Method for generating emergency situation notification information

Abstract

According to an embodiment of the present disclosure, disclosed is a method for generating emergency situation notification information, the method being performed by a computing device. The method may include the steps of: obtaining help request information from a user equipment; transmitting emergency situation notification information to another user equipment located within a preset area based on the user equipment, in response to the help request information; and providing user information associated with the user equipment to the other user equipment when a response signal is received from the other user equipment that has received the emergency situation notification information.

Description

How to create emergency notification information

[0001] The present invention relates to a method for generating emergency notification information, and more particularly, to a technology that enables a user to request assistance with minimal manipulation in an emergency situation.

[0002] In emergency situations, rapid response is crucial. In these situations, users need tools to assess their own condition and proactively seek help when necessary. For example, in cases such as heart attacks, severe asthma attacks, or severe allergic reactions, rapid medical intervention is essential. In these situations, being able to quickly call for help from those nearby can shorten the time it takes to receive treatment. This is especially important in areas where medical resources are limited or ambulances may be delayed.

[0003] Korean Patent No. 10-0953629 (April 12, 2010) discloses a method for requesting help in an emergency using a GPS-equipped phone.

[0004] The present disclosure aims to provide a method for a user to request help in an emergency situation with minimal operations by providing an intuitive and simple interface so that the user can quickly and efficiently receive help with minimal effort in an emergency situation.

[0005] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0006] According to one embodiment of the present disclosure for achieving the above-described task, a method for generating emergency notification information performed by a computing device is disclosed. The method may include the steps of: obtaining help request information from a user equipment; transmitting emergency notification information to another user equipment located within a preset area relative to the user equipment in response to the help request information; and, when a response signal is received from the other user equipment that has received the emergency notification information, providing user information associated with the user equipment to the other user equipment.

[0007] In one embodiment, the assistance request information may include any one of a user input type; a text type; or a voice type.

[0008] In one embodiment, the step of obtaining help request information from the user equipment may include the step of displaying a GUI (Graphical User Interface) corresponding to the help request on the user equipment; and the step of obtaining the help request information based on an input of a button included in the GUI.

[0009] In one embodiment, the user input type may be obtained based on a user input to a GUI (Graphical User Interface) displayed on the user equipment.

[0010] In one embodiment, the step of obtaining help request information from the user equipment may include either a step of extracting a target keyword from the help request information using a Real Time STT (RT-STT) model; or a step of extracting a voice pattern from the voice type help request information using an artificial intelligence model.

[0011] In one embodiment, the method may further include, after assistance request information is obtained from the user equipment: obtaining location information from the user equipment.

[0012] In one embodiment, in response to the assistance request information, the step of transmitting emergency notification information to other user equipment located within a preset area based on the user equipment may include the steps of: identifying other candidate user equipment located within the preset area based on the acquired location information; determining priorities of the identified other candidate user equipments using an artificial intelligence model; and transmitting the emergency notification information to one of the identified other candidate user equipments based on the determined priorities.

[0013] In one embodiment, the step of determining priorities of the identified other candidate user equipments using the artificial intelligence model may further include one of the steps of: analyzing the expected arrival times of the identified other candidate user equipments using the artificial intelligence model; predicting the response potential of the identified other candidate user equipments using the artificial intelligence model; or determining emergency response capability information of the identified other candidate user equipments.

[0014] In one embodiment, the step of analyzing the expected arrival times of the identified other candidate user devices using the artificial intelligence model may include the steps of: obtaining location information of the identified other candidate user devices; estimating a moving speed based on the location information of the identified other candidate user devices; and analyzing the expected arrival times of the identified other candidate user devices based on road condition information and moving speed using the artificial intelligence model.

[0015] In one embodiment, the step of predicting the likelihood of response of the identified other candidate user devices by utilizing the artificial intelligence model may include the step of predicting the likelihood of response of the other candidate user devices by utilizing the artificial intelligence model based on any one of a previous response history of the other candidate user devices, a time or place at which the assistance request information was obtained.

[0016] In one embodiment, the step of determining emergency response capability information of the identified other candidate user equipments may include the step of obtaining emergency treatment qualification information from the other candidate user equipments, and the step of transmitting the emergency notification information to any one of the identified other candidate user equipments based on the determined priority may include the step of providing emergency guide information corresponding to the help request information in consideration of the obtained emergency treatment qualification information.

[0017] In one embodiment, the step of determining priorities of the identified other candidate user equipments by utilizing the artificial intelligence model may include the step of determining priorities of the identified other candidate user equipments by considering any one of the expected arrival time, the predicted response probability, or the determined emergency response capability information.

[0018] In one embodiment, the step of providing user information associated with the user equipment to the other user equipment may include the step of selectively transmitting the user information in consideration of a prior consent item of the user information.

[0019] According to one embodiment of the present disclosure for achieving the above-described task, a computer program stored in a computer-readable storage medium is disclosed. When the computer program is executed on one or more processors, the one or more processors perform the following operations for generating emergency notification information, wherein the operations may include: obtaining help request information from a user equipment; transmitting emergency notification information to another user equipment located within a preset area with respect to the user equipment in response to the help request information; and providing user information associated with the user equipment to the other user equipment when a response signal is received from the other user equipment that has received the emergency notification information.

[0020] In one embodiment, the assistance request information may include any one of a user input type; a text type; or a voice type.

[0021] In one embodiment, the operation of obtaining help request information from the user equipment may include the operation of displaying a GUI (Graphical User Interface) corresponding to the help request on the user equipment; and the operation of obtaining the help request information based on an input of a button included in the GUI.

