wherein the estimated road surface condition is detected as being one of flat ground, a step, a slope, and stairs.

3. The system according to claim 1, wherein the user's attribute information is detected as being one of a pedestrian, a wheelchair user, and a bicycle user.

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