system

Abstract

We provide the system. [Solution] A camera that acquires image data to identify wheelchair users, visually impaired persons, and preschool children, An analysis device that analyzes acquired image data and identifies the characteristics of the target, Based on the analysis results, a notification device is provided to alert those in the surrounding area that a target is approaching. A voice output device for providing voice guidance to a target, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need to reduce the safety risks faced by wheelchair users, visually impaired people, and preschool children in public places or temporarily unstable situations, and to provide an environment in which they can move with confidence. There is a need for a system that can efficiently and quickly assist in situations where such targets cannot receive support from the surroundings and have difficulty accessing information.

Means for Solving the Problems

[0005] The present invention provides a system that includes a camera that acquires image data to identify target wheelchair users, visually impaired persons, and preschool children, and an analysis device that analyzes the acquired image data to identify the characteristics of the target. Furthermore, it provides a system that includes a notification device that alerts those around the target when the target is approaching based on the analysis results, and an audio output device that provides the target with necessary information and safe movement methods via voice, thereby supporting the safe and secure movement of the target.

[0006] A "wheelchair user" refers to a person who uses a wheelchair to move around because walking is difficult for physical reasons.

[0007] "Visually impaired" refers to a person whose eyesight is impaired or who has difficulty perceiving visual information.

[0008] "Preschool children" refers to young children who have not yet legally begun schooling.

[0009] "Image capture equipment" refers to devices such as cameras and sensors used to capture image data.

[0010] An "analysis device" refers to a computer system that analyzes acquired image data and identifies specific objects or features from it.

[0011] A "notification device" refers to a means or technology for informing specific people or systems of information based on analysis results.

[0012] A "speech output device" refers to equipment that provides information through speech based on analysis results, including speakers and speech synthesis technology.

[0013] "Target" refers to individuals who require specific support, such as wheelchair users, visually impaired people, and preschool children. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely in public places, using an imaging device for acquiring image data, a server for analysis, and a terminal for notifications and voice guidance.

[0036] First, the camera is installed in a specific facility or public space to acquire video data in real time. This video data is sent to a server, which is responsible for analyzing the data.

[0037] The server uses machine learning algorithms to identify and recognize the characteristics of target individuals such as wheelchair users, visually impaired individuals, and preschool children from image data. This ensures that the system is ready to respond immediately when a target is detected.

[0038] Once the analysis is complete, the server sends a notification to the device based on the target's identification information. This notification is displayed on devices carried by facility staff and related personnel, alerting them to provide immediate support.

[0039] Meanwhile, terminals equipped with voice output devices are responsible for providing voice guidance to the target. This guidance includes specific instructions on which route the target should take and how to move safely. This allows the target to move safely based on their own judgment.

[0040] As a concrete example, consider a scenario where a wheelchair user approaches the ticket gate within a train station. In this case, the terminal sends a notification to station staff stating, "A wheelchair user is approaching," and simultaneously plays an audio guidance message saying, "Please use the priority lane," thereby supporting the target's safe movement.

[0041] Thus, the present invention provides an environment in which the target can safely engage in activities in public places by using a combination of a shooting device, an analysis server, a notification terminal, and an audio output terminal.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives video data from the camera in real time. This data contains image information for identifying the target.

[0045] Step 2:

[0046] The server performs preprocessing on the received video data. This preprocessing removes noise from the image and converts it into a format suitable for analysis. This conversion allows for more accurate analysis.

[0047] Step 3:

[0048] The server uses machine learning algorithms to analyze pre-processed data. Here, it identifies characteristics of wheelchair users, visually impaired individuals, and preschool children, and detects the approach of these targets.

[0049] Step 4:

[0050] Once the server successfully identifies the target, it generates a notification message based on the relevant information. This message is prepared for reception by facility personnel and staff.

[0051] Step 5:

[0052] The server sends the generated notification message to the terminal. This terminal could be a device carried by the designated recipient or a fixed notification display.

[0053] Step 6:

[0054] The terminal checks and immediately displays notification messages received from the server. Based on this notification, nearby staff and personnel can go to the target's location to provide support if necessary.

[0055] Step 7:

[0056] The terminal uses an audio output device to generate voice guidance for the target. This guidance includes safe route selection and precautions to help the target move safely.

[0057] Step 8:

[0058] The target user can select the appropriate route and begin their journey safely based on the voice guidance provided by the device.

[0059] Through these steps, the system supports the safe movement of the target.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] To support the safe movement of wheelchair users, visually impaired individuals, and preschool children in public spaces, it is necessary to accurately identify these targets in real time and provide voice guidance for safe routes. However, current systems lack sufficient target identification accuracy and notification speed, making it difficult to ensure adequate safety. A solution to this problem is needed.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for transmitting image information acquired by the imaging device in a public space to the server, means for the server to analyze the image information using a machine learning algorithm and identify the location information and characteristics of the target, and notification means for sending a notification to staff based on the information identified by the server. This enables real-time target identification, immediate response, and guidance of a safe route.

[0065] A "photography device" is a device used to acquire image information in a public space and transmit that information to a server.

[0066] A "server" is a device that receives acquired image information and performs analysis using machine learning algorithms.

[0067] A "machine learning algorithm" is a computational method used for data analysis to identify the location and features of a target from image information.

[0068] "Target" refers to wheelchair users, visually impaired individuals, and preschool children in public places, who are the target of identification and support by the system.

[0069] A "notification method" is a means of sending immediate notifications to facility staff based on information identified by the server.

[0070] A "voice output device" is a device that provides voice guidance to a target, guiding them along a safe route and conveying instructions.

[0071] A "generative AI model" is an artificial intelligence model used to continuously improve the content of voice guidance.

[0072] A "prompt message" is a sentence containing questions or instructions designed to collect user feedback and improve the accuracy of analysis.

[0073] This invention is a system that assists wheelchair users, visually impaired individuals, and preschool children in safely moving around in public spaces. The system is implemented by combining multiple devices.

[0074] First, a camera is installed within the facility to acquire image information in real time. This image information is crucial data for identifying targets (wheelchair users, visually impaired individuals, and preschool children). The image information acquired by the camera is immediately transmitted to a server.

[0075] The server uses machine learning algorithms that leverage generative AI models to analyze the received image information. This algorithm allows the server to identify the target's location and characteristics. For example, it can determine whether a person in an image is a wheelchair user based on their posture and movement.

[0076] After the server identifies the target, that information is provided to facility staff using a notification system. The notification may include a message such as, "A wheelchair user is approaching Area A."

[0077] Furthermore, terminals equipped with voice output devices provide voice guidance to the target. This guidance includes instructions for safe travel routes and specific instructions such as "Please use the priority lane." The content of this voice guidance is continuously improved by a generative AI model and adjusted using prompts based on user feedback.

[0078] As a concrete example, consider a scenario where a wheelchair user is heading towards the ticket gate in a train station. In this case, the terminal sends a notification to staff stating, "A wheelchair user is approaching," and simultaneously provides voice guidance saying, "Please use the elevator." Through this series of operations, the target user can move safely based on their own judgment.

[0079] An example of a prompt message would be a question like, "How can we make the transition smoother?" This would facilitate the development of more effective support measures and guidance methods.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server receives image information transmitted from the imaging device. The input is real-time image data. This image data includes spatial information within the facility and the movements of users. The server temporarily stores this data and prepares it for analysis.

[0083] Step 2:

[0084] The server analyzes the received image information. This analysis uses a machine learning algorithm that leverages a generative AI model. The input is the image data received in step 1, and the output is data identifying the target's location and characteristics. The specific actions include analyzing the posture and movement patterns of people in the image and identifying targets (wheelchair users, visually impaired individuals, preschool children) based on specific features.

[0085] Step 3:

[0086] The server uses a notification system based on the analysis results to send target information to facility staff. The input is the target identification information obtained in step 2, and the output is a notification message such as "Wheelchair user approaching area A." The specific action includes immediately distributing the generated message to the staff's mobile devices.

[0087] Step 4:

[0088] The terminal provides voice guidance to the target using an audio output device. The input is the target's location and travel route information obtained in step 2, and the output is a voice message such as "Please use the priority lane." The specific operation is to transmit a pre-set message to the target using speech synthesis technology.

[0089] Step 5:

[0090] The user safely follows the designated route while listening to voice guidance. The input here is voice instructions from the terminal. The user's output is movement along the safe route, allowing the user to decide their own path and ensure safe passage.

[0091] In this way, each step works in conjunction to form a system that supports safe movement in public spaces.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In public spaces, prompt and appropriate assistance is necessary for wheelchair users, visually impaired individuals, and preschool children to move safely. However, traditional methods may result in delayed responses or compromise safety due to a lack of manpower and information. To address these challenges, a more efficient and accurate support system is needed.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring image information using a camera, means for utilizing machine learning to analyze the acquired image information and identify the characteristics of a target, and means for using a communication device to transmit notifications to the target based on the results of the analysis. This enables real-time improvement of safety in public places and allows for quick and accurate assistance.

[0097] A "photography device" is a device installed in a specific environment to acquire images or videos.

[0098] "Image information" refers to visual data acquired by an imaging device, and is the subject of analysis.

[0099] "Analysis" is the process of identifying specific features or patterns based on acquired image information.

[0100] "Machine learning" is a technology that allows computers to learn patterns from data and perform tasks such as identification and prediction.

[0101] A "communication device" is a device used to transmit the results of the analysis to the subject or staff as a notification.

[0102] "Target audience" refers to individuals or groups who require specific support, such as wheelchair users, visually impaired individuals, and preschool children.

[0103] A "voice device" is a device that provides instructions or guidance to a target through voice information.

[0104] "Real-time" refers to a state in which information processing and data communication occur instantly without delay.

[0105] A "route" refers to the direction of travel or path that guides an object to move safely.

[0106] To implement this invention, it is first necessary to install a camera in a public facility or a specific environment and acquire image information in real time. The image information obtained from the camera is transmitted to a server using wireless communication technology.

[0107] The server uses software such as TENSORFLOW®, a machine learning platform, to analyze the acquired image information. This analysis identifies the target individuals—wheelchair users, visually impaired individuals, and preschool children—and identifies their characteristics. The identified information is then communicated to staff and equipment via communication devices.

[0108] Meanwhile, the subject will be provided with guidance on the route to take and points to note using a voice device and synthesized speech technology. This voice guidance will help the subject move safely without getting lost.

[0109] In a specific example using a shopping mall, this system allows security staff to patrol using smart glasses and receive notifications such as, "There is a person in a wheelchair near the escalator on the second floor. We recommend using the elevator," enabling them to provide necessary support quickly on the spot.

[0110] When using a generative AI model, an example prompt would be: "Consider the notification and guidance that security staff using smart glasses in a shopping mall should receive. The targets are wheelchair users and visually impaired individuals."

[0111] This enables the provision of prompt and accurate support.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server activates the camera and captures real-time video footage from within the public facility. The acquired image information is then input to the server. The image information is processed only and not stored, in a manner that respects privacy.

[0115] Step 2:

[0116] The server uses a machine learning model to analyze the acquired image information. In this step, TensorFlow is used to identify subjects within the image (wheelchair users, visually impaired individuals, preschool children). As a result of the analysis, feature information and location data of the subjects are generated and output to the server.

[0117] Step 3:

[0118] Based on the analysis results, the server uses a communication device to send a notification to the terminal. This notification includes information about the presence and location of the target and is output to the staff member's mobile device. The notification includes a message prompting specific action.

[0119] Step 4:

[0120] The user (staff) checks the device and takes the necessary action according to the notification content. For example, they might go to the designated location and prepare to support the person in question.

[0121] Step 5:

[0122] The server provides voice guidance to the target via a voice device. Using synthesized speech technology, it generates and outputs pre-configured guidance messages. This informs the target of safe routes and points of interest.

