Method and apparatus for managing obesity of user

Abstract

According to one aspect of the present disclosure, a method by which a server manages the obesity of a user is provided. The method may comprise the steps of: obtaining time-series health information of the user; obtaining life pattern information of the user from the health information by using a pre-trained artificial intelligence-based life pattern analysis model; and providing, on the basis of the life pattern information, first appetite-reducing sound content to a user device corresponding to the user.

Description

Method and device for managing obesity in a user

[0001] The present disclosure relates to software medical technology, and more particularly, to a method and apparatus for providing a user with a software medical device for obesity management.

[0002] With advancements in IT, the digital therapeutics (DTx) industry, which guarantees high accessibility to specialized medical fields and privacy protection, is emerging globally. Furthermore, the COVID-19 pandemic has also fueled growing interest in the digital healthcare industry, which relies on non-face-to-face treatment through wearable devices, in vitro diagnostic devices, digital therapeutics, and telemedicine platform technologies.

[0003] When utilizing software and digital devices rather than medications, the probability of toxicity and side effects is significantly lower than with pharmaceuticals, and commercialization is possible in a relatively short period of time compared to the existing new drug development process that requires a long research period from preclinical to clinical trials.

[0004] Meanwhile, the rate of obesity is rapidly increasing worldwide due to factors such as overnutrition and the proliferation of mobile devices. Obesity is a disease that requires ongoing individual management. It can be prevented and treated through consistent exercise and diet.

[0005] The present disclosure has been made in response to the aforementioned background technology, and aims to provide a method and device for managing obesity in a user.

[0006] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0007] According to one embodiment of the present disclosure for solving the aforementioned problem, a method for managing a user's obesity, performed by a server, is provided. The method may include: acquiring time-series health information of the user; acquiring lifestyle pattern information of the user from the health information using a pre-trained artificial intelligence-based lifestyle pattern analysis model; and providing first appetite-reducing sound content to a user device corresponding to the user based on the lifestyle pattern information.

[0008] In one embodiment, the health information includes at least one of the user's activity information, emotional information, sleep information, meal information, or body information, and the meal information may include the type of food consumed by the user, the user's meal location, meal amount, and meal time duration.

[0009] In one embodiment, the health information includes body information of the user, and the method may further include the steps of generating obesity management information corresponding to the user based on the body information and the lifestyle pattern information; and transmitting the obesity management information to the user device.

[0010] In one embodiment, the obesity management information includes recommended diet information corresponding to the user, and the step of generating the obesity management information may include: calculating a digestion time period between an average time at which the user completes the last meal before sleeping and an average sleep start time of the user based on the lifestyle pattern information; calculating a recommended calorie intake, a recommended digestion time period, and a recommended completion time of the last meal based on the body information and the digestion time period; and generating the recommended diet information including the recommended calorie intake, the recommended digestion time period, and the recommended completion time.

[0011] In one embodiment, the obesity management information includes recommended exercise information corresponding to the user, and the step of generating the obesity management information may include: calculating daily average exercise information of the user based on the lifestyle pattern information; determining daily recommended calories burned, daily recommended exercise time period, and daily recommended exercise type based on the body information and the daily average exercise information; and generating the recommended exercise information including the daily recommended calories burned, the daily recommended exercise time period, and the daily recommended exercise type.

[0012] In one embodiment, the lifestyle pattern information includes the user's eating pattern information, and the step of providing the first appetite-reducing sound content may include: identifying a target time period during which the user has a probability of consuming food greater than or equal to a preset probability value based on the eating pattern information; and providing the first appetite-reducing sound content to the user device during the target time period.

[0013] In one embodiment, the lifestyle pattern information includes at least one of the user's activity pattern information, emotional pattern information, sleep pattern information, or meal pattern information, and the method may further include at least one of the steps of: providing at least one of exercise stimulation sound content or concentration enhancement sound content to the user device based on the activity pattern information; providing at least one of stress relief sound content or depression reduction sound content to the user device based on the emotional pattern information; or providing sleep induction sound content to the user device based on the sleep pattern information.

[0014] In one embodiment, the method may further include: identifying a state change time point at which at least one of the user's activity state, emotional state, sleep state, or meal state changes based on the lifestyle pattern information; inputting the user's lifestyle pattern information into a pre-learned artificial intelligence-based cognitive behavioral therapy (CBT) model at a preset cycle or at the state change time point to obtain cognitive behavioral therapy data corresponding to the user's current state; and transmitting the cognitive behavioral therapy data to the user device.

[0015] In one embodiment, the cognitive behavioral therapy data includes information on recommended sound content corresponding to the user's current state, and the cognitive behavioral therapy model may correspond to an artificial intelligence-based large-scale language model that is fine-tuned to output information on recommended sound content corresponding to the user's current state from the user's lifestyle pattern information.

[0016] In one embodiment, the method may further include, after the step of providing the first appetite-reducing sound content, the step of obtaining reproduction information and changed health information of the first appetite-reducing sound content of the user; and, based on the reproduction information and the changed health information, providing, to the user device, second appetite-reducing sound content that is different from the first appetite-reducing sound content in at least one of a key, a tempo, a LUFS level, or a timbre.

[0017] In one embodiment, the method may further include: identifying a state change time point at which at least one of the user's activity state, emotional state, sleep state, or eating state changes based on the life pattern information; inputting the user's life pattern information into a pre-learned artificial intelligence-based health management sound generation model at a preset cycle or at the state change time point to generate health management sound content corresponding to the user's current state; and providing the health management sound content to the user device.

[0018] In one embodiment, the method may further include the steps of grouping the plurality of users into a plurality of groups based on health information of the plurality of users; obtaining group lifestyle pattern information of each of the plurality of groups; and transmitting first group lifestyle pattern information of a first group including the user to the user device.

[0019] In one embodiment, the first appetite reduction sound content is provided through an obesity management application running on the user device, and the obesity management application may include a user interface including a first screen displaying information about the first appetite reduction sound content and a second screen including a plurality of input objects for health information of the user.