[0022] In one embodiment, the operation of obtaining help request information in the voice type uttered by the user may include either an operation of extracting a target keyword from the help request information using a Real Time STT (RT-STT) model; or an operation of extracting a voice pattern from the help request information in the voice type using an artificial intelligence model.

[0023] In one embodiment, the operation may further include: obtaining location information from the user equipment after assistance request information is obtained from the user equipment.

[0024] In one embodiment, in response to the assistance request information, the operation of transmitting emergency notification information to other user equipment located within a preset area based on the user equipment may include: identifying other candidate user equipment located within the preset area based on the acquired location information; determining priorities of the identified other candidate user equipments using an artificial intelligence model; and transmitting the emergency notification information to one of the identified other candidate user equipments based on the determined priorities.

[0025] In one embodiment, the operation of determining the priority of the identified other candidate user equipments using the artificial intelligence model may further include any one of the following operations: analyzing the expected arrival times of the identified other candidate user equipments using the artificial intelligence model; predicting the response potential of the identified other candidate user equipments using the artificial intelligence model; or determining emergency response capability information of the identified other candidate user equipments.

[0026] In one embodiment, the step of providing user information associated with the user equipment to the other user equipment may include the step of selectively transmitting the user information in consideration of a prior consent item of the user information.

[0027] A computing device according to one embodiment of the present disclosure for achieving the above-described task is disclosed. The device comprises at least one processor; and a memory, wherein the at least one processor is configured to obtain help request information from a user equipment; in response to the help request information, transmit emergency notification information to another user equipment located within a preset area relative to the user equipment; and, when a response signal is received from the other user equipment that has received the emergency notification information, provide user information associated with the user equipment to the other user equipment.

[0028] In one embodiment, the assistance request information may include any one of a user input type; a text type; or a voice type.

[0029] In one embodiment, the at least one processor may be configured to display a GUI (Graphical User Interface) corresponding to a request for help on the user device; and obtain the request for help information based on an input of a button included in the GUI.

[0030] In one embodiment, the at least one processor may include one of the following operations: extracting a target keyword from the help request information using a Real Time STT (RT-STT) model; or extracting a voice pattern from the help request information of the voice type using an artificial intelligence model.

[0031] In one embodiment, the at least one processor may be further configured to: obtain location information from the user equipment after assistance request information is obtained from the user equipment.

[0032] In one embodiment, the at least one processor may be configured to identify other candidate user devices located within a preset area based on the acquired location information; determine priorities of the identified other candidate user devices using an artificial intelligence model; and transmit the emergency notification information to one of the identified other candidate user devices based on the determined priorities.

[0033] In one embodiment, the at least one processor may be configured to selectively transmit the user information taking into account prior consent items of the user information.

[0034] The present disclosure can minimize the initial response time to an emergency situation by analyzing the location, movement path, expected arrival time, etc. of surrounding users and sending a notification to the user who can provide assistance most quickly.

[0035] Meanwhile, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents described below.

[0036] FIG. 1 is a block diagram of a computing device for generating emergency situation notification information according to one embodiment of the present disclosure.

[0037] FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0038] FIG. 3 is a flowchart illustrating a method for generating emergency situation notification information according to one embodiment of the present disclosure.

[0039] FIG. 4 is a diagram illustrating a help request button displayed on a user interface corresponding to a home screen according to one embodiment of the present disclosure.

[0040] FIG. 5 is a diagram illustrating a help request confirmation pop-up displayed on a user interface corresponding to a home screen according to one embodiment of the present disclosure.

[0041] FIG. 6 is a diagram illustrating a help request button displayed on a user interface corresponding to a general self-classification screen according to one embodiment of the present disclosure.

[0042] FIG. 7 is a diagram illustrating a help request confirmation pop-up displayed on a user interface corresponding to a general person self-classification screen according to one embodiment of the present disclosure.

[0043] FIG. 8 is a diagram illustrating an operation of identifying other candidate user equipment located within a preset area based on a user equipment according to one embodiment of the present disclosure.

[0044] FIG. 9 is a diagram illustrating a push notification pop-up displayed on a user interface of a surrounding user according to one embodiment of the present disclosure.

[0045] FIG. 10 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0046] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0047] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0048] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0049] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0050] And, the term "at least one of A or B" should be interpreted to mean "if it includes only A", "if it includes only B", or "if it is combined in the composition of A and B".

[0051] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0052] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments disclosed herein. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0053] In the present disclosure, network function, artificial neural network and neural network can be used interchangeably.

[0054]

[0055] FIG. 1 is a block diagram of a computing device for generating emergency situation notification information according to one embodiment of the present disclosure.

[0056] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0057] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0058] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) to perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of a network function. For example, a CPU and a GPGPU can jointly process network function learning and data classification using network functions. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be jointly used to process network function learning and data classification using network functions. Furthermore, a computer program executed on a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0059] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0060] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0061] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0062] In addition, the network unit (150) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0063] In the present disclosure, the network unit (150) may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks, such as a local area network (LAN), a personal area network (PAN), and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0064] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0065]

[0066] FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0067] Throughout this specification, the terms artificial intelligence model, artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably.

[0068] A neural network can be composed of a set of interconnected computational units, generally referred to as nodes. These nodes can also be referred to as neurons. A neural network consists of at least one node. The nodes (or neurons) that make up a neural network can be interconnected by one or more links.

[0069] Within a neural network, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, meaning that any node that is in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0070] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by the user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set on the link corresponding to each input node.