[0123] Step 6:

[0124] The user (target) follows voice guidance to move safely to their destination. While staff assistance may be available in some cases, voice guidance is the primary support for movement.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely and securely in public places, using a camera that acquires and analyzes image data, a server incorporating an emotion engine that identifies emotions, and a terminal that provides notifications and voice guidance.

[0127] First, the camera is installed in the facility or public space and continuously records the surrounding environment. The video data is sent to a server where image preprocessing is performed. The server uses the preprocessed data to run machine learning algorithms to identify targets. This allows for the identification of wheelchair users, visually impaired individuals, and preschool children.

[0128] Furthermore, the emotion engine, a distinctive element of this invention, analyzes the user's facial expressions and voice patterns to estimate their emotional state. This information is used to assess the user's sense of security and stress levels, and to provide appropriate support.

[0129] Once the analysis is complete, the server generates notification messages and voice guidance based on the user's identification and emotional state. The notifications are sent to the facility's staff terminals, allowing staff to take immediate action.

[0130] In addition to receiving notifications, the device provides users with appropriate voice guidance through its voice output device. The tone and content of the voice guidance are adjusted according to the user's emotional state. For example, if the user is feeling anxious, a gentle voice guidance will be provided to reassure them. This feature allows users to feel psychologically at ease and reach their destination safely.

[0131] As a concrete example, when a visually impaired person is trying to reach their destination in a shopping mall, this system can detect the user's anxiety and provide reassuring guidance such as, "Don't worry, there is a staff member nearby." This allows the user to have a more fulfilling experience.

[0132] Thus, the present invention enhances safety and security in public places by supporting the target's movement from an emotional perspective.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server receives video data in real time from the camera. The camera is installed within the facility or in a specific public area and records video to identify targets.

[0136] Step 2:

[0137] The server preprocesses the received video data. This process removes image noise and prepares the data for easier analysis. This process is crucial for improving the accuracy of subsequent analysis.

[0138] Step 3:

[0139] The server applies machine learning algorithms to identify target features from pre-processed data. These algorithms are trained to recognize wheelchair users, visually impaired individuals, and preschool children.

[0140] Step 4:

[0141] When a target is identified, the server simultaneously activates the emotion engine to analyze the user's facial expressions and voice patterns. By estimating the emotional state, it obtains information to design an appropriate response.

[0142] Step 5:

[0143] The server generates notification messages based on the target's identification and emotional state. These notifications are sent to facility staff in real time, allowing them to instantly understand the situation.

[0144] Step 6:

[0145] The device receives notifications from the server and displays them on the screen. The notification content includes the target's location information and warnings tailored to the target's emotional state.

[0146] Step 7:

[0147] The terminal sends instructions to the voice output device, which then plays voice guidance tailored to the user. This guidance is designed to provide a sense of security through its tone and content, which are aligned with the user's emotional state.

[0148] Step 8:

[0149] Users begin their journey based on voice guidance from their devices. This reduces user anxiety and ensures safe and smooth travel.

[0150] Through this process, the system provides technical and emotional support to help targets operate more safely in public spaces.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] In recent years, there has been a lack of adequate support for target groups who require safe and secure movement in public spaces, such as wheelchair users, visually impaired individuals, and preschool children. Particular attention is needed in situations requiring psychological considerations. However, current technology often only provides basic support such as user location information and simple directions. Therefore, personalized guidance that takes into account the user's psychological state is not provided, resulting in an environment where users can move with peace of mind. Consequently, there is a need for a system that analyzes the user's psychological state in real time and provides adaptive voice guidance based on that analysis.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes means for acquiring image information of the environment including a target user, computation means for preprocessing the acquired image information and identifying the user, and means for analyzing voice patterns and facial expressions to estimate the user's emotional state. This makes it possible to provide adaptive voice guidance that takes the user's psychological state into consideration.

[0156] "Target" refers to a specific user group, such as wheelchair users, visually impaired individuals, and preschool children, who require safe and secure transportation.

[0157] "Image information" refers to visual data acquired using a camera or imaging device, and is information used to visualize a specific environment or the state of a user.

[0158] "Preprocessing" refers to a series of processes performed to prepare acquired raw data for analysis, and includes techniques such as image resizing and noise reduction.

[0159] "Computation means" refers to information processing devices or processes that execute programs based on acquired information to derive specific results.

[0160] "Emotional state" refers to a state that indicates psychological characteristics such as a user's level of psychological stability and anxiety.

[0161] "Voice patterns" are a concept used as a model to analyze the characteristic changes and rhythms contained in the sounds a user makes.

[0162] "Methods for analyzing facial expressions" refers to image processing techniques and algorithms used to infer a user's psychological state from their facial expressions.

[0163] "Adaptive voice guidance" refers to a system that provides dynamic voice support, where the content and tone change according to the user's psychological state.

[0164] "Notification information" refers to messages or signals sent to encourage follow-up or support based on the user's status and requests.

[0165] This invention is a system that enables users to move safely and securely in public places. The system consists of a camera, a server, and a terminal, and each of these devices works in coordination.

[0166] First, the imaging devices are installed in each facility and public space to constantly monitor the surrounding environment and acquire image information. The image information obtained by the imaging devices is transmitted to a server via the internet or a dedicated network.

[0167] The server operates using the Python programming language and performs image preprocessing using the OpenCV library. Using the preprocessed data, the server applies machine learning algorithms to identify target individuals: wheelchair users, visually impaired individuals, and preschool children. An artificial intelligence model using TensorFlow is employed for target identification.

[0168] Next, the server uses an emotion analysis engine to analyze the user's facial expressions and voice patterns. The voice data is converted to text using a cloud-based speech recognition service, and then analyzed to estimate the emotional state. This makes it possible to evaluate the user's sense of security and stress level.

[0169] Once the analysis is complete, the server generates appropriate notification messages and voice guidance. The generated notifications are immediately sent to the facility staff's terminals, enabling staff to respond quickly to users. The terminals operate as smartphone or tablet applications, and upon receiving guidance information, they provide instructions to the user via a voice output device. This voice guidance is adjusted in content and tone based on the user's emotional state.

[0170] As a concrete example, consider a visually impaired person searching for their destination in a shopping mall. This system senses their anxiety and provides psychological support by offering voice guidance such as, "Don't worry, there is a staff member nearby."

[0171] An example of a prompt to input into a generative AI model is, "Describe a method for providing emotion-responsive audio guidance to visually impaired people in public spaces." Using this prompt, the AI ​​model is expected to generate a detailed description of the system.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] A camera is installed in a public space to continuously capture images of the surrounding environment. The input for this step is real-time visual information of the environment, and the output is digital image data. This image data is acquired to monitor user movement within the facility. The camera has excellent resolution and provides clear images even under diverse lighting conditions.

[0175] Step 2:

[0176] The server receives image data transmitted from the imaging device. The input image data is high-resolution color images, which are used for image preprocessing. Preprocessing includes noise reduction, image resizing, and format conversion. The output image data is generated in a format that facilitates analysis. This process uses Python and the OpenCV library.

[0177] Step 3:

[0178] The server runs an artificial intelligence model using pre-processed data. The input for this step is formatted image data, and the output is a list of identified targets. The server utilizes TensorFlow to run a model designed to identify wheelchair users, visually impaired individuals, and preschool children. The model identifies targets with high accuracy based on patterns learned from the database.

[0179] Step 4:

[0180] The server uses target information to operate an emotion analysis engine and evaluate the target's psychological state. The input is video and audio data of the identified target, and the output is an estimated result of their emotional state. The server analyzes audio patterns and reads emotions from the user's facial expressions. It uses Google Cloud's speech recognition service to convert the audio to text and performs emotion analysis using DeepStream.

[0181] Step 5:

[0182] The server generates notification messages and voice guidance based on emotional states. Inputs are estimated emotion data and target information, while outputs are specific notification content and voice guidance content. The server uses a Python script to dynamically create content that provides reassurance to the user.

[0183] Step 6:

[0184] The terminal receives notifications from the server and displays them as push notifications on the staff terminal. The input is notification data from the server, and the output is the notification message on the terminal's display. It operates on smartphones and tablets, prompting immediate action.

[0185] Step 7:

[0186] The terminal provides voice guidance to the user through an audio output device. The input is generated voice guide content, and the output is clear and well-tuned voice guidance. Using AWS® voice services, it provides reassurance to the user with a human-like voice. It genuinely supports the user with concrete examples such as "There is a staff member nearby."

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In public spaces, it is essential that wheelchair users, visually impaired individuals, and preschool children move safely, but it is difficult to alleviate the associated psychological anxiety and stress and provide appropriate support. Conventional systems can address physical obstacle avoidance, but they do not go beyond providing support that takes into account the emotional state of the user. Therefore, there is a need to develop a system that can respond flexibly based on the emotional state of the user and support safer and more secure movement.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes a shooting means for acquiring image information to identify the user, an analysis means for analyzing the acquired image information and identifying the characteristics of the subject, and an emotion analysis means for analyzing the emotional state and providing psychological support. This makes it possible to provide not only safe movement for the user but also emotional support simultaneously.

[0192] "Photography means" refers to a device that acquires image information in order to identify the user.

[0193] An "analysis means" is a device that analyzes acquired image information and identifies the characteristics of the target.

[0194] A "means of communication" is a device that, based on the analysis results, notifies those around that an object is approaching.

[0195] "Voice output means" refers to a device that provides voice guidance to an object.

[0196] An "emotional analysis device" is a device used to analyze emotional states and provide psychological support.

[0197] A "learning algorithm" is an algorithm used to identify an object in an analytical method, and it improves the performance of the model based on data.

[0198] This invention is a system designed to enable wheelchair users, visually impaired individuals, and preschool children to move safely and securely within public facilities. The system consists of a shooting means for acquiring image information, an analysis means for identifying the characteristics of an object, an emotion analysis means for analyzing emotional states, and an audio output means for providing voice guidance.

[0199] The server first acquires user image data in real time using imaging devices (e.g., network cameras) installed within the facility. The acquired image information is then analyzed using image analysis software (e.g., OpenCV or TensorFlow) to identify the user. The analysis method identifies the user's movements and characteristics through a learning algorithm and processes the data according to the situation.

[0200] Next, emotion analysis is used to determine the user's emotional state based on their facial expressions and voice information. Emotion analysis software (e.g., EmotionEngine) is used for this purpose. Based on this information, the server understands the user's current state and generates optimal voice guidance to provide psychological support.

[0201] The generated voice guidance is delivered to the user's smartphone or smart glasses via an audio output device. The voice guidance is adjusted in tone and content to reassure the user. For example, if a visually impaired person is heading to their destination in a shopping mall, the system can sense their anxiety and provide a gentle message such as, "Don't worry, there is a staff member nearby to assist you."

[0202] Such a system ensures the safety of movement and allows users to move around the facility with psychological peace of mind. An example of a prompt message would be: "Analyze the user's video and audio data to determine their stress level and provide appropriate guidance messages."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server acquires image data in real time from cameras installed within the facility. The input is a video stream from the cameras, which is then divided into individual frames. The resulting frames are sent to the next analysis step.

[0206] Step 2:

[0207] The server analyzes the acquired image data to identify the user. The input is the image frame acquired in step 1. The server uses image analysis software to execute face recognition and motion detection algorithms and outputs characteristic data of the identified user.

[0208] Step 3:

[0209] The server performs emotion analysis. The input is the user's characteristic data output in step 2, which the server inputs into emotion analysis software (e.g., EmotionEngine). It analyzes facial expressions and voice patterns and outputs data that quantifies the user's emotional state.

[0210] Step 4:

[0211] The server generates voice guidance based on the emotion analysis results. The input is numerical data of the emotional state obtained in step 3. The server uses a generative AI model to create the optimal voice message and outputs it as audio data in digital format.

