Method and apparatus for recording emotion based on multi-axis emotion preset

Abstract

A method for recording emotions based on a multi-axis emotion preset according to various embodiments of the present invention is disclosed. The method may include: receiving emotion selection information as a log is generated at a user terminal; providing preset values ​​for a plurality of emotion axes corresponding to the emotion selection information to the user terminal; generating multi-axis emotion information including values ​​of emotion axes changed according to the adjustment input when an adjustment input for the preset values ​​is received; and mapping and storing the multi-axis emotion information in the log.

Description

Technology Field

[0001] The present invention relates to a multi-axis emotion preset-based emotion recording method and apparatus. Background Technology

[0003] With the rapid increase in interest in mental health management and self-objectification in modern society, life-log services and mental health care applications that promote psychological stability by recording daily life and tracking emotional states are being widely utilized. These services help users monitor their psychological state and manage stress by consistently recording their daily moods and emotions; furthermore, their scope of application is expanding into various fields, such as providing psychological counseling or AI-based personalized content based on accumulated data. Generally, this emotion recording technology is implemented by allowing users to easily input and save the emotions they felt at a specific point in time through devices such as smartphones or wearable devices.

[0004] Conventional emotion recording services primarily rely on users selecting predefined, standardized emotion labels or emoticons, such as 'joy,' 'sadness,' and 'surprise.' While this approach offers the advantage of allowing users to input emotions intuitively and quickly, it has been pointed out as having limitations in that it is difficult to fully reflect the subtle nuances or specific contexts of complex and multi-layered human emotions by simply classifying them into fixed categories. For example, assuming a user experiences the same emotion of 'surprise' multiple times a day, even though some moments might be pleasant surprises while others might be unpleasant or highly tense, existing technologies mostly treat this as a repetitive input of the same emotion category without distinction. In other words, there have been technical limitations in digitizing and recording data regarding the fact that the intensity or detailed attributes of an emotion can vary depending on the situation, even if the emotion is labeled the same.

[0005] Furthermore, existing methods for analyzing and reporting emotion data are limited to quantitative statistics centered on simple frequency, which aggregate how many times a particular emotion was selected during a specific period. With only such simple statistical information, it is difficult for users to gain in-depth insights into the specific tendencies of the emotions they have recorded, or into their unique emotional characteristics and definitions that distinguish them from others. Considering that the purpose of emotion recording goes beyond simple history storage to understand users' emotional patterns and aid in self-awareness, standardized input methods and fragmented statistical provision methods have had certain limitations in expanding the user experience.

[0006] Therefore, there is a demand in the industry for a method of recording emotions for personalized emotion definition and analysis. In this regard, Korean Published Patent No. 10-2023-0085481 discloses an emotion recording application. The problem to be solved

[0008] The technical problem that the present invention aims to solve is to provide a multi-axis emotion preset-based emotion recording method and apparatus.

[0009] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0011] According to an embodiment of the present invention for solving the problem described above, a method for recording emotions based on a multi-axis emotion preset is disclosed. The method may include: receiving emotion selection information as a log is generated at a user terminal; providing preset values ​​for a plurality of emotion axes corresponding to the emotion selection information to the user terminal; generating multi-axis emotion information including values ​​of emotion axes changed according to the adjustment input when an adjustment input for the preset values ​​is received; and mapping and storing the multi-axis emotion information in the log.

[0012] In an alternative embodiment, the method may further include: a step of calculating a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to the specific emotion when a plurality of logs for which a specific emotion is selected are stored; and a step of generating personalized emotion information for the specific emotion based on the representative value for each axis and providing it to the user terminal.

[0013] In an alternative embodiment, the step of calculating a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to the specific emotion includes the step of determining the representative value for each axis by accumulating the adjustment values ​​of each emotion axis included in the plurality of adjustment inputs and calculating at least one of the average value, median value, and mode for each axis; and the step of generating personalized emotion information for the specific emotion based on the representative value for each axis and providing it to the user terminal may include the step of generating at least one of the user's tendency intensity and change trend for the specific emotion as the personalized emotion information based on the deviation between the representative value for each axis and the preset value.

[0014] In an alternative embodiment, the plurality of emotion axes may be characterized by being set to a different number depending on the emotion item, and the preset values ​​for the plurality of emotion axes may be characterized by being reset to preset initial values ​​whenever a log is generated.

[0015] In an alternative embodiment, the method may further include: a step of calculating a statistical representative value for each axis based on a plurality of multi-axis emotion information generated by a plurality of user terminals; and a step of updating a preset value set for a specific emotion corresponding to the plurality of multi-axis emotion information based on the statistical representative value for each axis.