[0020] According to one aspect of the present disclosure, a server for managing a user's obesity is provided. The server comprises at least one processor and a memory, wherein the at least one processor acquires time-series health information of the user, acquires lifestyle pattern information of the user from the health information using a pre-learned artificial intelligence-based lifestyle pattern analysis model, and provides first appetite-reducing sound content to a user device corresponding to the user based on the lifestyle pattern information.

[0021] According to one aspect of the present disclosure, a computer program stored in a computer-readable storage medium is provided. When the computer program is executed by one or more processors, the computer program causes the one or more processors to perform methods for managing obesity of a user, the methods including: obtaining time-series health information of the user; obtaining lifestyle pattern information of the user from the health information using a pre-learned artificial intelligence-based lifestyle pattern analysis model; and providing first appetite-reducing sound content to a user device corresponding to the user based on the lifestyle pattern information.

[0022] The present disclosure may provide a method and device for managing obesity in a user.

[0023] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains from the description below.

[0024] Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to similar components generally. In the following examples, for purposes of explanation, numerous specific details are set forth to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspects may be practiced without these specific details.

[0025] FIG. 1 is a block diagram of a server, a network, and a user device for managing a user's obesity according to one embodiment of the present disclosure.

[0026] FIG. 2 is a diagram illustrating an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0027] FIG. 3 is a flowchart illustrating a method for managing a user's obesity, performed on a server according to one embodiment of the present disclosure.

[0028] FIG. 4 is a block diagram illustrating a plurality of modules included in a server according to one embodiment of the present disclosure.

[0029] FIG. 5 is a diagram illustrating a process of obtaining user lifestyle pattern information using a lifestyle pattern analysis model according to one embodiment of the present disclosure.

[0030] FIG. 6 is a diagram illustrating an example of a user interface related to appetite reduction sound content according to one embodiment of the present disclosure.

[0031] FIG. 7 is a diagram illustrating an example of a user interface for obtaining health information of a user according to one embodiment of the present disclosure.

[0032] FIG. 8 illustrates a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0033] 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.

[0034] 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).

[0035] 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 terms "or" and "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated listed items.

[0036] 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."

[0037] 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".

[0038] 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.

[0039] 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.

[0040] In the present disclosure, terms such as first, second, or third, expressed as N, may be used to distinguish between different or corresponding entities. For example, entities expressed as first and second may be the same or different. N may be a natural number.

[0041] FIG. 1 is a block diagram of a server, a network, and a user device for managing a user's obesity according to one embodiment of the present disclosure.

[0042] Referring to FIG. 1, the server (100) may include a processor (110), a memory (130), and a network unit (150). The configuration of the server (100) illustrated in FIG. 1 is merely a simplified example, and in one embodiment, the server (100) may include other configurations, and may include only some of the disclosed configurations. The server (100) in the present disclosure may refer to any type of device having processing capabilities.

[0043] The server (100) may include any type of computer system or computer device, such as, for example, a microprocessor, a mainframe computer, a digital processor, a portable device, or a device controller. The server (100) may include any type of computing device and / or user device.

[0044] The processor (110) may be composed of one or more cores and may include a processor for performing operations related to processing data, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a neural processing unit (NPU), or a tensor processing unit (TPU) of the server (100).

[0045] According to one embodiment of the present disclosure, the processor (110) may perform operations for learning an artificial intelligence-based model. For example, 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 the input data, calculating errors, and updating weights of an artificial intelligence-based model using backpropagation. The processor (110) may process learning of a network function. Furthermore, in one embodiment, the processor (110) may perform learning of a network function and data processing of the network function by jointly using processors of a plurality of computing devices.

[0046] The processor (110) can typically control the overall operation of the server (100). The processor (110) can process signals, data, information, etc. input or output through components included in the server (100) or run a computer program stored in the memory (130), thereby providing or processing appropriate information or functions to the user.

[0047] The processor (110) can read a computer program stored in the memory (130) and perform data processing for managing a user's obesity according to one embodiment of the present disclosure. In addition, the processor (110) can implement any component for performing data processing for managing a user's obesity according to one embodiment of the present disclosure.

[0048] In one embodiment, the processor (110) may obtain time-series health information of the user. Using a pre-trained artificial intelligence-based lifestyle pattern analysis model, the processor (110) may obtain lifestyle pattern information from the user's health information. Based on the user's lifestyle pattern information, the processor (110) may provide first appetite-reducing sound content to a user device corresponding to the user.

[0049] According to one embodiment, the memory (130) can store any form of information generated or determined by the processor (110) and / or any form of information received by the network unit (150). The memory (130) can be operated by the processor (110).

[0050] According to one embodiment, 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 / or an optical disk. The server (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet.

[0051] In one embodiment, the memory (130) may store any form of information for managing the user's obesity according to one embodiment of the present disclosure. For example, the memory (130) may store the user's health information, lifestyle pattern information, appetite-reducing sound content, exercise-stimulating sound content, concentration-enhancing sound content, stress-relieving sound content, depression-reducing sound content, sleep-inducing sound content, a pre-learned artificial intelligence-based lifestyle pattern analysis model, a health management sound generation model, a cognitive behavioral therapy model, and the like. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0052] The network unit (150) according to one embodiment of the present disclosure may use any type of wired or wireless communication system.

[0053] 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 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. The technologies described in the present disclosure may also be used in other networks mentioned above.

[0054] In one embodiment, the network unit (150) may communicate with a user device (200) to transmit and receive data to manage the user's obesity according to one embodiment of the present disclosure. FIG. 1 is merely an exemplary diagram, and the network unit (150) may communicate with multiple user devices to transmit and receive data.

[0055] A user device (200) according to one embodiment of the present disclosure may include a personal computer (PC), a notebook, a mobile terminal, a smart phone, a tablet PC, a wearable device, etc. owned by a user, and may include all types of devices capable of connecting to a wired / wireless network. The user device (200) may include an obesity management application (210).