[0212] Step 5:

[0213] The device delivers the audio data received from the server to the user. The input is the audio data generated by the server in step 4, which is played back through the speaker of the smartphone or smart glasses. This allows the user to listen to the audio guidance and gain reassurance tailored to their situation.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0230] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely in public places, using an imaging device for acquiring image data, a server for analysis, and a terminal for notifications and voice guidance.

[0231] First, the camera is installed in a specific facility or public space to acquire video data in real time. This video data is sent to a server, which is responsible for analyzing the data.

[0232] The server uses machine learning algorithms to identify and recognize the characteristics of target individuals such as wheelchair users, visually impaired individuals, and preschool children from image data. This ensures that the system is ready to respond immediately when a target is detected.

[0233] Once the analysis is complete, the server sends a notification to the device based on the target's identification information. This notification is displayed on devices carried by facility staff and related personnel, alerting them to provide immediate support.

[0234] Meanwhile, terminals equipped with voice output devices are responsible for providing voice guidance to the target. This guidance includes specific instructions on which route the target should take and how to move safely. This allows the target to move safely based on their own judgment.

[0235] As a concrete example, consider a scenario where a wheelchair user approaches the ticket gate within a train station. In this case, the terminal sends a notification to station staff stating, "A wheelchair user is approaching," and simultaneously plays an audio guidance message saying, "Please use the priority lane," thereby supporting the target's safe movement.

[0236] Thus, the present invention provides an environment in which the target can safely engage in activities in public places by using a combination of a shooting device, an analysis server, a notification terminal, and an audio output terminal.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server receives video data from the camera in real time. This data contains image information for identifying the target.

[0240] Step 2:

[0241] The server performs preprocessing on the received video data. This preprocessing removes noise from the image and converts it into a format suitable for analysis. This conversion allows for more accurate analysis.

[0242] Step 3:

[0243] The server uses machine learning algorithms to analyze pre-processed data. Here, it identifies characteristics of wheelchair users, visually impaired individuals, and preschool children, and detects the approach of these targets.

[0244] Step 4:

[0245] Once the server successfully identifies the target, it generates a notification message based on the relevant information. This message is prepared for reception by facility personnel and staff.

[0246] Step 5:

[0247] The server sends the generated notification message to the terminal. This terminal could be a device carried by the designated recipient or a fixed notification display.

[0248] Step 6:

[0249] The terminal checks and immediately displays notification messages received from the server. Based on this notification, nearby staff and personnel can go to the target's location to provide support if necessary.

[0250] Step 7:

[0251] The terminal uses an audio output device to generate voice guidance for the target. This guidance includes safe route selection and precautions to help the target move safely.

[0252] Step 8:

[0253] The target user can select the appropriate route and begin their journey safely based on the voice guidance provided by the device.

[0254] Through these steps, the system supports the safe movement of the target.

[0255] (Example 1)

[0256] Next, we will describe Example 1. 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."

[0257] To support the safe movement of wheelchair users, visually impaired individuals, and preschool children in public spaces, it is necessary to accurately identify these targets in real time and provide voice guidance for safe routes. However, current systems lack sufficient target identification accuracy and notification speed, making it difficult to ensure adequate safety. A solution to this problem is needed.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for transmitting image information acquired by the imaging device in a public space to the server, means for the server to analyze the image information using a machine learning algorithm and identify the location information and characteristics of the target, and notification means for sending a notification to staff based on the information identified by the server. This enables real-time target identification, immediate response, and guidance of a safe route.

[0260] A "photography device" is a device used to acquire image information in a public space and transmit that information to a server.

[0261] A "server" is a device that receives acquired image information and performs analysis using machine learning algorithms.

[0262] A "machine learning algorithm" is a computational method used for data analysis to identify the location and features of a target from image information.

[0263] "Target" refers to wheelchair users, visually impaired individuals, and preschool children in public places, who are the target of identification and support by the system.

[0264] A "notification method" is a means of sending immediate notifications to facility staff based on information identified by the server.

[0265] A "voice output device" is a device that provides voice guidance to a target, guiding them along a safe route and conveying instructions.

[0266] A "generative AI model" is an artificial intelligence model used to continuously improve the content of voice guidance.

[0267] A "prompt message" is a sentence containing questions or instructions designed to collect user feedback and improve the accuracy of analysis.

[0268] This invention is a system that assists wheelchair users, visually impaired individuals, and preschool children in safely moving around in public spaces. The system is implemented by combining multiple devices.

[0269] First, a camera is installed within the facility to acquire image information in real time. This image information is crucial data for identifying targets (wheelchair users, visually impaired individuals, and preschool children). The image information acquired by the camera is immediately transmitted to a server.

[0270] The server uses machine learning algorithms that leverage generative AI models to analyze the received image information. This algorithm allows the server to identify the target's location and characteristics. For example, it can determine whether a person in an image is a wheelchair user based on their posture and movement.

[0271] After the server identifies the target, that information is provided to facility staff using a notification system. The notification may include a message such as, "A wheelchair user is approaching Area A."

[0272] Furthermore, terminals equipped with voice output devices provide voice guidance to the target. This guidance includes instructions for safe travel routes and specific instructions such as "Please use the priority lane." The content of this voice guidance is continuously improved by a generative AI model and adjusted using prompts based on user feedback.

[0273] As a concrete example, consider a scenario where a wheelchair user is heading towards the ticket gate in a train station. In this case, the terminal sends a notification to staff stating, "A wheelchair user is approaching," and simultaneously provides voice guidance saying, "Please use the elevator." Through this series of operations, the target user can move safely based on their own judgment.

[0274] An example of a prompt message would be a question like, "How can we make the transition smoother?" This would facilitate the development of more effective support measures and guidance methods.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The server receives image information transmitted from the imaging device. The input is real-time image data. This image data includes spatial information within the facility and the movements of users. The server temporarily stores this data and prepares it for analysis.

[0278] Step 2:

[0279] The server analyzes the received image information. For this analysis, a machine learning algorithm utilizing a generative AI model is used. The input is the image data received in Step 1, and the output is data that identifies the position information and characteristics of the target. The specific operations include analyzing the posture and movement patterns of people in the image and identifying the target (wheelchair users, visually impaired people, preschool children) based on specific features.

[0280] Step 3:

[0281] Based on the analysis results, the server uses the notification means to transmit the target information to the facility staff. The input is the identification information of the target obtained in Step 2, and the output is a notification message such as "A wheelchair user is approaching Area A". The specific operations include immediately distributing the generated message to the staff's mobile terminals.

[0282] Step 4:

[0283] The terminal uses the voice output device to provide voice guidance to the target. The input is the position and movement route information of the target obtained in Step 2, and the output is a voice guidance such as "Please use the priority passage". The specific operations are to transmit a preset message to the target using voice synthesis technology.

[0284] Step 5:

[0285] The user moves safely along the designated route while listening to the voice guidance. The input here is the voice instruction from the terminal. The user's output is the movement along a safe route, by which the user determines the movement route based on their own judgment and ensures safe passage.

[0286] In this way, each step operates in cooperation to constitute a system that supports safe movement in public spaces.

[0287] (Application Example 1)

[0288] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0289] In public spaces, prompt and appropriate assistance is necessary for wheelchair users, visually impaired individuals, and preschool children to move safely. However, traditional methods may result in delayed responses or compromise safety due to a lack of manpower and information. To address these challenges, a more efficient and accurate support system is needed.

[0290] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for acquiring image information using a camera, means for utilizing machine learning to analyze the acquired image information and identify the characteristics of a target, and means for using a communication device to transmit notifications to the target based on the results of the analysis. This enables real-time improvement of safety in public places and allows for quick and accurate assistance.

[0292] A "photography device" is a device installed in a specific environment to acquire images or videos.

[0293] "Image information" refers to visual data acquired by an imaging device, and is the subject of analysis.

[0294] "Analysis" is the process of identifying specific features or patterns based on acquired image information.

[0295] "Machine learning" is a technology that allows computers to learn patterns from data and perform tasks such as identification and prediction.

[0296] A "communication device" is a device used to transmit the results of the analysis to the subject or staff as a notification.

[0297] "Target audience" refers to individuals or groups who require specific support, such as wheelchair users, visually impaired individuals, and preschool children.

[0298] A "voice device" is a device that provides instructions or guidance to a target through voice information.

[0299] "Real-time" refers to a state in which information processing and data communication occur instantly without delay.

[0300] A "route" refers to the direction of travel or path that guides an object to move safely.

[0301] To implement this invention, it is first necessary to install a camera in a public facility or a specific environment and acquire image information in real time. The image information obtained from the camera is transmitted to a server using wireless communication technology.

[0302] The server uses software such as TensorFlow, a machine learning platform, to analyze the acquired image information. This analysis identifies the target individuals—wheelchair users, visually impaired individuals, and preschool children—and identifies their characteristics. The identified information is then communicated to staff and equipment via a communication device.

[0303] Meanwhile, the subject will be provided with guidance on the route to take and points to note using a voice device and synthesized speech technology. This voice guidance will help the subject move safely without getting lost.

[0304] In a specific example using a shopping mall, this system allows security staff to patrol using smart glasses and receive notifications such as, "There is a person in a wheelchair near the escalator on the second floor. We recommend using the elevator," enabling them to provide necessary support quickly on the spot.

[0305] When using a generative AI model, input it in the form of, for example, "Please consider the notifications and guidance that security staff using smart glasses in a shopping mall should receive. The targets are wheelchair users and visually impaired people."

[0306] This enables the provision of prompt and accurate support.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server activates the imaging device and captures real-time video within the public facility. As a result, the acquired image information is input into the server. The image information is only processed without being stored in a privacy-conscious manner.

[0310] Step 2:

[0311] The server analyzes the acquired image information using a machine learning model. In this step, TensorFlow is utilized to identify the targets (wheelchair users, visually impaired people, preschool children) within the image. As an analysis result, the characteristics and position information of the targets are generated and output to the server.

[0312] Step 3:

[0313] Based on the analysis result, the server uses a communication device to send a notification to the terminal. This notification includes information regarding the presence and position of the target and is output to the staff's mobile terminal. The notification contains a message prompting specific actions.

[0314] Step 4:

[0315] The user (staff) checks the terminal and takes necessary actions according to the notification content. For example, they go to the specified location and prepare to support the target person.

[0316] Step 5:

[0317] The server provides voice guidance to the target via a voice device. Using synthesized speech technology, it generates and outputs pre-configured guidance messages. This informs the target of safe routes and points of interest.

[0318] Step 6:

[0319] The user (target) follows voice guidance to move safely to their destination. While staff assistance may be available in some cases, voice guidance is the primary support for movement.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely and securely in public places, using a camera that acquires and analyzes image data, a server incorporating an emotion engine that identifies emotions, and a terminal that provides notifications and voice guidance.

[0322] First, the camera is installed in the facility or public space and continuously records the surrounding environment. The video data is sent to a server where image preprocessing is performed. The server uses the preprocessed data to run machine learning algorithms to identify targets. This allows for the identification of wheelchair users, visually impaired individuals, and preschool children.

[0323] Furthermore, the emotion engine, a distinctive element of this invention, analyzes the user's facial expressions and voice patterns to estimate their emotional state. This information is used to assess the user's sense of security and stress levels, and to provide appropriate support.

[0324] Once the analysis is complete, the server generates notification messages and voice guidance based on the user's identification and emotional state. The notifications are sent to the facility's staff terminals, allowing staff to take immediate action.

[0325] In addition to receiving notifications, the device provides users with appropriate voice guidance through its voice output device. The tone and content of the voice guidance are adjusted according to the user's emotional state. For example, if the user is feeling anxious, a gentle voice guidance will be provided to reassure them. This feature allows users to feel psychologically at ease and reach their destination safely.