[0071] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0072] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0073] In one embodiment of the present disclosure, a set of neurons or nodes may be defined as a layer.

[0074] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0075] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the neural networks described above.

[0076] An AI-based model according to one embodiment of the present disclosure may include a deep neural network (DNN). A DNN may refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. Using a DNN, it is possible to identify latent structures in data. That is, the latent structures of a photo, text, video, voice, protein sequence structure, gene sequence structure, peptide sequence structure, music (e.g., what objects are in a photo, what the content and emotion of a text are, what the content and emotion of a voice are, etc.), and / or the binding affinity between a peptide and MHC can be identified. Deep neural networks may include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q-networks, U-networks, Siamese networks, generative adversarial networks (GANs), transformers, and the like. The description of the above-described deep neural networks is merely an example and the present disclosure is not limited thereto.

[0077] The artificial intelligence-based model of the present disclosure can be represented by a network structure of any structure described above, including an input layer, a hidden layer, and an output layer.

[0078] The neural network that can be used in the artificial intelligence-based model of the present disclosure may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, transfer learning, active learning, or reinforcement learning. Training of the neural network may be a process of applying knowledge to the neural network to perform a specific action.

[0079] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. In supervised learning, training data with the correct answer for each training data is used (i.e., labeled training data). In unsupervised learning, the correct answer may not be labeled for each training data. For example, in supervised learning for data classification, the training data may be data with each category labeled. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the training data labels. Alternatively, in unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of neural network training to quickly achieve a certain level of performance, thereby increasing efficiency. A lower learning rate can be used in the later stages of training to increase accuracy.

[0080] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout, which disables some nodes in the network during the learning process, and the use of batch normalization layers.

[0081]

[0082] According to one embodiment of the present disclosure, the present disclosure can provide a simplified user interface that allows a user to request help with minimal effort in an emergency situation. Furthermore, the present disclosure can transmit emergency notification information to nearby users when a help request button is activated. Furthermore, the present disclosure can quickly identify and share the precise location of an emergency situation, and, if the user has given prior consent, can quickly provide important information, such as the user's health history and self-classification records. The present disclosure can appropriately share only necessary information without providing user information for items for which the user has not consented.

[0083] FIG. 3 is a flowchart illustrating a method for generating emergency notification information according to one embodiment of the present disclosure, FIG. 4 is a diagram illustrating a help request button displayed on a user interface corresponding to a home screen according to one embodiment of the present disclosure, and FIG. 5 is a diagram illustrating a help request confirmation pop-up displayed on a user interface corresponding to a home screen according to one embodiment of the present disclosure. Meanwhile, the method for generating emergency notification information described below can be performed by a computing device (100).

[0084] According to one embodiment of the present disclosure, a computing device (100) can obtain help request information from a user device (S110). For example, the help request information may include any of a user input type, a text type, or a voice type. For example, the user input type may be obtained based on a user input to a graphical user interface (GUI) displayed on the user device.

[0085] For example, the computing device (100) can obtain help request information through a user interface (UI) related to a help request provided to the user equipment. For example, referring to FIG. 4, the computing device (100) can display a home screen user interface related to a help request through an application installed on the user equipment (e.g., a smartphone). In addition, the computing device (100) can display a GUI (Graphical User Interface) corresponding to the help request on the user equipment. The home screen user interface can include a search field, a button for starting general self-classification, a button for starting Pre-KTAS classification, a button for starting KTAS classification, a list of real-time medical practice status, a "Request for help" button, etc. In addition, the computing device (100) can obtain the help request information based on an input of a button included in the GUI. For example, a user performs an action of selecting a "Request for help" button through the user equipment, and the computing device (100) can obtain user input information for the GUI (Graphical User Interface) displayed on the user equipment. The user equipment can provide input buttons or selection menus that are easily accessible to the user in an emergency situation through an intuitive and simplified user interface (UI). When the computing device (100) obtains help request information by selecting (clicking) a GUI (Graphical User Interface) related to "help request" through an application installed on the user equipment (e.g., a smartphone), the computing device (100) can display a help request confirmation pop-up window (modal window) as illustrated in FIG. 5. The computing device (100) can display a help request confirmation pop-up window (modal window) on the user equipment and ultimately confirm whether the user wants to request help.

[0086]

[0087] FIG. 6 is a drawing showing a help request button displayed on a user interface corresponding to a general self-classification screen according to one embodiment of the present disclosure, and FIG. 7 is a drawing showing a help request confirmation pop-up displayed on a user interface corresponding to a general self-classification screen according to one embodiment of the present disclosure.

[0088] For example, referring to FIG. 6, the computing device (100) may obtain help request information based on a user input to a GUI (Graphic User Interface) displayed on a user interface corresponding to a general user self-classification screen. The GUI (Graphic User Interface) displayed on the user interface corresponding to the general user self-classification screen may include buttons or text input fields, etc. If a user needs to request help while performing general user self-classification, the user may select (input) a button included in the GUI (Graphic User Interface) displayed on the user interface corresponding to the general user self-classification screen. In addition, the computing device (100) may obtain the help request information based on an input to a button included in the GUI displayed on the user interface corresponding to the general user self-classification screen. For example, the user may perform an action of selecting a "Request Help" button through the user equipment, and the computing device (100) may obtain user input information for the GUI (Graphic User Interface) displayed on the user equipment. The user equipment may provide an input button or selection menu that the user can easily access in an emergency situation through an intuitive and simplified user interface (UI). When the computing device (100) obtains help request information by selecting (clicking) a GUI (Graphical User Interface) related to "help request" through an application installed on the user equipment (e.g., a smartphone), the computing device (100) may display a help request confirmation pop-up window (modal window) as illustrated in FIG. 7. The computing device (100) may display a help request confirmation pop-up window (modal window) on the user equipment and ultimately confirm whether the user will make a help request.