[0016] In an alternative embodiment, the method further comprises: receiving a new emotion generation request from the user terminal, the request including an emotion name, an emotion image, one or more emotion axes, and preset values ​​for each of the one or more emotion axes; generating a user-defined emotion according to the new emotion generation request; and adding the user-defined emotion to an emotion item associated with the emotion selection information so that the user-defined emotion can be selected when generating the log; wherein the user-defined emotion may be characterized as being shareable with other user terminals.

[0017] In an alternative embodiment, the method may further include: receiving an N-axis value input including at least one emotion axis value from the user terminal prior to receiving the emotion selection information; calculating a similarity between the N-axis value input and a plurality of previously stored emotion item preset values; and providing one or more emotion items to the user terminal in order of highest calculated similarity.

[0018] In an alternative embodiment, the method may further include: a step of calculating a deviation between the values ​​of a plurality of emotion axes included in the multi-axis emotion information; a step of classifying the multi-axis emotion information into a specific emotion pattern when the deviation is greater than or equal to a preset threshold; and a step of mapping and storing specific emotion information corresponding to the specific emotion pattern into the log.

[0019] According to one embodiment of the present invention for solving the above-described problem, an apparatus is disclosed. The apparatus comprises: a memory for storing one or more instructions; and a processor for executing the one or more instructions stored in the memory, and the processor can perform the above-described method by executing the one or more instructions.

[0020] According to one embodiment of the present invention for solving the above-described problem, a computer-readable recording medium is disclosed that stores a computer program for executing the above-described method using a computer.

[0021] Other specific details of the present invention are included in the detailed description and drawings. Effects of the invention

[0023] The present invention provides an emotion preset composed of multiple axes to facilitate the convenience of emotion input, while also supporting independent variable input for each log. This allows for the accurate datafication of subtle differences (texture, intensity, etc.) in emotions that change at every moment, even with the same emotion name, and enables the provision of emotion definitions and tendency information specialized for individual users through statistical analysis.

[0024] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0026] FIG. 1 is a drawing illustrating a system according to one embodiment of the present invention. FIG. 2 is a hardware configuration diagram of a computing device according to one embodiment of the present invention. FIGS. 3 to 8 are drawings for explaining a multi-axis emotion preset-based emotion recording method according to an embodiment of the present invention. Specific details for implementing the invention

[0027] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments presented below, but can be implemented in various different forms and should be understood to include all modifications, equivalents, and substitutions that fall within the spirit and scope of the present disclosure. The embodiments presented below are provided to make the present disclosure complete and to fully inform those skilled in the art of the scope of the invention. In describing the present disclosure, detailed descriptions of related prior art are omitted where it is determined that such detailed descriptions may obscure the essence of the present invention.

[0028] The terms used herein are used merely to describe specific embodiments and are not intended to limit the disclosure. Unless otherwise defined, all terms used herein have the same meaning as generally understood by those skilled in the art to which this disclosure pertains.

[0029] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, terms such as "comprising" or "having" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0030] Additionally, terms including ordinal numbers, such as "first" or "second" as used herein, may be used to describe various components, but the components should not be limited by the terms. The terms are used solely for the purpose of distinguishing one component from another.

[0031] Phrases such as "in one embodiment," "according to one embodiment," "related to one embodiment," or "according to an implementation of one embodiment" in this specification do not necessarily refer to the same embodiment. Furthermore, throughout this specification, "examples" are arbitrary distinctions to facilitate the description of the present disclosure, and each embodiment does not need to be mutually exclusive. For example, configurations mentioned for the description of one embodiment may be applied and / or implemented in other embodiments, and may be modified and applied and / or implemented to the extent that they do not depart from the scope of the present disclosure.

[0032] Some embodiments of the present disclosure may be represented by functional block configurations and various processing steps. Some or all of these functional blocks may be implemented by various numbers of hardware and / or software configurations that perform specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors or by circuit configurations for a specific function.

[0033] Additionally, for example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented as algorithms executed on one or more processors. Furthermore, the present disclosure may employ prior art for electronic configuration, signal processing, and / or data processing, etc. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations. Additionally, terms such as "-part," "-module," etc. refer to a unit that processes at least one function or operation, which may be implemented in hardware or software, or as a combination of hardware and software.

[0034] Furthermore, the connecting lines or connecting members between the components depicted in the drawings are merely illustrative of functional connections and / or physical or circuit connections. In the actual device, connections between components may be represented by various alternative or added functional connections, physical connections, or circuit connections.