[0326] As a concrete example, when a visually impaired person is trying to reach their destination in a shopping mall, this system can detect the user's anxiety and provide reassuring guidance such as, "Don't worry, there is a staff member nearby." This allows the user to have a more fulfilling experience.

[0327] Thus, the present invention enhances safety and security in public places by supporting the target's movement from an emotional perspective.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server receives video data in real time from the camera. The camera is installed within the facility or in a specific public area and records video to identify targets.

[0331] Step 2:

[0332] The server preprocesses the received video data. This process removes image noise and prepares the data for easier analysis. This process is crucial for improving the accuracy of subsequent analysis.

[0333] Step 3:

[0334] The server applies machine learning algorithms to identify target features from pre-processed data. These algorithms are trained to recognize wheelchair users, visually impaired individuals, and preschool children.

[0335] Step 4:

[0336] When a target is identified, the server simultaneously activates the emotion engine to analyze the user's facial expressions and voice patterns. By estimating the emotional state, it obtains information to design an appropriate response.

[0337] Step 5:

[0338] The server generates notification messages based on the target's identification and emotional state. These notifications are sent to facility staff in real time, allowing them to instantly understand the situation.

[0339] Step 6:

[0340] The device receives notifications from the server and displays them on the screen. The notification content includes the target's location information and warnings tailored to the target's emotional state.

[0341] Step 7:

[0342] The terminal sends instructions to the voice output device, which then plays voice guidance tailored to the user. This guidance is designed to provide a sense of security through its tone and content, which are aligned with the user's emotional state.

[0343] Step 8:

[0344] Users begin their journey based on voice guidance from their devices. This reduces user anxiety and ensures safe and smooth travel.

[0345] Through this process, the system provides technical and emotional support to help targets operate more safely in public spaces.

[0346] (Example 2)

[0347] Next, we will describe Example 2. 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".

[0348] In recent years, there has been a lack of adequate support for target groups who require safe and secure movement in public spaces, such as wheelchair users, visually impaired individuals, and preschool children. Particular attention is needed in situations requiring psychological considerations. However, current technology often only provides basic support such as user location information and simple directions. Therefore, personalized guidance that takes into account the user's psychological state is not provided, resulting in an environment where users can move with peace of mind. Consequently, there is a need for a system that analyzes the user's psychological state in real time and provides adaptive voice guidance based on that analysis.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for acquiring image information of the environment including a target user, computation means for preprocessing the acquired image information and identifying the user, and means for analyzing voice patterns and facial expressions to estimate the user's emotional state. This makes it possible to provide adaptive voice guidance that takes the user's psychological state into consideration.

[0351] "Target" refers to a specific user group, such as wheelchair users, visually impaired individuals, and preschool children, who require safe and secure transportation.

[0352] "Image information" refers to visual data acquired using a camera or imaging device, and is information used to visualize a specific environment or the state of a user.

[0353] "Preprocessing" refers to a series of processes performed to prepare acquired raw data for analysis, and includes techniques such as image resizing and noise reduction.

[0354] "Computation means" refers to information processing devices or processes that execute programs based on acquired information to derive specific results.

[0355] "Emotional state" refers to a state that indicates psychological characteristics such as a user's level of psychological stability and anxiety.

[0356] "Voice patterns" are a concept used as a model to analyze the characteristic changes and rhythms contained in the sounds a user makes.

[0357] "Methods for analyzing facial expressions" refers to image processing techniques and algorithms used to infer a user's psychological state from their facial expressions.

[0358] "Adaptive voice guidance" refers to a system that provides dynamic voice support, where the content and tone change according to the user's psychological state.

[0359] "Notification information" refers to messages or signals sent to encourage follow-up or support based on the user's status and requests.

[0360] This invention is a system that enables users to move safely and securely in public places. The system consists of a camera, a server, and a terminal, and each of these devices works in coordination.

[0361] First, the imaging devices are installed in each facility and public space to constantly monitor the surrounding environment and acquire image information. The image information obtained by the imaging devices is transmitted to a server via the internet or a dedicated network.

[0362] The server operates using the Python programming language and performs image preprocessing using the OpenCV library. Using the preprocessed data, the server applies machine learning algorithms to identify target individuals: wheelchair users, visually impaired individuals, and preschool children. An artificial intelligence model using TensorFlow is employed for target identification.

[0363] Next, the server uses an emotion analysis engine to analyze the user's facial expressions and voice patterns. The voice data is converted to text using a cloud-based speech recognition service, and then analyzed to estimate the emotional state. This makes it possible to evaluate the user's sense of security and stress level.

[0364] Once the analysis is complete, the server generates appropriate notification messages and voice guidance. The generated notifications are immediately sent to the facility staff's terminals, enabling staff to respond quickly to users. The terminals operate as smartphone or tablet applications, and upon receiving guidance information, they provide instructions to the user via a voice output device. This voice guidance is adjusted in content and tone based on the user's emotional state.

[0365] As a concrete example, consider a visually impaired person searching for their destination in a shopping mall. This system senses their anxiety and provides psychological support by offering voice guidance such as, "Don't worry, there is a staff member nearby."

[0366] An example of a prompt to input into a generative AI model is, "Describe a method for providing emotion-responsive audio guidance to visually impaired people in public spaces." Using this prompt, the AI ​​model is expected to generate a detailed description of the system.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] A camera is installed in a public space to continuously capture images of the surrounding environment. The input for this step is real-time visual information of the environment, and the output is digital image data. This image data is acquired to monitor user movement within the facility. The camera has excellent resolution and provides clear images even under diverse lighting conditions.

[0370] Step 2:

[0371] The server receives image data transmitted from the imaging device. The input image data is high-resolution color images, which are used for image preprocessing. Preprocessing includes noise reduction, image resizing, and format conversion. The output image data is generated in a format that facilitates analysis. This process uses Python and the OpenCV library.

[0372] Step 3:

[0373] The server runs an artificial intelligence model using pre-processed data. The input for this step is formatted image data, and the output is a list of identified targets. The server utilizes TensorFlow to run a model designed to identify wheelchair users, visually impaired individuals, and preschool children. The model identifies targets with high accuracy based on patterns learned from the database.

[0374] Step 4:

[0375] The server uses target information to operate an emotion analysis engine and evaluate the target's psychological state. The input is video and audio data of the identified target, and the output is an estimated result of their emotional state. The server analyzes audio patterns and reads emotions from the user's facial expressions. It uses Google Cloud's speech recognition service to convert the audio to text and performs emotion analysis using DeepStream.

[0376] Step 5:

[0377] The server generates notification messages and voice guidance based on emotional states. Inputs are estimated emotion data and target information, while outputs are specific notification content and voice guidance content. The server uses a Python script to dynamically create content that provides reassurance to the user.

[0378] Step 6:

[0379] The terminal receives notifications from the server and displays them as push notifications on the staff terminal. The input is notification data from the server, and the output is the notification message on the terminal's display. It operates on smartphones and tablets, prompting immediate action.

[0380] Step 7:

[0381] The terminal provides voice guidance to the user through an audio output device. The input is generated voice guide content, and the output is clear and well-tuned voice guidance. Using AWS voice services, it provides reassurance to the user with a human-like voice. It genuinely supports the user with concrete examples such as "There is a staff member nearby."

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] In public spaces, it is essential that wheelchair users, visually impaired individuals, and preschool children move safely, but it is difficult to alleviate the associated psychological anxiety and stress and provide appropriate support. Conventional systems can address physical obstacle avoidance, but they do not go beyond providing support that takes into account the emotional state of the user. Therefore, there is a need to develop a system that can respond flexibly based on the emotional state of the user and support safer and more secure movement.

[0385] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0386] In this invention, the server includes a shooting means for acquiring image information to identify the user, an analysis means for analyzing the acquired image information and identifying the characteristics of the subject, and an emotion analysis means for analyzing the emotional state and providing psychological support. This makes it possible to provide not only safe movement for the user but also emotional support simultaneously.

[0387] "Photography means" refers to a device that acquires image information in order to identify the user.

[0388] An "analysis means" is a device that analyzes acquired image information and identifies the characteristics of the target.

[0389] A "means of communication" is a device that, based on the analysis results, notifies those around that an object is approaching.

[0390] "Voice output means" refers to a device that provides voice guidance to an object.

[0391] An "emotional analysis device" is a device used to analyze emotional states and provide psychological support.

[0392] A "learning algorithm" is an algorithm used to identify an object in an analytical method, and it improves the performance of the model based on data.

[0393] This invention is a system designed to enable wheelchair users, visually impaired individuals, and preschool children to move safely and securely within public facilities. The system consists of a shooting means for acquiring image information, an analysis means for identifying the characteristics of an object, an emotion analysis means for analyzing emotional states, and an audio output means for providing voice guidance.

[0394] The server first acquires user image data in real time using imaging devices (e.g., network cameras) installed within the facility. The acquired image information is then analyzed using image analysis software (e.g., OpenCV or TensorFlow) to identify the user. The analysis method identifies the user's movements and characteristics through a learning algorithm and processes the data according to the situation.

[0395] Next, emotion analysis is used to determine the user's emotional state based on their facial expressions and voice information. Emotion analysis software (e.g., EmotionEngine) is used for this purpose. Based on this information, the server understands the user's current state and generates optimal voice guidance to provide psychological support.

[0396] The generated voice guidance is delivered to the user's smartphone or smart glasses via an audio output device. The voice guidance is adjusted in tone and content to reassure the user. For example, if a visually impaired person is heading to their destination in a shopping mall, the system can sense their anxiety and provide a gentle message such as, "Don't worry, there is a staff member nearby to assist you."

[0397] Such a system ensures the safety of movement and allows users to move around the facility with psychological peace of mind. An example of a prompt message would be: "Analyze the user's video and audio data to determine their stress level and provide appropriate guidance messages."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server acquires image data in real time from cameras installed within the facility. The input is a video stream from the cameras, which is then divided into individual frames. The resulting frames are sent to the next analysis step.

[0401] Step 2:

[0402] The server analyzes the acquired image data to identify the user. The input is the image frame acquired in step 1. The server uses image analysis software to execute face recognition and motion detection algorithms and outputs characteristic data of the identified user.

[0403] Step 3:

[0404] The server performs emotion analysis. The input is the user's characteristic data output in step 2, which the server inputs into emotion analysis software (e.g., EmotionEngine). It analyzes facial expressions and voice patterns and outputs data that quantifies the user's emotional state.

[0405] Step 4:

[0406] The server generates voice guidance based on the emotion analysis results. The input is numerical data of the emotional state obtained in step 3. The server uses a generative AI model to create the optimal voice message and outputs it as audio data in digital format.

[0407] Step 5:

[0408] The device delivers the audio data received from the server to the user. The input is the audio data generated by the server in step 4, which is played back through the speaker of the smartphone or smart glasses. This allows the user to listen to the audio guidance and gain reassurance tailored to their situation.

[0409] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0410] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0411] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0415] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0416] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0417] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0418] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0419] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0420] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0421] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0422] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0423] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0424] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0425] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely in public places, using an imaging device for acquiring image data, a server for analysis, and a terminal for notifications and voice guidance.

[0426] First, the camera is installed in a specific facility or public space to acquire video data in real time. This video data is sent to a server, which is responsible for analyzing the data.

[0427] The server uses machine learning algorithms to identify and recognize the characteristics of target individuals such as wheelchair users, visually impaired individuals, and preschool children from image data. This ensures that the system is ready to respond immediately when a target is detected.

[0428] Once the analysis is complete, the server sends a notification to the device based on the target's identification information. This notification is displayed on devices carried by facility staff and related personnel, alerting them to provide immediate support.

[0429] Meanwhile, terminals equipped with voice output devices are responsible for providing voice guidance to the target. This guidance includes specific instructions on which route the target should take and how to move safely. This allows the target to move safely based on their own judgment.