[0056] In one embodiment, the user device (200) can communicate with the server (100) through the obesity management application (210) to transmit and receive data. The user device (200) can transmit the user's health information to the server (100) through the obesity management application (210). The server (100) can provide the user device (200) with various sound contents including appetite reduction sound contents, cognitive behavioral therapy data, etc. through the obesity management application (210).

[0057] A network (300) according to one embodiment of the present disclosure may include any wired or wireless communication network capable of transmitting and receiving any type of data and signals.

[0058] FIG. 2 is a diagram illustrating an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] In one embodiment of the present disclosure, a set of neurons or nodes may be defined as a layer.

[0066] 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.

[0067] 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 decreases 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 increases 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 a combined form of the neural networks described above.

[0068] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using DNNs, one can identify latent structures in data. This includes images, text, videos, audio, protein sequence structures, gene sequence structures, peptide sequence structures, the latent structure of 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 peptides and MHC. DNNs can 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), and transformers. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

[0069] 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.

[0070] 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.

[0071] 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 neural network output (category) 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 improving efficiency. A lower learning rate can be used in the later stages of training to improve accuracy.

[0072] 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.

[0073] 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 storage unit in the present disclosure, executed by a processor, and transmitted and received by a communication unit.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] The data structure may include a neural network. The data structure including the neural network may be stored on a computer-readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network learning, etc. The data structure including the neural network may include any of the components disclosed above. That is, the data structure including the neural network may be configured to include all or any combination of the following: preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network learning, etc. In addition to the aforementioned components, the data structure including the neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include any form of data used or generated during 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] FIG. 3 is a flowchart illustrating a method for managing a user's obesity, performed by a server (100) according to one embodiment of the present disclosure. For convenience of explanation, the following description will exemplify a method for managing a single user's obesity by the server (100). However, the present invention is not limited thereto, and the server (100) can manage the obesity of multiple users using the method described below.

[0084] In step S310, the server (100) according to one embodiment may obtain time-series health information of the user. The health information in the present disclosure is information related to the health of the user in his / her daily life, and may include at least one of the user's activity information, emotional information, sleep information, meal information, or physical information. The activity information is information related to the user's physical and mental activities in his / her daily life, and may include information related to at least one of exercise, study, and work. The emotional information is information related to the user's emotional state in his / her daily life, and may include information related to angry states, happy states, sad states, stressed states, depressed states, etc. The meal information is information related to the user's meals in his / her daily life, and may include the type of food consumed by the user, the user's meal location, meal amount, and meal time duration. The physical information may include the user's height, weight, body fat percentage, BMI index, obesity level (e.g., obesity severity), age, gender, etc.

[0085] In one embodiment, the server (100) may obtain the user's time-series health information including time information from the user device (200). For example, the server (100) may obtain the user's time-series health information from the user device (200) at a preset interval.

[0086] In one embodiment, the server (100) may receive location information of the user device (200) from the user device (200). Based on the location information of the user device (200), the server (100) may obtain health information of the user. Based on the location where the user device (200) is located, the server (100) may determine the current status of the user, such as whether the user is exercising, eating, or resting, and obtain health information of the user.

[0087] In one embodiment, the server (100) can obtain sound information generated by the user from the user device (200). For example, the sound information may include the user's voice, breathing sounds, sounds generated by the user's movements, etc. The server (100) can obtain the user's health information based on the sound information.

[0088] In one embodiment, the server (100) may provide a questionnaire related to the user's health information to the user device (200). The server (100) may obtain the user's health information corresponding to the user's response to the questionnaire from the user device (200).

[0089] In one embodiment, the server (100) can obtain the user's health information from an external server, database, device, etc.

[0090] In step S320, the server (100) according to one embodiment may acquire the user's lifestyle pattern information from the user's health information using a pre-trained artificial intelligence-based lifestyle pattern analysis model. In one embodiment, the lifestyle pattern analysis model may correspond to a pre-trained artificial intelligence model that inputs the user's time-series health information and outputs lifestyle pattern information. In one embodiment, the user's lifestyle pattern information may refer to information related to the user's average lifestyle pattern in daily life. A detailed description of the lifestyle pattern analysis model and lifestyle pattern information will be described below with reference to FIG. 5.

[0091] In step S330, the server (100) according to one embodiment may provide a first appetite-reducing sound content to a user device (200) corresponding to the user based on the user's lifestyle pattern information. In addition, the server (100) may provide a plurality of appetite-reducing sound contents including the first appetite-reducing sound content to the user device (200). For example, the server (100) may determine the user's average food intake time period based on the user's lifestyle pattern information. The server (100) may provide the first appetite-reducing sound content to the user device (200) during the user's average food intake time period.

[0092] For example, the server (100) may transmit a notification regarding the first appetite-reducing sound content to the user device (200) based on the user's lifestyle pattern information. The server (100) may also transmit the first appetite-reducing sound content as recommended sound content to the user device (200) based on the user's lifestyle pattern information. The user device (200) may identify the first appetite-reducing sound content as recommended sound content. For another example, the server (100) may transmit a playback command to the user device (200) based on the user's lifestyle pattern information so that the first appetite-reducing sound content is played on the user device (200). Through this, the technical effect of providing the user with the appetite-reducing sound content at an appropriate time according to his / her lifestyle pattern in daily life can be achieved.

[0093] In one embodiment, the server (100) may obtain information regarding whether the user has consumed food. For example, the server (100) may obtain information regarding whether the user has consumed food based on at least one of the location information of the user device (200) and the user's sound information. For example, the server (100) may determine whether the user is in a place where the user consumes food, such as a restaurant, based on the location information of the user device (200). For example, the server (100) may obtain sound information related to the user's food consumption. In this case, the server (100) may transmit a questionnaire regarding whether the user has consumed food to the user device (200). Based on the information regarding whether the user has consumed food, the server (100) may provide the first appetite-reducing sound content to the user device (200) if the user consumes food during a time period other than the user's average food consumption time period. Alternatively, the server (100) may provide the first appetite-reducing sound content to the user device (200) if it is determined that the user is consuming food.