[0089] For example, the computing device (100) may obtain help request information through a text input field via an application installed on the user equipment (e.g., a smartphone). For example, the computing device (100) may obtain help request information in text form through a text input field displayed on a user interface corresponding to a general user self-classification screen. The user interface including the text input field may be configured so that the user can clearly describe his or her condition or request. The user may input help request information in text form or by clicking a button (e.g., a touch screen or a physical button) through the user interface (UI) provided on the user equipment. The computing device (100) may utilize the help request information obtained through the user interface (UI) provided on the user equipment as key information describing the emergency situation. For example, the computing device (100) may classify the type, severity, and related information of the emergency situation by analyzing the obtained text data through a natural language processing (NLP) algorithm. The computing device (100) can apply the acquired text type help request information to an NLP algorithm or language model to extract target keywords related to the help request information.

[0090] According to one embodiment, the computing device (100) may obtain voice-type help request information spoken by the user through a user interface (UI) provided to the user equipment. For example, the computing device (100) may obtain conversation text by utilizing a Speech-To-Text (STT) model. As another example, the computing device (100) may obtain real-time conversation text based on conversational speech occurring in an emergency situation by utilizing a Real Time STT (RT-STT) model. The computing device (100) may apply the obtained conversation text or real-time conversation text to an NLP algorithm or a language model to extract target keywords related to the help request information. For example, the computing device (100) may extract predetermined target keywords from the obtained conversation text or real-time conversation text. For example, the predetermined target keywords may include words or sentences related to the help request information (e.g., pain, difficulty breathing, help me, I'm sick), etc. Keyword extraction can be performed by referencing a predefined emergency response database, and the computing device (100) can also assign an urgency rating to each keyword. For example, the computing device (100) can utilize a language model to analyze the contextual relationships and frequencies between keywords in acquired conversation text or real-time conversation text, thereby assessing whether the utterance indicates a specific emergency situation. For example, the computing device (100) can determine that a situation with a high probability of a heart attack is likely when the words "chest," "pain," and "heart" are repeatedly used.

[0091] According to one embodiment, the computing device (100) may utilize an artificial intelligence (AI) model to extract voice patterns from voice-type help request information. For example, the computing device (100) may utilize an artificial intelligence (AI) model to extract voice patterns from the user's voice data and analyze them to determine the severity of an emergency situation. For example, the user equipment may collect voice-type help request information (voice spoken by the user) in real time, and the computing device (100) may perform preprocessing processes such as background noise removal, volume adjustment, and time alignment before inputting the voice-type help request information into the AI ​​model. For example, the voice signal may be converted into an analyzable form through spectrogram transformation or a neural network-based feature extraction model, but is not limited thereto. The AI ​​model may extract voice patterns such as frequency, pitch, speech rate, and volume changes from the voice-type help request information. During the feature extraction process, the AI ​​model may identify voice patterns that appear in urgent situations (e.g., trembling, high-pitched voice, fast speech rate, etc.). Additionally, the AI ​​model can analyze extracted voice patterns to assess the severity of an emergency. For example, if the AI ​​model detects a trembling voice, a rapid speech rate, and a high-pitched, strong tone, it can determine that the user is experiencing an emergency due to a high level of stress or fear. Meanwhile, the computing device (100) can significantly improve the accuracy and efficiency of emergency response by contributing to severity assessment and response priority setting beyond simple voice conversion.

[0092] In one embodiment, the computing device (100) may request real-time location data from the user equipment at the time the assistance request information is obtained. For example, the location information may be obtained via the Global Positioning System (GPS), Wi-Fi signals, Bluetooth signals, or cellular network data. The user equipment transmits the requested location information to the computing device (100), and the location information may include coordinate values ​​(latitude and longitude) or relative distance information.

[0093]

[0094] FIG. 8 is a diagram illustrating an operation of identifying other candidate user equipment located within a preset area based on a user equipment according to one embodiment of the present disclosure.

[0095] According to one embodiment, the computing device (100) may identify other candidate user devices located within a preset area based on the acquired location information in order to transmit emergency notification information to other user devices. For example, the computing device (100) may identify other candidate user devices located within a preset area (e.g., a preset radius or a designated geographic area) based on the acquired location information from the user devices. For example, the computing device (100) may acquire location information of other candidate user devices located within the preset area and an additional preset area based on the acquired location information. For example, the computing device (100) may identify other candidate user devices located within a preset area (e.g., 1 km) and an additional preset area (0.5 km) based on the acquired location information from the user devices. Referring to FIG. 6 as an example, the computing device (100) can identify a first candidate user device (B1), a second candidate user device (B2), a third candidate user device (B3), and a fourth candidate user device (B4) located within a preset radius relative to the user device (A). In addition, the computing device (100) can also identify a fifth candidate user device (B4) located within an additional preset area relative to the user device (A).

[0096] In one embodiment, the computing device (100) may utilize an artificial intelligence model to determine priorities for other candidate user devices located within a preset area. For example, the computing device (100) may assign the highest priority to the candidate user device with the closest distance and fastest arrival time relative to the user device.