[0430] As a concrete example, consider a scenario where a wheelchair user approaches the ticket gate within a train station. In this case, the terminal sends a notification to station staff stating, "A wheelchair user is approaching," and simultaneously plays an audio guidance message saying, "Please use the priority lane," thereby supporting the target's safe movement.

[0431] Thus, the present invention provides an environment in which the target can safely engage in activities in public places by using a combination of a shooting device, an analysis server, a notification terminal, and an audio output terminal.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The server receives video data from the camera in real time. This data contains image information for identifying the target.

[0435] Step 2:

[0436] The server performs preprocessing on the received video data. This preprocessing removes noise from the image and converts it into a format suitable for analysis. This conversion allows for more accurate analysis.

[0437] Step 3:

[0438] The server uses machine learning algorithms to analyze pre-processed data. Here, it identifies characteristics of wheelchair users, visually impaired individuals, and preschool children, and detects the approach of these targets.

[0439] Step 4:

[0440] Once the server successfully identifies the target, it generates a notification message based on the relevant information. This message is prepared for reception by facility personnel and staff.

[0441] Step 5:

[0442] The server sends the generated notification message to the terminal. This terminal could be a device carried by the designated recipient or a fixed notification display.

[0443] Step 6:

[0444] The terminal checks and immediately displays notification messages received from the server. Based on this notification, nearby staff and personnel can go to the target's location to provide support if necessary.

[0445] Step 7:

[0446] The terminal uses an audio output device to generate voice guidance for the target. This guidance includes safe route selection and precautions to help the target move safely.

[0447] Step 8:

[0448] The target user can select the appropriate route and begin their journey safely based on the voice guidance provided by the device.

[0449] Through these steps, the system supports the safe movement of the target.

[0450] (Example 1)

[0451] Next, we will describe Example 1. 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."

[0452] To support the safe movement of wheelchair users, visually impaired individuals, and preschool children in public spaces, it is necessary to accurately identify these targets in real time and provide voice guidance for safe routes. However, current systems lack sufficient target identification accuracy and notification speed, making it difficult to ensure adequate safety. A solution to this problem is needed.

[0453] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0454] In this invention, the server includes means for transmitting image information acquired by the imaging device in a public space to the server, means for the server to analyze the image information using a machine learning algorithm and identify the location information and characteristics of the target, and notification means for sending a notification to staff based on the information identified by the server. This enables real-time target identification, immediate response, and guidance of a safe route.

[0455] A "photography device" is a device used to acquire image information in a public space and transmit that information to a server.

[0456] A "server" is a device that receives acquired image information and performs analysis using machine learning algorithms.

[0457] A "machine learning algorithm" is a computational method used for data analysis to identify the location and features of a target from image information.

[0458] "Target" refers to wheelchair users, visually impaired individuals, and preschool children in public places, who are the target of identification and support by the system.

[0459] A "notification method" is a means of sending immediate notifications to facility staff based on information identified by the server.

[0460] A "voice output device" is a device that provides voice guidance to a target, guiding them along a safe route and conveying instructions.

[0461] A "generative AI model" is an artificial intelligence model used to continuously improve the content of voice guidance.

[0462] A "prompt message" is a sentence containing questions or instructions designed to collect user feedback and improve the accuracy of analysis.

[0463] This invention is a system that assists wheelchair users, visually impaired individuals, and preschool children in safely moving around in public spaces. The system is implemented by combining multiple devices.

[0464] First, a camera is installed within the facility to acquire image information in real time. This image information is crucial data for identifying targets (wheelchair users, visually impaired individuals, and preschool children). The image information acquired by the camera is immediately transmitted to a server.

[0465] The server uses machine learning algorithms that leverage generative AI models to analyze the received image information. This algorithm allows the server to identify the target's location and characteristics. For example, it can determine whether a person in an image is a wheelchair user based on their posture and movement.

[0466] After the server identifies the target, that information is provided to facility staff using a notification system. The notification may include a message such as, "A wheelchair user is approaching Area A."

[0467] Furthermore, terminals equipped with voice output devices provide voice guidance to the target. This guidance includes instructions for safe travel routes and specific instructions such as "Please use the priority lane." The content of this voice guidance is continuously improved by a generative AI model and adjusted using prompts based on user feedback.

[0468] As a concrete example, consider a scenario where a wheelchair user is heading towards the ticket gate in a train station. In this case, the terminal sends a notification to staff stating, "A wheelchair user is approaching," and simultaneously provides voice guidance saying, "Please use the elevator." Through this series of operations, the target user can move safely based on their own judgment.

[0469] An example of a prompt message would be a question like, "How can we make the transition smoother?" This would facilitate the development of more effective support measures and guidance methods.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The server receives image information transmitted from the imaging device. The input is real-time image data. This image data includes spatial information within the facility and the movements of users. The server temporarily stores this data and prepares it for analysis.

[0473] Step 2:

[0474] The server analyzes the received image information. This analysis uses a machine learning algorithm that leverages a generative AI model. The input is the image data received in step 1, and the output is data identifying the target's location and characteristics. The specific actions include analyzing the posture and movement patterns of people in the image and identifying targets (wheelchair users, visually impaired individuals, preschool children) based on specific features.

[0475] Step 3:

[0476] The server uses a notification system based on the analysis results to send target information to facility staff. The input is the target identification information obtained in step 2, and the output is a notification message such as "Wheelchair user approaching area A." The specific action includes immediately distributing the generated message to the staff's mobile devices.

[0477] Step 4:

[0478] The terminal provides voice guidance to the target using an audio output device. The input is the target's location and travel route information obtained in step 2, and the output is a voice message such as "Please use the priority lane." The specific operation is to transmit a pre-set message to the target using speech synthesis technology.

[0479] Step 5:

[0480] The user safely follows the designated route while listening to voice guidance. The input here is voice instructions from the terminal. The user's output is movement along the safe route, allowing the user to decide their own path and ensure safe passage.

[0481] In this way, each step works in conjunction to form a system that supports safe movement in public spaces.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, 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."

[0484] In public spaces, prompt and appropriate assistance is necessary for wheelchair users, visually impaired individuals, and preschool children to move safely. However, traditional methods may result in delayed responses or compromise safety due to a lack of manpower and information. To address these challenges, a more efficient and accurate support system is needed.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes means for acquiring image information using a camera, means for utilizing machine learning to analyze the acquired image information and identify the characteristics of a target, and means for using a communication device to transmit notifications to the target based on the results of the analysis. This enables real-time improvement of safety in public places and allows for quick and accurate assistance.

[0487] A "photography device" is a device installed in a specific environment to acquire images or videos.

[0488] "Image information" refers to visual data acquired by an imaging device, and is the subject of analysis.

[0489] "Analysis" is the process of identifying specific features or patterns based on acquired image information.

[0490] "Machine learning" is a technology that allows computers to learn patterns from data and perform tasks such as identification and prediction.

[0491] A "communication device" is a device used to transmit the results of the analysis to the subject or staff as a notification.

[0492] "Target audience" refers to individuals or groups who require specific support, such as wheelchair users, visually impaired individuals, and preschool children.

[0493] A "voice device" is a device that provides instructions or guidance to a target through voice information.

[0494] "Real-time" refers to a state in which information processing and data communication occur instantly without delay.

[0495] A "route" refers to the direction of travel or path that guides an object to move safely.

[0496] To implement this invention, it is first necessary to install a camera in a public facility or a specific environment and acquire image information in real time. The image information obtained from the camera is transmitted to a server using wireless communication technology.

[0497] The server uses software such as TensorFlow, a machine learning platform, to analyze the acquired image information. This analysis identifies the target individuals—wheelchair users, visually impaired individuals, and preschool children—and identifies their characteristics. The identified information is then communicated to staff and equipment via a communication device.

[0498] Meanwhile, the subject will be provided with guidance on the route to take and points to note using a voice device and synthesized speech technology. This voice guidance will help the subject move safely without getting lost.

[0499] In a specific example using a shopping mall, this system allows security staff to patrol using smart glasses and receive notifications such as, "There is a person in a wheelchair near the escalator on the second floor. We recommend using the elevator," enabling them to provide necessary support quickly on the spot.

[0500] When using a generative AI model, an example prompt would be: "Consider the notification and guidance that security staff using smart glasses in a shopping mall should receive. The targets are wheelchair users and visually impaired individuals."

[0501] This enables the provision of prompt and accurate support.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server activates the camera and captures real-time video footage from within the public facility. The acquired image information is then input to the server. The image information is processed only and not stored, in a manner that respects privacy.

[0505] Step 2:

[0506] The server uses a machine learning model to analyze the acquired image information. In this step, TensorFlow is used to identify subjects within the image (wheelchair users, visually impaired individuals, preschool children). As a result of the analysis, feature information and location data of the subjects are generated and output to the server.

[0507] Step 3:

[0508] Based on the analysis results, the server uses a communication device to send a notification to the terminal. This notification includes information about the presence and location of the target and is output to the staff member's mobile device. The notification includes a message prompting specific action.

[0509] Step 4:

[0510] The user (staff) checks the device and takes the necessary action according to the notification content. For example, they might go to the designated location and prepare to support the person in question.

[0511] Step 5:

[0512] The server provides voice guidance to the target via a voice device. Using synthesized speech technology, it generates and outputs pre-configured guidance messages. This informs the target of safe routes and points of interest.

[0513] Step 6:

[0514] The user (target) follows voice guidance to move safely to their destination. While staff assistance may be available in some cases, voice guidance is the primary support for movement.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely and securely in public places, using a camera that acquires and analyzes image data, a server incorporating an emotion engine that identifies emotions, and a terminal that provides notifications and voice guidance.

[0517] First, the camera is installed in the facility or public space and continuously records the surrounding environment. The video data is sent to a server where image preprocessing is performed. The server uses the preprocessed data to run machine learning algorithms to identify targets. This allows for the identification of wheelchair users, visually impaired individuals, and preschool children.

[0518] Furthermore, the emotion engine, a distinctive element of this invention, analyzes the user's facial expressions and voice patterns to estimate their emotional state. This information is used to assess the user's sense of security and stress levels, and to provide appropriate support.

[0519] Once the analysis is complete, the server generates notification messages and voice guidance based on the user's identification and emotional state. The notifications are sent to the facility's staff terminals, allowing staff to take immediate action.

[0520] In addition to receiving notifications, the device provides users with appropriate voice guidance through its voice output device. The tone and content of the voice guidance are adjusted according to the user's emotional state. For example, if the user is feeling anxious, a gentle voice guidance will be provided to reassure them. This feature allows users to feel psychologically at ease and reach their destination safely.

[0521] As a concrete example, when a visually impaired person is trying to reach their destination in a shopping mall, this system can detect the user's anxiety and provide reassuring guidance such as, "Don't worry, there is a staff member nearby." This allows the user to have a more fulfilling experience.

[0522] Thus, the present invention enhances safety and security in public places by supporting the target's movement from an emotional perspective.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The server receives video data in real time from the camera. The camera is installed within the facility or in a specific public area and records video to identify targets.

[0526] Step 2:

[0527] The server preprocesses the received video data. This process removes image noise and prepares the data for easier analysis. This process is crucial for improving the accuracy of subsequent analysis.

[0528] Step 3:

[0529] The server applies machine learning algorithms to identify target features from pre-processed data. These algorithms are trained to recognize wheelchair users, visually impaired individuals, and preschool children.

[0530] Step 4:

[0531] When a target is identified, the server simultaneously activates the emotion engine to analyze the user's facial expressions and voice patterns. By estimating the emotional state, it obtains information to design an appropriate response.

[0532] Step 5:

[0533] The server generates notification messages based on the target's identification and emotional state. These notifications are sent to facility staff in real time, allowing them to instantly understand the situation.

[0534] Step 6:

[0535] The device receives notifications from the server and displays them on the screen. The notification content includes the target's location information and warnings tailored to the target's emotional state.