[0094] In one embodiment, the appetite-reducing sound content may refer to sound content including sounds effective in reducing appetite, generated based on clinical prior information. For example, the server (100) may determine the key, tempo, LUFS level, and timbre of the appetite-reducing sound content based on clinical prior information. The server (100) may determine the genre, rhythm, key, composition, tempo, instruments played, volume balance of instruments played, LUFS (Loudness Unit Relative to Full Scale) level, etc. of the appetite-reducing sound content based on clinical prior information.

[0095] In one embodiment, the server (100) may generate obesity management information corresponding to the user based on the user's body information and lifestyle pattern information. In one embodiment, the obesity management information may include at least one of recommended diet information or recommended exercise information corresponding to the user. The server (100) may transmit the obesity management information to the user device (200).

[0096] In one embodiment, the recommended diet information may include a recommended daily calorie intake. For example, the server (100) may determine a recommended daily calorie intake corresponding to the user based on at least one of the user's obesity level, age, or gender. The recommended daily calorie intake may be determined by reducing the user's average daily calorie intake by a certain amount. The amount of calories reduced from the average daily calorie intake may increase as the user's obesity level increases. In addition, the recommended diet information may include a recommended nutrient intake ratio and a recommended intake time zone. In addition, the recommended diet information may include recommended diet information for each intake time zone.

[0097] In one embodiment, the server (100) may calculate the digestion time period between the average time the user completes the last meal before going to sleep and the average sleep start time of the user based on the user's lifestyle pattern information. The server (100) may calculate the recommended calorie intake, recommended digestion time period, and recommended completion time of the last meal based on the user's body information and digestion time period. For example, the server (100) may determine the recommended calorie intake, recommended digestion time period, and recommended completion time of the last meal based on at least one of the user's obesity level, age, or gender. The user's server (100) may generate recommended diet information including the recommended calorie intake, recommended digestion time period, and recommended completion time of the last meal. Through this, the user may achieve the technical effect of obtaining recommended diet information that takes into account both his or her eating and sleeping patterns.

[0098] In one embodiment, the server (100) may calculate the user's daily average exercise information based on the user's lifestyle pattern information. The server (100) may determine the recommended daily calorie consumption, recommended daily exercise time period, and recommended daily exercise type based on the user's body information and the user's daily average exercise information. The server (100) may generate recommended exercise information including the recommended daily calorie consumption, recommended daily exercise time period, and recommended daily exercise type. For example, the server (100) may generate recommended exercise information corresponding to the user based on at least one of the user's obesity level, age, and gender. The recommended exercise information may further include a recommended exercise intensity. The recommended exercise intensity may be determined based on the ratio of the exercise heart rate to the maximum heart rate. For example, the recommended exercise intensity may be lowered as the user's obesity level increases.

[0099] In one embodiment, after providing the first appetite-reducing sound content, the server (100) may obtain playback information of the user's first appetite-reducing sound content and changed health information. For example, the server (100) may obtain log data related to access to and use of an obesity management application of the user device (200). The log data may include playback information of sound contents including the user's first appetite-reducing sound content, cognitive behavioral treatment information, health information, lifestyle pattern information, etc. In one embodiment, the server (100) may update the user's lifestyle pattern information from the changed health information using a lifestyle pattern analysis model.

[0100] In one embodiment, the server (100) can determine the user's compliance with the obesity management application based on the user's log data.

[0101] In one embodiment, the server (100) may generate health statistics data related to the user's time-series health information based on the user's time-series health information. The health statistics data may include changes in the user's time-series activity information, emotional information, sleep information, meal information, and physical information. For example, the health statistics data may include statistical data based on preset cycles, such as daily statistical data, weekly statistical data, monthly statistical data, quarterly statistical data, and semi-annual statistical data.

[0102] In one embodiment, the server (100) may provide the user device (200) with second appetite-reducing sound content that is different from the first appetite-reducing sound content in at least one of key, tempo, LUFS level, or timbre based on the playback information of the user's first appetite-reducing sound content and the changed health information. For example, if the number of times the user's first appetite-reducing sound content is played and the playback time period is greater than or equal to a preset number of times the user's first appetite-reducing sound content is played and the playback time period is greater than or equal to a preset number of times the user's first appetite-reducing sound content is played and the playback time period is greater than or equal to a preset target weight, the server (100) may determine that the first appetite-reducing sound content is not effective in reducing the user's weight. In this case, the server (100) may provide the second appetite-reducing sound content to the user device (200). For example, the server (100) may transmit a notification regarding the second appetite-reducing sound content to the user device (200). As another example, the server (100) may transmit the second appetite-reducing sound content as recommended sound content to the user device (200). The user device (200) may identify the second appetite-reducing sound content as recommended sound content. As another example, the server (100) may transmit a playback command to cause the second appetite-reducing sound content to be played on the user device (200) based on the user's lifestyle pattern information.

[0103] In one embodiment, the server (100) may group a plurality of users into a plurality of groups based on their health information. The server (100) may obtain group lifestyle pattern information for each of the plurality of groups. For example, the server (100) may obtain lifestyle pattern information for each of the plurality of users using a lifestyle pattern analysis model, and may obtain group lifestyle pattern information by averaging the lifestyle pattern information of each of the plurality of users included in each group. The plurality of servers (100) may transmit the first group lifestyle pattern information of the first group including the user to the user device (200). Through this, the user may obtain the technical effect of understanding the average lifestyle pattern information of the first group to which he or she belongs and obtaining supplementary points and motivation.