[0097] For example, the computing device (100) may analyze the expected arrival times of the identified other candidate user devices using an artificial intelligence model to determine priorities for the identified other candidate user devices. For example, the computing device (100) may obtain current location information of the identified other candidate user devices. Furthermore, the computing device (100) may estimate the moving speed based on the location information of the identified other candidate devices. Furthermore, the computing device (100) may estimate the moving speed and moving direction of the candidate user devices using location data including GPS, Wi-Fi signals, Bluetooth data, cellular data, etc. For example, the computing device (100) may analyze the real-time moving status of the identified other candidate user devices using sensor data (e.g., accelerometer, gyroscope). Furthermore, the computing device (100) may obtain traffic situation and weather condition information from an external server (e.g., a weather service or a road traffic management server). For example, the computing device (100) can calculate an optimal route for other candidate user devices to travel to the location of the user device. The computing device (100) can collect recommended routes from a map API (e.g., Google Maps, OpenStreetMap). Additionally, the computing device (100) can collect candidate routes by calculating various route options and expected travel times. For example, the computing device (100) can utilize an artificial intelligence model to analyze the expected arrival times of other candidate user devices identified based on road condition information and travel speed. For example, the computing device (100) can utilize an artificial intelligence model to calculate the expected arrival times of other candidate user devices identified based on previously acquired data.For example, the computing device (100) can use a deep learning-based route prediction model to calculate an optimal travel route, and predict the expected arrival time by selecting the fastest route by reflecting past traffic patterns and current conditions. In addition, the computing device (100) can use an artificial intelligence model to analyze the means of transportation (e.g., walking, car, bicycle, etc.) of other identified candidate user devices to estimate an average speed, and predict changes in the travel speed to calculate the expected arrival time. In addition, the computing device (100) can use the distance and average speed between the identified candidate user devices to predict the basic arrival. In addition, the computing device (100) can apply additional factors such as traffic delays, signal waiting times, road closures, and the interior environment of a building to modify the expected arrival time. In addition, the computing device (100) can additionally reflect weather, time zone (e.g., rush hour), and road condition data to calculate the expected arrival time of other identified candidate user devices. For example, in the computing device (100), the first candidate user equipment (B1) is moving by car, the road is not congested, the speed is measured as 50 km / h, and the artificial intelligence model can predict that it will take 3 minutes to travel through the optimal route. In addition, in the computing device (100), the second candidate user equipment (B2) is moving by foot, the speed is 5 km / h, and the artificial intelligence model can predict that it will take 7 minutes. In this case, the computing device (100) can select the first candidate user equipment (B1) as the top priority. The computing device (100) can utilize the artificial intelligence model to calculate the expected arrival times of the other identified candidate equipment by additionally considering at least one of the route, speed, time, environmental variables, etc. The computing device (100) can preferentially select the candidate with the shortest arrival time.AI-based predictions are more precise than simple distance calculations, and by reflecting real-time variables, they can enable accurate and rapid responses in emergency situations.

[0098] For another example, the computing device (100) inputs the location, movement speed, past response history, distance from the user equipment, etc. of the identified candidate user equipment into the artificial intelligence model, and the artificial intelligence model can comprehensively evaluate the reachability time, emergency response potential, user profile (e.g., occupation, possession of medical knowledge), etc. of each other candidate user equipment to determine priority. The above-described matters are merely examples, and the present disclosure is not limited thereto.

[0099] For example, the computing device (100) may utilize an artificial intelligence model to predict the response likelihood of other identified candidate user devices. For example, the computing device (100) may utilize the artificial intelligence model to predict the response likelihood of other identified candidate user devices based on either the previous response history of the other identified candidate user devices or the time or location at which the assistance request information was obtained. For example, the computing device (100) may acquire user environment data, configuration data, time data, past behavior data, etc. of the identified other identified candidate user devices. For example, the environment data may include current location information, movement status information, battery status information, network connection status information, etc. of the identified other identified candidate user devices. In addition, the time data may include information related to the identified time zone, day of the week, location (e.g., work, home, outdoors), etc. of the other identified candidate user devices. In addition, the configuration data may include information related to whether the identified other identified candidate user devices allow notifications, whether they are in use, etc. In addition, the past behavior data may include information related to past response records, response time patterns, etc. The computing device (100) inputs at least one of environmental data, configuration data, time data, or past behavior data acquired from each of the identified other candidate user devices into an artificial intelligence model, and the artificial intelligence model can predict the response probability of each of the identified other candidate user devices. For example, the artificial intelligence model can predict that the response probability of a first candidate user device (B1) among the identified other candidate user devices is low when the battery is low or the network connection is unstable. For another example, if the artificial intelligence model has learned a pattern of "responding well to notifications between 2 PM and 4 PM" from the past records of a second candidate user device (B2) among the identified other candidate user devices, the model can predict that the response probability is high during that time period.Additionally, the AI ​​model can predict the likelihood of each candidate's response by utilizing the fact that the third candidate user device (B3) has accepted emergency notification information 80% of the time in the past, and the fourth candidate user device (B4) has accepted emergency notification information 50% of the time in the past.

[0100] As another example, the computing device (100) may determine the emergency response capability information of the other identified candidate user devices. For example, the computing device (100) may obtain pre-entered profile information corresponding to the other identified candidate user devices. For example, the pre-entered profile information may include personal information, emergency treatment qualification information, etc. For example, the computing device (100) may obtain pre-entered profile information in which a first user possessing a first candidate user device (B1) is registered as a medical professional (e.g., a doctor or nurse), and a second user possessing a second candidate user device (B2) is registered as a general user. The computing device (100) may utilize the pre-entered profile information of the other identified candidate user devices to determine the emergency response capability information of the other identified candidate user devices. For example, the computing device (100) may determine that the first user of the first candidate user device (B1) is evaluated as a medical professional and thus has high emergency response capability.