[0035] In addition, some components in the drawings may be depicted with their size or proportions slightly exaggerated. Also, components depicted in one drawing may not be depicted in another drawing.

[0036] The present disclosure will be described in detail below with reference to the attached drawings.

[0038] FIG. 1 is a drawing illustrating a system according to one embodiment of the present invention.

[0039] Referring to FIG. 1, a system according to one embodiment of the present invention may include a computing device (100), a user terminal (200), and an external server (300). The system illustrated in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment illustrated in FIG. 1 and may be added, changed, or deleted as needed.

[0040] In one embodiment, the computing device (100) may be a server or an application processor (AP) operating within a user terminal (200) as the entity that provides the emotion recording method according to the present invention, namely a multi-axis emotion preset, processes variable inputs per log, and performs statistical analysis.

[0041] In one embodiment, the computing device (100) may provide a multi-axis emotion preset-based emotion recording function. For example, the computing device (100) may define emotions as a multi-dimensional data structure having multiple axes rather than simple text labels, and may provide an interface that allows the user to record by finely adjusting the values ​​of each axis.

[0042] Specifically, the computing device (100) can receive emotion selection information as the user terminal (200) generates a log. Here, the log refers to a diary, memo, or status information recorded by the user at a specific point in time, and may include timestamp and location information. Additionally, the emotion selection information may refer to an input signal in which the user selects one of predefined emotion categories, such as 'joy', 'sadness', or 'surprise'.

[0043] Additionally, the computing device (100) may provide preset values ​​for a plurality of emotion axes corresponding to emotion selection information to a user terminal (200). Here, the plurality of emotion axes refer to detailed attributes (e.g., tension, mood, fun, energy, etc.) that constitute the corresponding emotion, and the preset values ​​may refer to initial reference values ​​that are typically or basically set for each emotion axis.

[0044] Additionally, when the computing device (100) receives an adjustment input for a preset value, it may generate multi-axis emotional information including the values ​​of the emotional axes changed according to the adjustment input. Here, the adjustment input may refer to an operation in which a user increases or decreases the preset value (i.e., the reference value) to match their current emotional state through a slider or touch input. Additionally, the multi-axis emotional information refers to the final numerical data of the emotional axes determined for the corresponding log.

[0045] And, the computing device (100) can map multi-axis emotional information to a log and store it.

[0046] Accordingly, the computing device (100) of the present invention can accurately record and accumulate quantitative data on the intensity, texture, or subtle nuance differences of emotions that change from log (moment) to moment, even if the same emotion name is selected.

[0047] In various embodiments, the computing device (100) can statistically analyze accumulated emotion data to derive a definition of emotion specialized for an individual user.

[0048] Specifically, when a computing device (100) stores a plurality of logs in which a specific emotion is selected, it can calculate a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to a specific emotion. Here, the representative value for each axis may refer to a statistical indicator such as the mean, median, mode, or variance of the corresponding emotion axis values ​​accumulated over a specific period.

[0049] Additionally, the computing device (100) can generate personalized emotion information for a specific emotion based on representative values ​​for each axis and provide it to a user terminal (200). Here, the personalized emotion information may include text-based insights such as "The user's 'sadness' mainly shows a tendency to be high energy," or visual information displayed by overlaying a basic preset graph and a user average graph.

[0050] Accordingly, the computing device (100) of the present invention can go beyond simply providing the frequency of emotional occurrence and provide objective indicators and personalized definitions of how the user feels and defines their emotions differently from others.

[0051] In various embodiments, the computing device (100) may provide Web or Application-based services. However, it is not limited thereto.

[0052] The computing device (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, and a device controller. However, it is not limited thereto.

[0053] Hereinafter, the hardware configuration of the computing device (100) will be described with reference to FIG. 2.

[0054] Meanwhile, the user terminal (200) may be connected to the computing device (100) via a network (400) and may be a user terminal that uses the multi-axis emotion preset-based emotion recording service provided by the computing device (100). For example, the user terminal (200) may include a user terminal that generates daily emotion logs, fine-tunes emotion axis values ​​based on preset values, or checks personalized emotion definitions and analysis results.

[0055] Here, the user terminal (200) may include, for example, various types of computer devices. More specifically, for example, the user terminal (200) may refer to various terminal devices such as smartphones, tablet PCs, desktops, and laptops.

[0056] The user terminal (200) includes a display on at least a part of the terminal and may include an operating system for running applications or extension-based services provided by the computing device (100). For example, the user terminal (200) may be a smartphone, but is not limited thereto, and the user terminal (200) may include all kinds of handheld-based wireless communication devices such as navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartpad, tablet PC, etc., as wireless communication devices that ensure portability and mobility.