[0104] In one embodiment, the server (100) may obtain a food image including at least one food from a user device (200). The server (100) may calculate the calories contained in at least one food from the food image including at least one food using a pre-trained artificial intelligence-based calorie analysis model. The calorie analysis model may correspond to a pre-trained artificial intelligence model that determines the type and amount of food from the food image and calculates the calories contained in the food through learning food image data including the food image and the calories for the food. For example, the calorie analysis model may output the type of food, the amount of food (e.g., weight), and the calories contained in the food from the food image. In addition, the calorie analysis model may calculate the amount and / or ratio of nutrients (e.g., carbohydrates, proteins, fats) contained in the food in addition to the calories. The server (100) may obtain the user's meal information including the type of food, the amount of food, and the calories contained in the food from the food image using the calorie analysis model. The server (100) can transmit information about the type of food, the amount of food, and the calories contained in the food output from the calorie analysis model to the user device (200). Through this, the server (100) can achieve the technical effect of obtaining the user's meal information through food images of the food consumed by the user.

[0105] In one embodiment, the server (100) may obtain a time-series facial image of the user from the user device (200). The server (100) may use a pre-trained artificial intelligence-based facial area calculation model to calculate the area of ​​the user's face from the facial image. The facial area calculation model may correspond to a pre-trained artificial intelligence model that calculates the area of ​​the face from the facial image. The server (100) may use the facial area calculation model to obtain a time-series facial area from the time-series facial images and calculate changes in the user's facial area. The server (100) may transmit information about changes in the facial area to the user device (200). Through this, the server (100) may achieve the technical effect of being able to identify changes in the user's facial area (e.g., swelling) over time.

[0106] FIG. 4 is a block diagram illustrating a plurality of modules included in a server according to one embodiment of the present disclosure.

[0107] In one embodiment, the server (100) may include a sound content provision module (410), a lifestyle pattern analysis module (420), and a cognitive behavioral therapy module (430). The server (100) may perform a methodology for managing a user's obesity through each module. Each module may be driven by a processor (110) included in the server (100). That is, the operations performed by each module may have the same meaning as the operations performed by the server (100).

[0108] In one embodiment, the sound content provision module (410) can generate and / or provide various sound contents to the user device (200). For example, the sound content provision module (410) can generate and / or provide health management sound contents including appetite reduction contents, exercise stimulation sound contents, concentration enhancement sound contents, stress relief sound contents, depression reduction sound contents, sleep induction sound contents, etc. The sound contents provided by the sound content provision module (410) can be generated based on clinical prior information. In one embodiment, the sound contents can additionally include at least one of an image, video, or text corresponding to the sound in addition to the sound.

[0109] In one embodiment, the sound content provision module (410) may include a pre-trained artificial intelligence-based health management sound generation model. In one embodiment, the health management sound generation model may correspond to a pre-trained artificial intelligence model that determines the user's current status by inputting the user's lifestyle pattern information and generates health management sound content corresponding to the user's current status. Details regarding the health management sound generation model are described in Korean Patent No. 10-2486690 (registered on January 5, 2023), which is incorporated herein by reference. The health management sound generation model in the present disclosure may correspond to an artificial neural network that determines the genre, rhythm, tempo, instruments played, volume balance of the instruments played, and LUFS level corresponding to the user's activity status in the patent document. The server (100) may additionally acquire the user's brainwave data for identifying the user's current status as input data to be input into the health management sound generation model. The server (100) can input at least one of brain wave data or user's lifestyle pattern information into a health management sound generation model to generate health management sound content corresponding to the user's current condition.

[0110] In one embodiment, the health management sound generation model may correspond to an artificial intelligence model that is pre-trained to output appetite-reducing sound content using clinical prior information related to the key, tempo, LUFS level, etc. of music that is effective in reducing appetite as a learning dataset. For example, the health management sound generation model may generate appetite-reducing sound content that falls within the key range, tempo range, and LUFS range of music that has been clinically confirmed to be effective in reducing appetite. The server (100) may transmit at least one appetite-reducing sound content including the first appetite-reducing sound content generated by the health management sound generation model to the user device (200).

[0111] In one embodiment, the server (100) can identify a time point at which at least one of the user's activity state, emotional state, sleep state, or eating state changes based on the user's lifestyle pattern information. The server (100) can input the user's lifestyle pattern information into a health management sound generation model at preset cycles or status change times to generate health management sound content corresponding to the user's current status. The server (100) can then provide the health management sound content to the user's device.

[0112] In one embodiment, the lifestyle pattern analysis module (420) may include a lifestyle pattern analysis model. The lifestyle pattern analysis module (420) may generate lifestyle pattern information for the user from the user's health information. The lifestyle pattern analysis module (420) may transmit the user's lifestyle pattern information to the sound content provision module (410) and the cognitive behavioral therapy module (430).

[0113] In one embodiment, a Cognitive Behavioral Therapy (CBT) module (430) may transmit cognitive behavioral therapy data to a user device (200). Cognitive behavioral therapy in the present disclosure may refer to a treatment that changes a user's cognition so that the user's lifestyle pattern changes in a direction that meets preset health criteria. The health criteria may include criteria related to obesity, criteria related to emotions, criteria related to sleep, criteria related to meals, etc. In one embodiment, the cognitive behavioral therapy data may include various formats of data such as text, images, sounds, and videos related to the user's cognitive behavioral therapy.

[0114] In one embodiment, the server (100) may identify a state change time point at which at least one of the user's activity state, emotional state, sleep state, or eating state changes based on the user's lifestyle pattern information. The state change time point may include a first state change time point at which the first state itself changes, and a second state change time point at which the first state changes to a second state.

[0115] For example, a first state change time point may include, within an activity state, a time point at which a first activity state (e.g., a resting state) changes to a second activity state (e.g., an exercise state), within an emotional state, a time point at which a first emotional state (e.g., a stress state) changes to a second emotional state (e.g., a happy state), within a sleep state, a time point at which a first sleep state (e.g., a deep sleep state) changes to a second sleep state (e.g., a light sleep state), and within a eating state, a time point at which a first eating state (e.g., a start-of-eating state) changes to a second eating state (e.g., a mid-eating state).