[0101] For example, the computing device (100) may determine the priority of the identified other candidate user equipments by considering any one of the expected arrival time, the expected response probability, or the determined emergency response capability information. For example, the computing device (100) may determine the priority of the identified other candidate user equipments by utilizing two or more pieces of information from the expected arrival time, the expected response probability, or the determined emergency response capability information. For example, if the identified other candidate user equipments are a first candidate user equipment (B1), a second candidate user equipment (B2), a third candidate user equipment (B3), and a fourth candidate user equipment (B4), the computing device (100) may obtain the location information of each of the identified other candidate user equipments. The computing device (100) may obtain the location information by utilizing GPS data, Wi-Fi signal strength, and Bluetooth signal information of each of the identified other candidate user equipments. In addition, the computing device (100) may estimate the time required to arrive from the current location of each of the identified other candidate user equipments to the user equipment where the emergency situation occurred. In addition, the computing device (100) can obtain information such as the battery status, whether notifications are allowed, and past response records of each of the identified other candidate user devices. In addition, the computing device (100) can obtain emergency response information (e.g., whether the user is in a medical-related occupation, first aid experience, etc.) registered in each of the identified other candidate user devices. According to one embodiment, the computing device (100) can determine the first candidate user based on the information obtained from the first candidate user device (B1), such as current vehicle movement, expected arrival time of 5 minutes, notification allowance status, past emergency notification response rate of 90%, and medical-related occupation (holding first aid certification).In addition, the computing device (100) can determine the second candidate user based on the information obtained from the second candidate user equipment (B2), such as the current vehicle movement, expected arrival time of 12 minutes in a traffic congested area, notification acceptance status, past emergency notification response rate of 50%, and general user. In addition, the computing device (100) can determine the third candidate user based on the information obtained from the third candidate user equipment (B3), such as the current stationary state, expected arrival time of 8 minutes, notification silence status, past emergency notification response rate of 70%, and CPR training completion. In addition, the computing device (100) can determine the fourth candidate user based on the information obtained from the fourth candidate user equipment (B4), such as the fourth candidate user moving on foot, located at a very close distance, expected arrival time of 4 minutes, notification acceptance status, past emergency notification response rate of 80%, and no first aid experience. The computing device (100) can calculate a weighted score based on the determined result and determine the final priority. For example, the computing device (100) may assign 50% weight to the expected arrival time, 30% weight to the likelihood of response, and 20% weight to the ability to respond to emergency situations. In other words, the computing device (!00) may assign a higher score as the expected arrival time is closer, a higher score as the likelihood of response is higher, and a higher score as the number of emergency response experiences is greater. Considering the above, the computing device (100) may determine the first candidate user equipment (B1) as the candidate with the highest priority based on the final score, and may determine the priorities in the following order: the fourth candidate user equipment (B4), the third candidate user equipment (B3), and the second candidate user equipment (B2).

[0102]

[0103] FIG. 9 is a diagram illustrating a push notification pop-up displayed on a user interface of a surrounding user according to one embodiment of the present disclosure.

[0104] According to one embodiment of the present disclosure, in response to the help request information, the computing device (100) may transmit emergency notification information to other user equipment located within a preset area relative to the user equipment (S120). For example, the computing device (100) may transmit emergency notification information to all user equipment located within a preset area relative to the user equipment. Referring again to FIG. 6, the computing device (100) may transmit emergency notification information to a first candidate user equipment (B1), a second candidate user equipment (B2), a third candidate user equipment (B3), and a fourth candidate user equipment (B4) located within a preset area relative to the user equipment (A).

[0105] According to one embodiment, the computing device (100) may transmit emergency notification information to any one of the other candidate user devices identified based on the determined priority. For example, referring to FIG. 7, the computing device (100) may transmit emergency notification information to any one of the other candidate user devices identified based on the priority by utilizing a push notification pop-up. For example, if the candidate user device with the highest priority is determined to be the first candidate user device (B1), the computing device (100) may first transmit emergency notification information to the first candidate user device (B1). In this case, if the first candidate user device (B1) does not receive the notification or does not transmit a response signal, the computing device (100) may transmit the emergency notification information to the fourth candidate user device (B4) in the next order. Here, if the computing device (100) fails to receive a response signal from the highest priority candidate user equipment within a preset time period (e.g., 30 seconds), it may transmit emergency notification information to the next candidate user equipment. For example, the computing device (100) may simultaneously transmit emergency notification information to multiple candidate user equipments, depending on the severity of the situation. For reference, the severity of the situation may be determined based on the assistance request information obtained from the user equipment.

[0106]

[0107] According to one embodiment of the present disclosure, when the computing device (100) receives a response signal from another user equipment that has received emergency notification information, it may provide user information associated with the user equipment to the other user equipment (S130). For example, the computing device (100) may selectively provide the user information in consideration of the user information's pre-consent items. For example, the user information may include the user's medical record, health status, contact information, etc. Before providing the user information to the other user equipment, the computing device (100) may check the user's pre-set consent items. For example, if the user has agreed to "sharing medical history information in an emergency" but not to "sharing detailed contact information," the computing device (100) may provide only the medical history information to the other user equipment. For example, when the computing device (100) receives a response signal from the first candidate user equipment (B1) that has received emergency notification information, the computing device (100) may selectively provide the user information associated with the user equipment to the first candidate user equipment (B1) in consideration of the user information's pre-consent items. Meanwhile, the computing device (100) guarantees privacy by limiting the sharing of user information based on the user's consent, and can effectively share necessary information even in emergency situations.