[0536] Step 7:

[0537] The terminal sends instructions to the voice output device, which then plays voice guidance tailored to the user. This guidance is designed to provide a sense of security through its tone and content, which are aligned with the user's emotional state.

[0538] Step 8:

[0539] Users begin their journey based on voice guidance from their devices. This reduces user anxiety and ensures safe and smooth travel.

[0540] Through this process, the system provides technical and emotional support to help targets operate more safely in public spaces.

[0541] (Example 2)

[0542] Next, we will describe Example 2. 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."

[0543] In recent years, there has been a lack of adequate support for target groups who require safe and secure movement in public spaces, such as wheelchair users, visually impaired individuals, and preschool children. Particular attention is needed in situations requiring psychological considerations. However, current technology often only provides basic support such as user location information and simple directions. Therefore, personalized guidance that takes into account the user's psychological state is not provided, resulting in an environment where users can move with peace of mind. Consequently, there is a need for a system that analyzes the user's psychological state in real time and provides adaptive voice guidance based on that analysis.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0545] In this invention, the server includes means for acquiring image information of the environment including a target user, computation means for preprocessing the acquired image information and identifying the user, and means for analyzing voice patterns and facial expressions to estimate the user's emotional state. This makes it possible to provide adaptive voice guidance that takes the user's psychological state into consideration.

[0546] "Target" refers to a specific user group, such as wheelchair users, visually impaired individuals, and preschool children, who require safe and secure transportation.

[0547] "Image information" refers to visual data acquired using a camera or imaging device, and is information used to visualize a specific environment or the state of a user.

[0548] "Preprocessing" refers to a series of processes performed to prepare acquired raw data for analysis, and includes techniques such as image resizing and noise reduction.

[0549] "Computation means" refers to information processing devices or processes that execute programs based on acquired information to derive specific results.

[0550] "Emotional state" refers to a state that indicates psychological characteristics such as a user's level of psychological stability and anxiety.

[0551] "Voice patterns" are a concept used as a model to analyze the characteristic changes and rhythms contained in the sounds a user makes.

[0552] "Methods for analyzing facial expressions" refers to image processing techniques and algorithms used to infer a user's psychological state from their facial expressions.

[0553] "Adaptive voice guidance" refers to a system that provides dynamic voice support, where the content and tone change according to the user's psychological state.

[0554] "Notification information" refers to messages or signals sent to encourage follow-up or support based on the user's status and requests.

[0555] This invention is a system that enables users to move safely and securely in public places. The system consists of a camera, a server, and a terminal, and each of these devices works in coordination.

[0556] First, the imaging devices are installed in each facility and public space to constantly monitor the surrounding environment and acquire image information. The image information obtained by the imaging devices is transmitted to a server via the internet or a dedicated network.

[0557] The server operates using the Python programming language and performs image preprocessing using the OpenCV library. Using the preprocessed data, the server applies machine learning algorithms to identify target individuals: wheelchair users, visually impaired individuals, and preschool children. An artificial intelligence model using TensorFlow is employed for target identification.

[0558] Next, the server uses an emotion analysis engine to analyze the user's facial expressions and voice patterns. The voice data is converted to text using a cloud-based speech recognition service, and then analyzed to estimate the emotional state. This makes it possible to evaluate the user's sense of security and stress level.

[0559] Once the analysis is complete, the server generates appropriate notification messages and voice guidance. The generated notifications are immediately sent to the facility staff's terminals, enabling staff to respond quickly to users. The terminals operate as smartphone or tablet applications, and upon receiving guidance information, they provide instructions to the user via a voice output device. This voice guidance is adjusted in content and tone based on the user's emotional state.

[0560] As a concrete example, consider a visually impaired person searching for their destination in a shopping mall. This system senses their anxiety and provides psychological support by offering voice guidance such as, "Don't worry, there is a staff member nearby."

[0561] An example of a prompt to input into a generative AI model is, "Describe a method for providing emotion-responsive audio guidance to visually impaired people in public spaces." Using this prompt, the AI ​​model is expected to generate a detailed description of the system.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] A camera is installed in a public space to continuously capture images of the surrounding environment. The input for this step is real-time visual information of the environment, and the output is digital image data. This image data is acquired to monitor user movement within the facility. The camera has excellent resolution and provides clear images even under diverse lighting conditions.

[0565] Step 2:

[0566] The server receives image data transmitted from the imaging device. The input image data is high-resolution color images, which are used for image preprocessing. Preprocessing includes noise reduction, image resizing, and format conversion. The output image data is generated in a format that facilitates analysis. This process uses Python and the OpenCV library.

[0567] Step 3:

[0568] The server runs an artificial intelligence model using pre-processed data. The input for this step is formatted image data, and the output is a list of identified targets. The server utilizes TensorFlow to run a model designed to identify wheelchair users, visually impaired individuals, and preschool children. The model identifies targets with high accuracy based on patterns learned from the database.

[0569] Step 4:

[0570] The server uses target information to operate an emotion analysis engine and evaluate the target's psychological state. The input is video and audio data of the identified target, and the output is an estimated result of their emotional state. The server analyzes audio patterns and reads emotions from the user's facial expressions. It uses Google Cloud's speech recognition service to convert the audio to text and performs emotion analysis using DeepStream.

[0571] Step 5:

[0572] The server generates notification messages and voice guidance based on emotional states. Inputs are estimated emotion data and target information, while outputs are specific notification content and voice guidance content. The server uses a Python script to dynamically create content that provides reassurance to the user.

[0573] Step 6:

[0574] The terminal receives notifications from the server and displays them as push notifications on the staff terminal. The input is notification data from the server, and the output is the notification message on the terminal's display. It operates on smartphones and tablets, prompting immediate action.

[0575] Step 7:

[0576] The terminal provides voice guidance to the user through an audio output device. The input is generated voice guide content, and the output is clear and well-tuned voice guidance. Using AWS voice services, it provides reassurance to the user with a human-like voice. It genuinely supports the user with concrete examples such as "There is a staff member nearby."

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, 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."

[0579] In public spaces, it is essential that wheelchair users, visually impaired individuals, and preschool children move safely, but it is difficult to alleviate the associated psychological anxiety and stress and provide appropriate support. Conventional systems can address physical obstacle avoidance, but they do not go beyond providing support that takes into account the emotional state of the user. Therefore, there is a need to develop a system that can respond flexibly based on the emotional state of the user and support safer and more secure movement.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes a shooting means for acquiring image information to identify the user, an analysis means for analyzing the acquired image information and identifying the characteristics of the subject, and an emotion analysis means for analyzing the emotional state and providing psychological support. This makes it possible to provide not only safe movement for the user but also emotional support simultaneously.

[0582] "Photography means" refers to a device that acquires image information in order to identify the user.

[0583] An "analysis means" is a device that analyzes acquired image information and identifies the characteristics of the target.

[0584] A "means of communication" is a device that, based on the analysis results, notifies those around that an object is approaching.

[0585] "Voice output means" refers to a device that provides voice guidance to an object.

[0586] An "emotional analysis device" is a device used to analyze emotional states and provide psychological support.

[0587] A "learning algorithm" is an algorithm used to identify an object in an analytical method, and it improves the performance of the model based on data.

[0588] This invention is a system designed to enable wheelchair users, visually impaired individuals, and preschool children to move safely and securely within public facilities. The system consists of a shooting means for acquiring image information, an analysis means for identifying the characteristics of an object, an emotion analysis means for analyzing emotional states, and an audio output means for providing voice guidance.

[0589] The server first acquires user image data in real time using imaging devices (e.g., network cameras) installed within the facility. The acquired image information is then analyzed using image analysis software (e.g., OpenCV or TensorFlow) to identify the user. The analysis method identifies the user's movements and characteristics through a learning algorithm and processes the data according to the situation.

[0590] Next, emotion analysis is used to determine the user's emotional state based on their facial expressions and voice information. Emotion analysis software (e.g., EmotionEngine) is used for this purpose. Based on this information, the server understands the user's current state and generates optimal voice guidance to provide psychological support.

[0591] The generated voice guidance is delivered to the user's smartphone or smart glasses via an audio output device. The voice guidance is adjusted in tone and content to reassure the user. For example, if a visually impaired person is heading to their destination in a shopping mall, the system can sense their anxiety and provide a gentle message such as, "Don't worry, there is a staff member nearby to assist you."

[0592] Such a system ensures the safety of movement and allows users to move around the facility with psychological peace of mind. An example of a prompt message would be: "Analyze the user's video and audio data to determine their stress level and provide appropriate guidance messages."

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The server acquires image data in real time from cameras installed within the facility. The input is a video stream from the cameras, which is then divided into individual frames. The resulting frames are sent to the next analysis step.

[0596] Step 2:

[0597] The server analyzes the acquired image data to identify the user. The input is the image frame acquired in step 1. The server uses image analysis software to execute face recognition and motion detection algorithms and outputs characteristic data of the identified user.

[0598] Step 3:

[0599] The server performs emotion analysis. The input is the user's characteristic data output in step 2, which the server inputs into emotion analysis software (e.g., EmotionEngine). It analyzes facial expressions and voice patterns and outputs data that quantifies the user's emotional state.

[0600] Step 4:

[0601] The server generates voice guidance based on the emotion analysis results. The input is numerical data of the emotional state obtained in step 3. The server uses a generative AI model to create the optimal voice message and outputs it as audio data in digital format.

[0602] Step 5:

[0603] The device delivers the audio data received from the server to the user. The input is the audio data generated by the server in step 4, which is played back through the speaker of the smartphone or smart glasses. This allows the user to listen to the audio guidance and gain reassurance tailored to their situation.

[0604] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0605] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0606] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0610] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0611] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0612] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0613] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0614] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0615] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0616] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0617] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0618] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0619] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0620] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0621] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely in public places, using an imaging device for acquiring image data, a server for analysis, and a terminal for notifications and voice guidance.

[0622] First, the camera is installed in a specific facility or public space to acquire video data in real time. This video data is sent to a server, which is responsible for analyzing the data.

[0623] The server uses machine learning algorithms to identify and recognize the characteristics of target individuals such as wheelchair users, visually impaired individuals, and preschool children from image data. This ensures that the system is ready to respond immediately when a target is detected.

[0624] Once the analysis is complete, the server sends a notification to the device based on the target's identification information. This notification is displayed on devices carried by facility staff and related personnel, alerting them to provide immediate support.

[0625] Meanwhile, terminals equipped with voice output devices are responsible for providing voice guidance to the target. This guidance includes specific instructions on which route the target should take and how to move safely. This allows the target to move safely based on their own judgment.

[0626] As a concrete example, consider a scenario where a wheelchair user approaches the ticket gate within a train station. In this case, the terminal sends a notification to station staff stating, "A wheelchair user is approaching," and simultaneously plays an audio guidance message saying, "Please use the priority lane," thereby supporting the target's safe movement.

[0627] Thus, the present invention provides an environment in which the target can safely engage in activities in public places by using a combination of a shooting device, an analysis server, a notification terminal, and an audio output terminal.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server receives video data from the camera in real time. This data contains image information for identifying the target.

[0631] Step 2:

[0632] The server performs preprocessing on the received video data. This preprocessing removes noise from the image and converts it into a format suitable for analysis. This conversion allows for more accurate analysis.

[0633] Step 3:

[0634] The server uses machine learning algorithms to analyze pre-processed data. Here, it identifies characteristics of wheelchair users, visually impaired individuals, and preschool children, and detects the approach of these targets.

[0635] Step 4:

[0636] Once the server successfully identifies the target, it generates a notification message based on the relevant information. This message is prepared for reception by facility personnel and staff.

[0637] Step 5:

[0638] The server sends the generated notification message to the terminal. This terminal could be a device carried by the designated recipient or a fixed notification display.