[0116] For example, the second state change time point may include a time point changing from an active state to a sleep state, a time point changing from an active state to a eating state, etc.

[0117] In one embodiment, the server (100) may input the user's lifestyle pattern information into a pre-trained artificial intelligence-based cognitive behavioral therapy model at preset intervals or at the time of the user's status change to obtain cognitive behavioral therapy data corresponding to the user's current status. The cognitive behavioral therapy model in the present disclosure may correspond to an artificial intelligence model pre-trained to output cognitive behavioral therapy data from the user's lifestyle pattern information. For example, the cognitive behavioral therapy model may determine the user's current status based on the user's lifestyle pattern information and output cognitive behavioral therapy data corresponding to the user's current status. For example, the cognitive behavioral therapy data may be determined based on clinical prior information. The server (100) may transmit the cognitive behavioral therapy data to the user device (200).

[0118] In one embodiment, the cognitive behavioral therapy model may correspond to a pre-trained, AI-based, large-scale language model. The large-scale language model in the present disclosure may correspond to a pre-trained, AI-based model that inputs natural language-based instruction text and outputs a response text. For example, the large-scale language model may be pre-trained to predict the next word or token from a given sequence of words or sentences. Furthermore, the large-scale language model may be pre-trained to predict the correct word for a masked or altered word in a given sentence. In this case, the large-scale language model may include an encoder series, a decoder series, and an encoder-decoder series of pre-trained transformers. Examples of large-scale language models include, but are not limited to, Google's Gemini and OpenAI's Chat-GPT, and various other language models may be used.

[0119] In one embodiment, the server (100) may incorporate a chatbot based on a cognitive behavioral therapy model into an obesity management application. The server (100) may allow the user device (200) to receive user input through the chatbot. The server (100) may input input text corresponding to the user input into the cognitive behavioral therapy model to obtain cognitive behavioral therapy data as response text. The server (100) may provide cognitive behavioral therapy data to the user device (200) through the chatbot.

[0120] In one embodiment, the cognitive behavioral therapy data may include information on recommended sound content corresponding to the user's current state. For example, the cognitive behavioral therapy model may correspond to an artificial intelligence-based large-scale language model that is fine-tuned to output information on recommended sound content corresponding to the user's current state based on the user's lifestyle pattern information. The server (100) may fine-tune the cognitive behavioral therapy model using a learning dataset containing recommended sound content mapped to each user's state. Alternatively, the server (100) may obtain a fine-tuned cognitive behavioral therapy model using a learning dataset containing recommended sound content mapped to each user's state through an external device.

[0121] In one embodiment, the cognitive behavioral therapy model can be mapped to a pre-trained, AI-based, multimodal, large-scale language model. For example, the cognitive behavioral therapy model can input information about a user's lifestyle patterns and output cognitive behavioral therapy data in various formats, such as text, images, audio, and video, that correspond to the user's current condition.

[0122] In one embodiment, the cognitive behavioral therapy model may include a pre-trained AI-based generative model. The generative model in the present disclosure may correspond to a pre-trained AI model that receives an input prompt as input and generates data corresponding to the input prompt. For example, the generative model may include, but is not limited to, a Variational Autoencoder (VAE) model, a Generative Adversarial Network (GAN) model, and the like, and various generative models may be used.

[0123] In one embodiment, the server (100) may generate an input prompt including instruction text that commands the output of cognitive behavioral therapy data corresponding to the user's current state as well as information about the user's lifestyle pattern. For example, the instruction text may include instructions such as "output cognitive behavioral therapy phrases helpful to the user's current state," "play cognitive behavioral therapy music helpful to the user's current state," or "generate cognitive behavioral therapy images helpful to the user's current state." The server (100) may input the input prompt into a cognitive behavioral therapy model to obtain cognitive behavioral therapy data.

[0124] For example, if the user's current state is an exercise state based on the user's lifestyle pattern information, the cognitive behavioral therapy model can output cognitive behavioral therapy data that stimulates the user to exercise. For example, if the user's current state is a resting state, the cognitive behavioral therapy model can output cognitive behavioral therapy data that helps the user relax. For example, if the user's current state is a resting state, the cognitive behavioral therapy model can output cognitive behavioral therapy data that helps the user relax. For example, if the user's current state is an obese state, the cognitive behavioral therapy model can output cognitive behavioral therapy data that encourages the user to exercise or reduces their appetite. For example, if the user's current state is a depressed state, angry state, or stressed state, the cognitive behavioral therapy model can output cognitive behavioral therapy data that alleviates the user's emotions.

[0125] FIG. 5 is a diagram illustrating a process of obtaining user lifestyle pattern information using a lifestyle pattern analysis model according to one embodiment of the present disclosure.

[0126] In one embodiment, the lifestyle pattern analysis model (520) may take as input the user's time-series health information (510) and output lifestyle pattern information (530) related to the user's average lifestyle pattern in daily life. For example, the lifestyle pattern analysis model (520) may take as input at least one of activity information, emotional information, sleep information, or meal information included in the user's health information (510) and output the user's lifestyle pattern information (530). For example, the lifestyle pattern information (530) may include a daily average lifestyle pattern, a weekly average lifestyle pattern, a monthly average lifestyle pattern, an hourly average lifestyle pattern, etc. In the following, for the convenience of explanation, a case in which the lifestyle pattern information corresponds to the daily average lifestyle pattern will be described as an example. For example, the lifestyle pattern analysis model (520) may include a pre-trained artificial intelligence-based prediction model that predicts the next time-series data from the previous time-series data. The lifestyle pattern analysis model (520) may include a linear regression model, RNN, LSTM (Long Short-Term Memory), etc., but is not limited thereto and various artificial intelligence models may be used.