[0108] According to one embodiment, the computing device (100) may provide emergency situation guidance information corresponding to the help request information, taking into account the acquired emergency treatment qualification information. The computing device (100) may provide emergency situation guidance information appropriate for the situation, taking into account the emergency treatment qualification information of other user equipment that responded in an emergency situation. This configuration may improve the effectiveness of emergency response and help prevent secondary accidents caused by inappropriate actions. For example, if the computing device (100) receives a response signal from another user equipment that has received emergency situation notification information, it may acquire emergency treatment qualification information from the other user equipment. In addition, the computing device (100) may retrieve the emergency treatment information of the other user equipment stored in the memory (130) to acquire emergency treatment qualification information for the other user equipment that has received the emergency situation notification information. The computing device (100) may analyze the acquired emergency treatment qualification information to determine the scope of emergency treatment that the corresponding user can perform. For example, the computing device (100) can generate an appropriate first aid guide by mapping the type of emergency (e.g., cardiac arrest, respiratory distress, etc.) and qualification information. In addition, the computing device (100) can provide specific and step-by-step first aid guides in an intuitive form, such as text, voice, or video, to other user equipment that responds. For example, the computing device (100) can determine that the current user is a cardiac arrest patient by considering help request information obtained from the user equipment. In response to the help request information, the computing device (100) can transmit emergency notification information to other user equipment located within a preset area based on the user equipment. In addition, when the computing device (100) receives a response signal from another user equipment that has received the emergency notification information, the computing device (100) can obtain the emergency treatment qualification information of the other user equipment that responded.The computing device (100) may provide user information and emergency situation guide information related to the user equipment to the other user equipment based on the emergency treatment qualification information of the other user equipment that responded. For example, if the emergency treatment qualification information of the other user equipment that responded indicates that the user has a CPR certificate, the computing device (100) may generate and provide a CPR performance guide necessary in a cardiac arrest situation. In another example, if the emergency treatment qualification information of the other user equipment that responded indicates that the user is a layperson, the computing device (100) may generate and provide a guide such as an airway securing method and a 119 reporting method. In another example, the user may be a traffic accident patient with multiple trauma, and the responding user may be a first candidate user terminal (B1, who has a CPR qualification) and a second candidate user terminal (B2, who has an emergency rescue qualification). The computing device (100) may generate a guide appropriate for each of the qualifications and capabilities. For example, the computing device (100) may provide "a method for bleeding management and compression" to a first candidate user terminal (B1), and "a method for maintaining the position of an injured person and protecting them before the arrival of an ambulance" to a second candidate user terminal (B2). Meanwhile, the computing device (100) may provide emergency situation guidance information, thereby enabling accurate and rapid emergency measures to be taken in each situation, reducing the burden on responding users and resolving emergency situations, and efficiently responding to emergency situations by distributing roles among multiple responders according to their qualifications.

[0109]

[0110] The steps described in the above description may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present disclosure. Furthermore, some steps may be omitted as needed, and the order of the steps may be changed.

[0111]

[0112] Meanwhile, a computer-readable medium storing a data structure according to an embodiment of the present disclosure is disclosed.

[0113] A computer-readable medium storing a data structure according to one embodiment of the present disclosure is disclosed. The aforementioned data structure can be stored in a memory within the present disclosure, executed by a processor, and transmitted and received by a network unit.

[0114] A data structure can refer to the organization, management, and storage of data that enables efficient access and modification. A data structure can refer to the organization of data to solve specific problems (e.g., data analysis, data retrieval, data storage, data modification). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include connections between user-defined data elements. Physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0115] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one piece of data is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each piece having a pointer. In a linked list, a pointer can contain information about the next or previous piece of data. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0116] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structures can include tree data structures. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0117] Throughout this specification, the terms artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they are collectively referred to as neural networks. A data structure may include a neural network. And, a data structure including a neural network may be stored on a computer-readable medium. A data structure including a neural network may also include preprocessed data for processing by a neural network, data input to a neural network, neural network weights, neural network hyperparameters, data obtained from a neural network, activation functions associated with each node or layer of a neural network, loss functions for neural network learning, etc. A data structure including a neural network may include any of the components disclosed above. That is, a data structure including a neural network may be configured to include all or any combination of preprocessed data for processing by a neural network, data input to a neural network, neural network weights, neural network hyperparameters, data obtained from a neural network, activation functions associated with each node or layer of a neural network, loss functions for neural network learning, etc. In addition to the aforementioned configurations, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0118] The data structure may include data input to a neural network. The data structure including the data input to the neural network may be stored on a computer-readable medium. The data input to the neural network may include training data input during the neural network training process and / or input data input to the neural network after training has been completed. The data input to the neural network may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to the neural network. Accordingly, the data structure may include data that is the target of preprocessing and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0119] The data structure may include weights of the neural network. (In this specification, the terms "weight" and "parameter" may be used interchangeably.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on the values ​​input to the input nodes connected to the output node and the weights set for the links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0120] By way of example and not limitation, the weights may include weights that vary during the neural network training process and / or weights that have completed neural network training. The weights that vary during the neural network training process may include weights at the start of the training cycle and / or weights that vary during the training cycle. The weights that have completed neural network training may include weights that have completed the training cycle. Accordingly, a data structure including the weights of a neural network may include a data structure including weights that vary during the neural network training process and / or weights that have completed neural network training. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of a neural network. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0121] A data structure including neural network weights can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The serialized data structure including neural network weights can be reconstructed on the same computing device or another computing device through deserialization. The data structure including neural network weights is not limited to serialization. Furthermore, the data structure including neural network weights can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, an R-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0122] The data structure may include hyperparameters of a neural network. Furthermore, the data structure including the hyperparameters of the neural network may be stored on a computer-readable medium. The hyperparameters may be variables that can be varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle repetitions, weight initialization (e.g., setting a range of weight values ​​to be subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0123]