[0639] Step 6:

[0640] The terminal checks and immediately displays notification messages received from the server. Based on this notification, nearby staff and personnel can go to the target's location to provide support if necessary.

[0641] Step 7:

[0642] The terminal uses an audio output device to generate voice guidance for the target. This guidance includes safe route selection and precautions to help the target move safely.

[0643] Step 8:

[0644] The target user can select the appropriate route and begin their journey safely based on the voice guidance provided by the device.

[0645] Through these steps, the system supports the safe movement of the target.

[0646] (Example 1)

[0647] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] To support the safe movement of wheelchair users, visually impaired individuals, and preschool children in public spaces, it is necessary to accurately identify these targets in real time and provide voice guidance for safe routes. However, current systems lack sufficient target identification accuracy and notification speed, making it difficult to ensure adequate safety. A solution to this problem is needed.

[0649] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0650] In this invention, the server includes means for transmitting image information acquired by the imaging device in a public space to the server, means for the server to analyze the image information using a machine learning algorithm and identify the location information and characteristics of the target, and notification means for sending a notification to staff based on the information identified by the server. This enables real-time target identification, immediate response, and guidance of a safe route.

[0651] A "photography device" is a device used to acquire image information in a public space and transmit that information to a server.

[0652] A "server" is a device that receives acquired image information and performs analysis using machine learning algorithms.

[0653] A "machine learning algorithm" is a computational method used for data analysis to identify the location and features of a target from image information.

[0654] "Target" refers to wheelchair users, visually impaired individuals, and preschool children in public places, who are the target of identification and support by the system.

[0655] A "notification method" is a means of sending immediate notifications to facility staff based on information identified by the server.

[0656] A "voice output device" is a device that provides voice guidance to a target, guiding them along a safe route and conveying instructions.

[0657] A "generative AI model" is an artificial intelligence model used to continuously improve the content of voice guidance.

[0658] A "prompt message" is a sentence containing questions or instructions designed to collect user feedback and improve the accuracy of analysis.

[0659] This invention is a system that assists wheelchair users, visually impaired individuals, and preschool children in safely moving around in public spaces. The system is implemented by combining multiple devices.

[0660] First, a camera is installed within the facility to acquire image information in real time. This image information is crucial data for identifying targets (wheelchair users, visually impaired individuals, and preschool children). The image information acquired by the camera is immediately transmitted to a server.

[0661] The server uses machine learning algorithms that leverage generative AI models to analyze the received image information. This algorithm allows the server to identify the target's location and characteristics. For example, it can determine whether a person in an image is a wheelchair user based on their posture and movement.

[0662] After the server identifies the target, that information is provided to facility staff using a notification system. The notification may include a message such as, "A wheelchair user is approaching Area A."

[0663] Furthermore, terminals equipped with voice output devices provide voice guidance to the target. This guidance includes instructions for safe travel routes and specific instructions such as "Please use the priority lane." The content of this voice guidance is continuously improved by a generative AI model and adjusted using prompts based on user feedback.

[0664] As a concrete example, consider a scenario where a wheelchair user is heading towards the ticket gate in a train station. In this case, the terminal sends a notification to staff stating, "A wheelchair user is approaching," and simultaneously provides voice guidance saying, "Please use the elevator." Through this series of operations, the target user can move safely based on their own judgment.

[0665] An example of a prompt message would be a question like, "How can we make the transition smoother?" This would facilitate the development of more effective support measures and guidance methods.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The server receives image information transmitted from the imaging device. The input is real-time image data. This image data includes spatial information within the facility and the movements of users. The server temporarily stores this data and prepares it for analysis.

[0669] Step 2:

[0670] The server analyzes the received image information. This analysis uses a machine learning algorithm that leverages a generative AI model. The input is the image data received in step 1, and the output is data identifying the target's location and characteristics. The specific actions include analyzing the posture and movement patterns of people in the image and identifying targets (wheelchair users, visually impaired individuals, preschool children) based on specific features.

[0671] Step 3:

[0672] The server uses a notification system based on the analysis results to send target information to facility staff. The input is the target identification information obtained in step 2, and the output is a notification message such as "Wheelchair user approaching area A." The specific action includes immediately distributing the generated message to the staff's mobile devices.

[0673] Step 4:

[0674] The terminal provides voice guidance to the target using an audio output device. The input is the target's location and travel route information obtained in step 2, and the output is a voice message such as "Please use the priority lane." The specific operation is to transmit a pre-set message to the target using speech synthesis technology.

[0675] Step 5:

[0676] The user safely follows the designated route while listening to voice guidance. The input here is voice instructions from the terminal. The user's output is movement along the safe route, allowing the user to decide their own path and ensure safe passage.

[0677] In this way, each step works in conjunction to form a system that supports safe movement in public spaces.

[0678] (Application Example 1)

[0679] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] In public spaces, prompt and appropriate assistance is necessary for wheelchair users, visually impaired individuals, and preschool children to move safely. However, traditional methods may result in delayed responses or compromise safety due to a lack of manpower and information. To address these challenges, a more efficient and accurate support system is needed.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0682] In this invention, the server includes means for acquiring image information using a camera, means for utilizing machine learning to analyze the acquired image information and identify the characteristics of a target, and means for using a communication device to transmit notifications to the target based on the results of the analysis. This enables real-time improvement of safety in public places and allows for quick and accurate assistance.

[0683] A "photography device" is a device installed in a specific environment to acquire images or videos.

[0684] "Image information" refers to visual data acquired by an imaging device, and is the subject of analysis.

[0685] "Analysis" is the process of identifying specific features or patterns based on acquired image information.

[0686] "Machine learning" is a technology that allows computers to learn patterns from data and perform tasks such as identification and prediction.

[0687] A "communication device" is a device used to transmit the results of the analysis to the subject or staff as a notification.

[0688] "Target audience" refers to individuals or groups who require specific support, such as wheelchair users, visually impaired individuals, and preschool children.

[0689] A "voice device" is a device that provides instructions or guidance to a target through voice information.

[0690] "Real-time" refers to a state in which information processing and data communication occur instantly without delay.

[0691] A "route" refers to the direction of travel or path that guides an object to move safely.

[0692] To implement this invention, it is first necessary to install a camera in a public facility or a specific environment and acquire image information in real time. The image information obtained from the camera is transmitted to a server using wireless communication technology.

[0693] The server uses software such as TensorFlow, a machine learning platform, to analyze the acquired image information. This analysis identifies the target individuals—wheelchair users, visually impaired individuals, and preschool children—and identifies their characteristics. The identified information is then communicated to staff and equipment via a communication device.

[0694] Meanwhile, the subject will be provided with guidance on the route to take and points to note using a voice device and synthesized speech technology. This voice guidance will help the subject move safely without getting lost.

[0695] In a specific example using a shopping mall, this system allows security staff to patrol using smart glasses and receive notifications such as, "There is a person in a wheelchair near the escalator on the second floor. We recommend using the elevator," enabling them to provide necessary support quickly on the spot.

[0696] When using a generative AI model, an example prompt would be: "Consider the notification and guidance that security staff using smart glasses in a shopping mall should receive. The targets are wheelchair users and visually impaired individuals."

[0697] This enables the provision of prompt and accurate support.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] The server activates the camera and captures real-time video footage from within the public facility. The acquired image information is then input to the server. The image information is processed only and not stored, in a manner that respects privacy.

[0701] Step 2:

[0702] The server uses a machine learning model to analyze the acquired image information. In this step, TensorFlow is used to identify subjects within the image (wheelchair users, visually impaired individuals, preschool children). As a result of the analysis, feature information and location data of the subjects are generated and output to the server.

[0703] Step 3:

[0704] Based on the analysis results, the server uses a communication device to send a notification to the terminal. This notification includes information about the presence and location of the target and is output to the staff member's mobile device. The notification includes a message prompting specific action.

[0705] Step 4:

[0706] The user (staff) checks the device and takes the necessary action according to the notification content. For example, they might go to the designated location and prepare to support the person in question.

[0707] Step 5:

[0708] The server provides voice guidance to the target via a voice device. Using synthesized speech technology, it generates and outputs pre-configured guidance messages. This informs the target of safe routes and points of interest.

[0709] Step 6:

[0710] The user (target) follows voice guidance to move safely to their destination. While staff assistance may be available in some cases, voice guidance is the primary support for movement.

[0711] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0712] This invention provides a system that enables wheelchair users, visually impaired individuals, and preschool children to move safely and securely in public places, using a camera that acquires and analyzes image data, a server incorporating an emotion engine that identifies emotions, and a terminal that provides notifications and voice guidance.

[0713] First, the camera is installed in the facility or public space and continuously records the surrounding environment. The video data is sent to a server where image preprocessing is performed. The server uses the preprocessed data to run machine learning algorithms to identify targets. This allows for the identification of wheelchair users, visually impaired individuals, and preschool children.

[0714] Furthermore, the emotion engine, a distinctive element of this invention, analyzes the user's facial expressions and voice patterns to estimate their emotional state. This information is used to assess the user's sense of security and stress levels, and to provide appropriate support.

[0715] Once the analysis is complete, the server generates notification messages and voice guidance based on the user's identification and emotional state. The notifications are sent to the facility's staff terminals, allowing staff to take immediate action.

[0716] In addition to receiving notifications, the device provides users with appropriate voice guidance through its voice output device. The tone and content of the voice guidance are adjusted according to the user's emotional state. For example, if the user is feeling anxious, a gentle voice guidance will be provided to reassure them. This feature allows users to feel psychologically at ease and reach their destination safely.

[0717] As a concrete example, when a visually impaired person is trying to reach their destination in a shopping mall, this system can detect the user's anxiety and provide reassuring guidance such as, "Don't worry, there is a staff member nearby." This allows the user to have a more fulfilling experience.

[0718] Thus, the present invention enhances safety and security in public places by supporting the target's movement from an emotional perspective.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The server receives video data in real time from the camera. The camera is installed within the facility or in a specific public area and records video to identify targets.

[0722] Step 2:

[0723] The server preprocesses the received video data. This process removes image noise and prepares the data for easier analysis. This process is crucial for improving the accuracy of subsequent analysis.

[0724] Step 3:

[0725] The server applies machine learning algorithms to identify target features from pre-processed data. These algorithms are trained to recognize wheelchair users, visually impaired individuals, and preschool children.

[0726] Step 4:

[0727] When a target is identified, the server simultaneously activates the emotion engine to analyze the user's facial expressions and voice patterns. By estimating the emotional state, it obtains information to design an appropriate response.

[0728] Step 5:

[0729] The server generates notification messages based on the target's identification and emotional state. These notifications are sent to facility staff in real time, allowing them to instantly understand the situation.

[0730] Step 6:

[0731] The device receives notifications from the server and displays them on the screen. The notification content includes the target's location information and warnings tailored to the target's emotional state.

[0732] Step 7:

[0733] The terminal sends instructions to the voice output device, which then plays voice guidance tailored to the user. This guidance is designed to provide a sense of security through its tone and content, which are aligned with the user's emotional state.

[0734] Step 8:

[0735] Users begin their journey based on voice guidance from their devices. This reduces user anxiety and ensures safe and smooth travel.

[0736] Through this process, the system provides technical and emotional support to help targets operate more safely in public spaces.

[0737] (Example 2)

[0738] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] In recent years, there has been a lack of adequate support for target groups who require safe and secure movement in public spaces, such as wheelchair users, visually impaired individuals, and preschool children. Particular attention is needed in situations requiring psychological considerations. However, current technology often only provides basic support such as user location information and simple directions. Therefore, personalized guidance that takes into account the user's psychological state is not provided, resulting in an environment where users can move with peace of mind. Consequently, there is a need for a system that analyzes the user's psychological state in real time and provides adaptive voice guidance based on that analysis.