[0127] In one embodiment, the user's lifestyle pattern information (530) may include at least one of the user's activity pattern information (532), emotion pattern information (534), sleep pattern information (536), or meal pattern information (538). For example, the activity pattern information (532) may include information related to the user's activity pattern, such as the user's average exercise time zone, average rest time zone, average study time zone, and average work time zone. In addition, the activity pattern information (532) may include detailed activity information for each activity. For example, the emotion pattern information (534) may include information related to the user's emotional changes during the day. For example, the sleep pattern information (536) may include information related to the user's sleep pattern, such as the user's average sleep start time, average sleep duration, average sleep end time, average deep sleep time zone, and average light sleep (REM sleep) time zone. For example, the meal pattern information (538) may include information related to the user's meal pattern, such as the user's average meal time zone, meal location, and average calorie intake.

[0128] In one embodiment, the server (100) may identify a target time period during which the probability of the user consuming food is greater than or equal to a preset probability value based on the user's eating pattern information (538). For example, the eating pattern information (538) may include the user's food consumption probability value for each time period. Alternatively, the eating pattern information (538) may include the target time period itself. The target time period may include the user's average food consumption time period. The server (100) may provide the first appetite-reducing sound content to the user device (200) during the target time period.

[0129] In one embodiment, the server (100) may provide at least one of exercise stimulation sound content or concentration enhancement sound content to the user device (200) based on the user's activity pattern information (532). For example, the server (100) may predict that the user's current activity state is an exercise state based on the activity pattern information (532) and transmit a notification or a playback command regarding the exercise stimulation sound content to the user device (200). In another example, the server (100) may predict that the user's current activity state is a study state based on the activity pattern information (532) and transmit a notification or a playback command regarding the concentration enhancement sound content to the user device (200).

[0130] In one embodiment, the server (100) may provide at least one of stress-relieving sound content or depression-reducing sound content to the user device (200) based on the user's emotional pattern information (534). For example, the server (100) may predict that the user's current emotional state is a stress state based on the user's emotional pattern information (534) and transmit a notification or a playback command regarding the stress-relieving sound content to the user device (200). In another example, the server (100) may predict that the user's current emotional state is a depression state based on the user's emotional pattern information (534) and transmit a notification or a playback command regarding the depression-reducing sound content to the user device (200).

[0131] In one embodiment, the server (100) may provide sleep-inducing sound content to the user device (200) based on the user's sleep pattern information (536). For example, the server (100) may predict that the user is currently in a sleeping state based on the user's sleep pattern information (536) and transmit a playback command regarding the sleep-inducing sound content to the user device (200). In another example, the server (100) may predict that the user is currently in a light sleep state based on the user's sleep pattern information (536) and transmit a playback command regarding the sleep-inducing sound content to the user device (200).

[0132] FIG. 6 is a diagram illustrating an example of a user interface related to appetite reduction sound content according to one embodiment of the present disclosure, and FIG. 7 is a diagram illustrating an example of a user interface for obtaining health information of a user according to one embodiment of the present disclosure.

[0133] In one embodiment, the first appetite reduction sound content may be provided through an obesity management application running on a user device (200). For example, the server (100) may provide the obesity management application to the user device (200). The server (100) may transmit various sound contents, cognitive behavioral therapy data, etc. to the user device (200) through the obesity management application. The obesity management application may include a user interface including a first screen displaying information about the first appetite reduction sound content and a second screen including an input object for the user's health information.

[0134] FIG. 6 is a diagram illustrating an example of a first screen included in a user interface. In one embodiment, the first screen may include a first area (610) for displaying detailed information about a first appetite-reducing sound content and a second area (620) for displaying effect information related to the first appetite-reducing sound content. For example, the detailed information may include information about the key, tempo, and LUFS level of the first appetite-reducing sound content. For example, the effect information may include the target effect content of the first appetite-reducing sound, information about medical staff who participated in the effectiveness verification, and effective usage instructions.

[0135] FIG. 7 is a diagram illustrating an example of a second screen included in a user interface. In one embodiment, the second screen may include a third area (710) including a first input object for receiving input of a user's current emotional state, a fourth area (720) including a second input object for receiving input of the user's emotional information, and a fifth area (730) including a third input object for receiving input of the user's meal information. However, this is merely an example, and the user interface may further include input objects, areas, or screens for receiving input of the user's health information, such as activity information, sleep information, and body information, in addition to emotional information and meal information.

[0136] In one embodiment, the server (100) may provide a questionnaire to the user device (200) to obtain the user's health information. The server (100) may obtain device information corresponding to the user device (200) from the user device (200). If the device information indicates a mobile device, the server (100) may determine to provide the first questionnaire to the user device (200). Conversely, if the device information indicates a laptop device or a desktop device, the server (100) may determine to provide the second questionnaire to the user device (200). For example, the first questionnaire may include simple questions requiring short answer or multiple choice answers. The second questionnaire may include detailed questions requiring descriptive answers. The number of questionnaires included in the first questionnaire may be less than the number of questionnaires included in the second questionnaire. The server (100) may obtain the user's health information through the first questionnaire or the second questionnaire. Through this, the server (100) can achieve the technical effect of providing a questionnaire optimized for the form of the user device (200).

[0137] FIG. 8 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0138] Although the present disclosure has been described above as being generally implemented by a server (100), those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions or other program modules that can be executed on at least one computer, or as a combination of hardware and software.

[0139] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular 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 can be operatively connected to at least one associated device.

[0140] 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 connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0141] 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.

[0142] 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 at least one 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, and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable transmission media.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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 skilled in the art will appreciate that other types of media readable by a computer, 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.

[0147] A plurality of program modules, including an operating system (1130), at least one application program (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, 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.

[0148] A user may enter commands and information into the computer (1102) via at least one wired / wireless input device, 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.

[0149] 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.

[0150] The computer (1102) may operate in a networked environment using logical connections to at least one remote computer, such as remote computer(s) (1148) via wired 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) 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.

[0151] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired 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.