[0124] FIG. 10 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0125] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may also be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0126] Generally, program modules include routines, programs, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0127] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0128] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0129] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0130] An exemplary environment for implementing various aspects of the present disclosure is illustrated, including a computer (1102), which includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0131] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0132] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0133] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those of ordinary skill in the art will appreciate that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0134] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0135] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0136] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0137] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0138] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0139] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0140] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0141] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0142] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0143] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0144] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0145] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0146] As described above, the relevant contents have been described in the best form for carrying out the invention.

Claims

1. A method for generating emergency notification information, performed by a computing device, Step of obtaining help request information from user equipment; In response to the above help request information, a step of transmitting emergency situation notification information to another user equipment located within a preset area based on the user equipment; and When receiving a response signal from said other user equipment that has received said emergency situation notification information, a step of providing user information associated with said user equipment to said other user equipment; Including, method.

2. In paragraph 1, The above assistance request information is, User input type; Text type; or Voice type; Including any one of the following, method.

3. In paragraph 1, The step of obtaining help request information from the above user equipment is: A step of displaying a GUI (Graphic User Interface) corresponding to a request for help on the user equipment; and A step of obtaining the help request information based on the input of the button included in the above GUI. Including, method.

4. In paragraph 2, The above user input type is obtained based on user input to the GUI (Graphic User Interface) displayed on the user equipment. method.

5. In paragraph 2, The step of obtaining help request information from the above user equipment is: A step of extracting target keywords from the help request information by utilizing the RT-STT (Real Time STT) model; or A step of extracting a voice pattern from the help request information of the above voice type by utilizing an artificial intelligence model; Including any one of the following, method.

6. In paragraph 2, The above method, After the help request information is obtained from the above user equipment: Step of obtaining location information from the above user equipment Including more, method.

7. In paragraph 6, In response to the above help request information, the step of transmitting emergency notification information to other user equipment located within a preset area based on the user equipment is as follows: A step of identifying other candidate user devices located within a preset area based on the acquired location information; A step of using an artificial intelligence model to determine priorities of the identified other candidate user devices; and A step of transmitting the emergency notification information to any one of the other identified candidate user devices based on the determined priority. Including, method.

8. In paragraph 7, The step of determining the priority of the identified other candidate user devices by utilizing the above artificial intelligence model is: A step of analyzing the expected arrival times of the identified other candidate user devices by utilizing the above artificial intelligence model; A step of predicting the response probability of the identified other candidate user devices by utilizing the above artificial intelligence model; or Step for determining emergency response capability information of other candidate user equipment identified above Including any one of the following, method.

9. In paragraph 8, The step of analyzing the expected arrival times of the identified other candidate user devices using the above artificial intelligence model is as follows: A step of obtaining location information of other candidate user devices identified above; A step of estimating a moving speed based on the location information of the other identified candidate user devices; and A step of analyzing the expected arrival time of the identified other candidate user devices based on road condition information and moving speed by utilizing the above artificial intelligence model; Including, method.

10. In paragraph 8, The step of predicting the response probability of the identified other candidate user devices by utilizing the above artificial intelligence model is: A step of predicting the possibility of response of the other candidate user devices based on the previous response history of the other candidate user devices, the time or place where the help request information was obtained, by utilizing the artificial intelligence model. Including, method.

11. In paragraph 8, The step of determining the emergency response capability information of the other candidate user equipment identified above is: Comprising a step of obtaining emergency treatment qualification information from said other candidate user devices, The step of transmitting the emergency notification information to any one of the identified other candidate user devices based on the determined priority is: A step of providing emergency situation guide information corresponding to the help request information, taking into account the acquired emergency treatment qualification information. Including, method.

12. In paragraph 8, The step of determining the priority of the identified other candidate user devices by utilizing the above artificial intelligence model is: A step for determining priorities of the identified other candidate user equipments by considering any one of the above-mentioned expected arrival time, the above-mentioned predicted response possibility, or the above-mentioned determined emergency response capability information. Including, method.

13. In paragraph 1, The step of providing user information associated with the user equipment to the other user equipment is: Step for selectively transmitting the user information in consideration of the prior consent items of the user information above Including, method.

14. A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, causes the one or more processors to perform the following operations for generating emergency situation notification information, the operations being: An action to obtain help request information from user equipment; In response to the above help request information, an action of transmitting emergency notification information to another user equipment located within a preset area based on the user equipment; and An action of providing user information associated with the user equipment to the other user equipment when a response signal is received from the other user equipment that has received the emergency situation notification information; Including, A computer program stored on a computer-readable storage medium.

15. As a computing device, at least one processor; and memory; Including, At least one processor of the above, Obtain help request information from user equipment; In response to the above help request information, transmit emergency notification information to other user equipment located within a preset area based on the user equipment; and When receiving a response signal from said other user equipment that has received said emergency situation notification information, configured to provide user information associated with said user equipment to said other user equipment, device.

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