[0740] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0741] In this invention, the server includes means for acquiring image information of the environment including a target user, computation means for preprocessing the acquired image information and identifying the user, and means for analyzing voice patterns and facial expressions to estimate the user's emotional state. This makes it possible to provide adaptive voice guidance that takes the user's psychological state into consideration.

[0742] "Target" refers to a specific user group, such as wheelchair users, visually impaired individuals, and preschool children, who require safe and secure transportation.

[0743] "Image information" refers to visual data acquired using a camera or imaging device, and is information used to visualize a specific environment or the state of a user.

[0744] "Preprocessing" refers to a series of processes performed to prepare acquired raw data for analysis, and includes techniques such as image resizing and noise reduction.

[0745] "Computation means" refers to information processing devices or processes that execute programs based on acquired information to derive specific results.

[0746] "Emotional state" refers to a state that indicates psychological characteristics such as a user's level of psychological stability and anxiety.

[0747] "Voice patterns" are a concept used as a model to analyze the characteristic changes and rhythms contained in the sounds a user makes.

[0748] "Methods for analyzing facial expressions" refers to image processing techniques and algorithms used to infer a user's psychological state from their facial expressions.

[0749] "Adaptive voice guidance" refers to a system that provides dynamic voice support, where the content and tone change according to the user's psychological state.

[0750] "Notification information" refers to messages or signals sent to encourage follow-up or support based on the user's status and requests.

[0751] This invention is a system that enables users to move safely and securely in public places. The system consists of a camera, a server, and a terminal, and each of these devices works in coordination.

[0752] First, the imaging devices are installed in each facility and public space to constantly monitor the surrounding environment and acquire image information. The image information obtained by the imaging devices is transmitted to a server via the internet or a dedicated network.

[0753] The server operates using the Python programming language and performs image preprocessing using the OpenCV library. Using the preprocessed data, the server applies machine learning algorithms to identify target individuals: wheelchair users, visually impaired individuals, and preschool children. An artificial intelligence model using TensorFlow is employed for target identification.

[0754] Next, the server uses an emotion analysis engine to analyze the user's facial expressions and voice patterns. The voice data is converted to text using a cloud-based speech recognition service, and then analyzed to estimate the emotional state. This makes it possible to evaluate the user's sense of security and stress level.

[0755] Once the analysis is complete, the server generates appropriate notification messages and voice guidance. The generated notifications are immediately sent to the facility staff's terminals, enabling staff to respond quickly to users. The terminals operate as smartphone or tablet applications, and upon receiving guidance information, they provide instructions to the user via a voice output device. This voice guidance is adjusted in content and tone based on the user's emotional state.

[0756] As a concrete example, consider a visually impaired person searching for their destination in a shopping mall. This system senses their anxiety and provides psychological support by offering voice guidance such as, "Don't worry, there is a staff member nearby."

[0757] An example of a prompt to input into a generative AI model is, "Describe a method for providing emotion-responsive audio guidance to visually impaired people in public spaces." Using this prompt, the AI ​​model is expected to generate a detailed description of the system.

[0758] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0759] Step 1:

[0760] A camera is installed in a public space to continuously capture images of the surrounding environment. The input for this step is real-time visual information of the environment, and the output is digital image data. This image data is acquired to monitor user movement within the facility. The camera has excellent resolution and provides clear images even under diverse lighting conditions.

[0761] Step 2:

[0762] The server receives image data transmitted from the imaging device. The input image data is high-resolution color images, which are used for image preprocessing. Preprocessing includes noise reduction, image resizing, and format conversion. The output image data is generated in a format that facilitates analysis. This process uses Python and the OpenCV library.

[0763] Step 3:

[0764] The server runs an artificial intelligence model using pre-processed data. The input for this step is formatted image data, and the output is a list of identified targets. The server utilizes TensorFlow to run a model designed to identify wheelchair users, visually impaired individuals, and preschool children. The model identifies targets with high accuracy based on patterns learned from the database.

[0765] Step 4:

[0766] The server uses target information to operate an emotion analysis engine and evaluate the target's psychological state. The input is video and audio data of the identified target, and the output is an estimated result of their emotional state. The server analyzes audio patterns and reads emotions from the user's facial expressions. It uses Google Cloud's speech recognition service to convert the audio to text and performs emotion analysis using DeepStream.

[0767] Step 5:

[0768] The server generates notification messages and voice guidance based on emotional states. Inputs are estimated emotion data and target information, while outputs are specific notification content and voice guidance content. The server uses a Python script to dynamically create content that provides reassurance to the user.

[0769] Step 6:

[0770] The terminal receives notifications from the server and displays them as push notifications on the staff terminal. The input is notification data from the server, and the output is the notification message on the terminal's display. It operates on smartphones and tablets, prompting immediate action.

[0771] Step 7:

[0772] The terminal provides voice guidance to the user through an audio output device. The input is generated voice guide content, and the output is clear and well-tuned voice guidance. Using AWS voice services, it provides reassurance to the user with a human-like voice. It genuinely supports the user with concrete examples such as "There is a staff member nearby."

[0773] (Application Example 2)

[0774] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] In public spaces, it is essential that wheelchair users, visually impaired individuals, and preschool children move safely, but it is difficult to alleviate the associated psychological anxiety and stress and provide appropriate support. Conventional systems can address physical obstacle avoidance, but they do not go beyond providing support that takes into account the emotional state of the user. Therefore, there is a need to develop a system that can respond flexibly based on the emotional state of the user and support safer and more secure movement.

[0776] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0777] In this invention, the server includes a shooting means for acquiring image information to identify the user, an analysis means for analyzing the acquired image information and identifying the characteristics of the subject, and an emotion analysis means for analyzing the emotional state and providing psychological support. This makes it possible to provide not only safe movement for the user but also emotional support simultaneously.

[0778] "Photography means" refers to a device that acquires image information in order to identify the user.

[0779] An "analysis means" is a device that analyzes acquired image information and identifies the characteristics of the target.

[0780] A "means of communication" is a device that, based on the analysis results, notifies those around that an object is approaching.

[0781] "Voice output means" refers to a device that provides voice guidance to an object.

[0782] An "emotional analysis device" is a device used to analyze emotional states and provide psychological support.

[0783] A "learning algorithm" is an algorithm used to identify an object in an analytical method, and it improves the performance of the model based on data.

[0784] This invention is a system designed to enable wheelchair users, visually impaired individuals, and preschool children to move safely and securely within public facilities. The system consists of a shooting means for acquiring image information, an analysis means for identifying the characteristics of an object, an emotion analysis means for analyzing emotional states, and an audio output means for providing voice guidance.

[0785] The server first acquires user image data in real time using imaging devices (e.g., network cameras) installed within the facility. The acquired image information is then analyzed using image analysis software (e.g., OpenCV or TensorFlow) to identify the user. The analysis method identifies the user's movements and characteristics through a learning algorithm and processes the data according to the situation.

[0786] Next, emotion analysis is used to determine the user's emotional state based on their facial expressions and voice information. Emotion analysis software (e.g., EmotionEngine) is used for this purpose. Based on this information, the server understands the user's current state and generates optimal voice guidance to provide psychological support.

[0787] The generated voice guidance is delivered to the user's smartphone or smart glasses via an audio output device. The voice guidance is adjusted in tone and content to reassure the user. For example, if a visually impaired person is heading to their destination in a shopping mall, the system can sense their anxiety and provide a gentle message such as, "Don't worry, there is a staff member nearby to assist you."

[0788] Such a system ensures the safety of movement and allows users to move around the facility with psychological peace of mind. An example of a prompt message would be: "Analyze the user's video and audio data to determine their stress level and provide appropriate guidance messages."

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The server acquires image data in real time from cameras installed within the facility. The input is a video stream from the cameras, which is then divided into individual frames. The resulting frames are sent to the next analysis step.

[0792] Step 2:

[0793] The server analyzes the acquired image data to identify the user. The input is the image frame acquired in step 1. The server uses image analysis software to execute face recognition and motion detection algorithms and outputs characteristic data of the identified user.

[0794] Step 3:

[0795] The server performs emotion analysis. The input is the user's characteristic data output in step 2, which the server inputs into emotion analysis software (e.g., EmotionEngine). It analyzes facial expressions and voice patterns and outputs data that quantifies the user's emotional state.

[0796] Step 4:

[0797] The server generates voice guidance based on the emotion analysis results. The input is numerical data of the emotional state obtained in step 3. The server uses a generative AI model to create the optimal voice message and outputs it as audio data in digital format.

[0798] Step 5:

[0799] The device delivers the audio data received from the server to the user. The input is the audio data generated by the server in step 4, which is played back through the speaker of the smartphone or smart glasses. This allows the user to listen to the audio guidance and gain reassurance tailored to their situation.

[0800] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0801] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0802] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0803] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0804] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0805] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0806] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0807] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0808] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0809] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0810] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0811] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0812] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0813] 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.

[0814] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0815] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0816] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0817] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0818] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0819] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0820] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0821] The following is further disclosed regarding the embodiments described above.

[0822] (Claim 1)

[0823] A camera that acquires image data to identify wheelchair users, visually impaired persons, and preschool children,

[0824] An analysis device that analyzes acquired image data and identifies the characteristics of the target,

[0825] Based on the analysis results, a notification device is provided to alert those in the surrounding area that a target is approaching.

[0826] A voice output device for providing voice guidance to a target,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for outputting information via voice to safely guide a target along its movement path.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising a process in which the analysis device identifies a target using a machine learning model.

[0832] "Example 1"

[0833] (Claim 1)

[0834] A means for a camera to transmit image information acquired in a public space to a server,

[0835] The server uses a machine learning algorithm to analyze image information and identify the target's location and features,

[0836] A notification system that sends notifications to staff based on information identified by the server,

[0837] A means by which an audio output device provides voice guidance to a target on a safe route,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for the voice output device to continuously improve the voice guidance content using a generated AI model.

[0841] (Claim 3)

[0842] The system according to claim 1, which includes a process in which the server collects user feedback using prompt messages and improves the accuracy of the analysis.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means for acquiring image information using a photographic device,

[0846] A method that utilizes machine learning to analyze acquired image information and identify the features of the target,

[0847] Means for using a communication device to transmit a notification to the target based on the results of the analysis,

[0848] A means of using an audio device to guide a target along a route through voice information,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for providing voice information for safely guiding the movement path of an object.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising means for providing notifications and guidance based on real-time data analysis using a mobile device.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A means of acquiring image information of the environment including the target user,

[0857] A computational means for preprocessing acquired image information and identifying the user,

[0858] A means for analyzing voice patterns and facial expressions to estimate the user's emotional state,

[0859] A means of providing voice guidance adapted to the user based on the estimated emotional state,

[0860] A means of sending notification information to facility workers,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for generating voice guidance corresponding to the user's psychological state.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the calculation means includes a process of identifying a user using an artificial intelligence technique.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A means of capturing image information to identify the user,

[0869] An analysis means for analyzing acquired image information and identifying the characteristics of the target,

[0870] Based on the analysis results, a means of communication is provided to inform the surroundings that the object is approaching,

[0871] A voice output means for providing voice guidance to the target,

[0872] An emotional analysis tool for analyzing emotional states and providing psychological support,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for outputting information via voice to safely guide the target along its movement path.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the analysis means includes a process of identifying an object using a learning algorithm. [Explanation of Symbols]

[0878] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A camera that acquires image data to identify wheelchair users, visually impaired persons, and preschool children, An analysis device that analyzes acquired image data and identifies the characteristics of the target, Based on the analysis results, a notification device is provided to alert those in the surrounding area that a target is approaching. A voice output device for providing voice guidance to a target, A system that includes this.

2. The system according to claim 1, further comprising means for outputting information via voice to safely guide a target along its movement path.

3. The system according to claim 1, comprising a process in which the analysis device identifies a target using a machine learning model.

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