[0152] The computer (1102) communicates with any wireless device or object that is configured and operates via wireless communication, such as a printer, scanner, desktop or portable computer, portable data assistant (PDA), 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 three devices.

[0153] 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).

[0154] 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, data, instructions, commands, information, signals, values, 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.

[0155] 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.

[0156] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming 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, the various storage media presented herein include at least one device or other machine-readable medium for storing information.

[0157] 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.

[0158] 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.

Claims

1. A method for managing a user's obesity, performed by a server, A step of obtaining time-series health information of the user; A step of obtaining the user's lifestyle pattern information from the health information using a pre-learned artificial intelligence-based lifestyle pattern analysis model; and A step of providing first appetite reduction sound content to a user device corresponding to the user based on the above lifestyle pattern information; including, method.

2. In paragraph 1, The health information includes at least one of the user's activity information, emotional information, sleep information, meal information, or body information, and The above meal information includes the type of food consumed by the user, the user's meal location, meal amount, and meal time duration. method.

3. In paragraph 1, The above health information includes the user's physical information, and The above method, A step of generating obesity management information corresponding to the user based on the above body information and the above lifestyle pattern information; and A step of transmitting the obesity management information to the user device; including more, method.

4. In paragraph 3, The above obesity management information includes recommended diet information corresponding to the user, and The steps for generating the above obesity management information are: Based on the above lifestyle pattern information, a step of calculating a digestion time period between the average time the user completes the last meal before sleeping and the average sleep start time of the user; A step of calculating the recommended calorie intake, recommended digestion time period, and recommended completion time of the last meal based on the above body information and the digestion time period; and A step of generating the recommended diet information including the recommended calorie intake, the recommended digestion time period, and the recommended completion time; including, method.

5. In paragraph 3, The above obesity management information includes recommended exercise information corresponding to the user, and The steps for generating the above obesity management information are: A step of calculating the user's daily average exercise information based on the above lifestyle pattern information; A step of determining a daily recommended calorie consumption, a daily recommended exercise time period, and a daily recommended exercise type based on the above body information and the daily average exercise information; and A step of generating the recommended exercise information including the daily recommended calorie consumption, the daily recommended exercise time period, and the daily recommended exercise type; including, method.

6. In paragraph 1, The above lifestyle pattern information includes the user's meal pattern information, and The step of providing the first appetite-reducing sound content is: A step of identifying a target time period in which the probability of the user consuming food is greater than a preset probability value based on the above meal pattern information; and providing the first appetite reducing sound content to the user device during the target time period; including, method.

7. In paragraph 1, The above lifestyle pattern information includes at least one of the user's activity pattern information, emotional pattern information, sleep pattern information, or meal pattern information, and The above method, A step of providing at least one of exercise stimulation sound content or concentration enhancement sound content to the user device based on the above activity pattern information; A step of providing at least one of stress relief sound content or depression reduction sound content to the user device based on the emotional pattern information; or A step of providing sleep-inducing sound content to the user device based on the above sleep pattern information; including at least one more of, method.

8. In paragraph 1, A step of identifying a state change time point at which at least one of the user's activity state, emotional state, sleep state, or eating state changes based on the above lifestyle pattern information; A step of inputting the user's lifestyle pattern information into a pre-learned artificial intelligence-based cognitive behavioral therapy (CBT) model at a preset cycle or at the time of the status change to obtain cognitive behavioral therapy data corresponding to the user's current status; and A step of transmitting the cognitive behavioral therapy data to the user device; including more, method.

9. In paragraph 8, The above cognitive behavioral therapy data includes information on recommended sound content corresponding to the user's current state, and The above cognitive behavioral therapy model corresponds to an artificial intelligence-based large-scale language model that is fine-tuned to output information on recommended sound content corresponding to the user's current state from the user's lifestyle pattern information. method.

10. In paragraph 1, After the step of providing the first appetite-reducing sound content, A step of obtaining playback information and changed health information of the first appetite reduction sound content of the user; and A step of providing a second appetite-reducing sound content, wherein at least one of a key, tempo, LUFS level, or tone is different from the first appetite-reducing sound content, to the user device based on the above reproduction information and the above changed health information; including more, method.

11. In paragraph 1, A step of identifying a state change time point at which at least one of the user's activity state, emotional state, sleep state, or eating state changes based on the above lifestyle pattern information; A step of inputting the user's lifestyle pattern information into a pre-learned artificial intelligence-based health management sound generation model at a preset cycle or at the time of the status change to generate health management sound content corresponding to the user's current status; and A step of providing the health care sound content to the user device; including more, method.

12. In paragraph 1, A step of grouping multiple users into multiple groups based on health information of multiple users; A step of obtaining group life pattern information of each of the above multiple groups; and A step of transmitting first group lifestyle pattern information of a first group including the user to the user device; including more, method.

13. In paragraph 1, The above first appetite reduction sound content is provided through an obesity management application running on the user device, and The above obesity management application is, A user interface including a first screen displaying information about the first appetite reduction sound content and a second screen including a plurality of input objects for the user's health information, method.

14. In a server for managing user obesity, at least one processor; and memory; Includes, At least one processor, Obtain time-series health information of the above user, By using a pre-learned artificial intelligence-based lifestyle pattern analysis model, the user's lifestyle pattern information is obtained from the health information, and Based on the above lifestyle pattern information, providing a first appetite reduction sound content to a user device corresponding to the user. Server.

15. A computer program stored in a computer-readable storage medium, wherein when the computer program is executed by one or more processors, the computer program causes the one or more processors to perform methods for managing obesity of a user, the methods comprising: A step of obtaining time-series health information of the user; A step of obtaining the user's lifestyle pattern information from the health information using a pre-learned artificial intelligence-based lifestyle pattern analysis model; and A step of providing first appetite reduction sound content to a user device corresponding to the user based on the above lifestyle pattern information; including, A computer program stored on a computer-readable storage medium.

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