[0057] An external server (300) can be connected to a computing device (100) through a network (400), and can transmit and receive various information / data necessary for the computing device (100) to provide various functions, technologies, or services related to the method of the present invention, and can store and manage various information / data generated as the computing device (100) provides various functions, technologies, or services related to the method of the present invention.

[0058] For example, the external server (300) may be a database server that stores information used in various functions, technologies, or services related to the method of the present invention. As another example, the external server (300) may be a server that provides information used in various functions, technologies, or services related to the method of the present invention.

[0059] In one embodiment, the external server (300) may be a database server that stores multiple user data, or an API server for linking with other services (e.g., healthcare platform, SNS, etc.).

[0060] The network (400) may refer to a connection structure capable of exchanging information between each node, such as computing devices, multiple terminals, and servers. For example, the network (400) includes a Local Area Network (LAN), a Wide Area Network (WAN), the World Wide Web (WWW), a wired and wireless data network, a telephone network, a wired and wireless television network, etc.

[0061] Wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, NFC (Near-Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc.

[0063] FIG. 2 is a hardware configuration diagram of a computing device according to one embodiment of the present invention.

[0064] Referring to FIG. 2, a computing device (100) according to one embodiment of the present invention may include one or more processors (110), a memory (120) for loading a computer program (151) executed by the processor (110), a bus (130), a communication interface (140), and a storage (150) for storing the computer program (151). Here, FIG. 2 illustrates only the components related to the embodiment of the present invention. Therefore, a person skilled in the art to which the present invention pertains will understand that other general-purpose components may be included in addition to the components illustrated in FIG. 2.

[0065] The processor (110) controls the overall operation of each component of the computing device (100). The processor (110) may be composed of one or more cores and may include processors for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU) of the computing device. Alternatively, it may be configured to include any type of processor well known in the art of the present invention.

[0066] Additionally, the processor (110) can perform operations for at least one application or program for executing the method according to embodiments of the present invention, and the computing device (100) may have one or more processors.

[0067] In various embodiments, the processor (110) may further include RAM (Random Access Memory, not shown) and ROM (Read-Only Memory, not shown) for temporarily and / or permanently storing signals (or data) processed within the processor (110). Additionally, the processor (110) may be implemented in the form of a system-on-chip (SoC) comprising at least one of a graphics processing unit, RAM, and ROM.

[0068] Memory (120) stores various data, instructions and / or information. Memory (120) may load a computer program (151) from storage (150) to execute a method / operation according to various embodiments of the present invention. When the computer program (151) is loaded into memory (120), the processor (110) may perform the method / operation by executing one or more instructions constituting the computer program (151). Memory (120) may be implemented as volatile memory such as RAM, but the technical scope of the present invention is not limited thereto.

[0069] The bus (130) provides communication functions between components of the computing device (100). The bus (130) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0070] The communication interface (140) supports wired and wireless internet communication of the computing device (100). Additionally, the communication interface (140) may support various communication methods other than internet communication. To this end, the communication interface (140) may be configured to include a communication module well known in the art of the present invention. In some embodiments, the communication interface (140) may be omitted.

[0071] Storage (150) can store a computer program (151) non-temporarily. When performing a process according to an embodiment of the present invention through a computing device (100), storage (150) can store various information necessary to perform a method according to the disclosed embodiment or to provide a service.

[0072] The storage (150) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0073] A computer program (151) may include one or more instructions that cause a processor (110) to perform a method / operation according to various embodiments of the present invention when loaded into memory (120). That is, the processor (110) may perform the method / operation according to various embodiments of the present invention by executing the one or more instructions.

[0074] In one embodiment, the computer program (151) may include one or more instructions to perform various methods related to various tasks related to learning a neural network model.

[0075] The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0076] The components of the present invention may be implemented as a program (or application) and stored on a medium to be executed in combination with a computer, which is hardware. The components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors.

[0078] FIGS. 3 to 8 are drawings for explaining a multi-axis emotion preset-based emotion recording method according to an embodiment of the present invention.

[0079] Referring to FIG. 3, the computing device (100) can receive emotion selection information as it generates a log from the user terminal (200) (S110).

[0080] Specifically, the computing device (100) may receive emotion selection information including a unique identifier (ID) capable of identifying an emotion item when a specific emotion item is selected by touch input or the like from among a plurality of emotion icons or lists displayed on the display of the user terminal (200). Here, the emotion selection information may be data indicating a predefined static label such as joy, sadness, surprise, etc., and in some cases, may be data indicating a custom emotion item created by the user by directly defining its name and attributes. Additionally, time information (Timestamp) or location information (Location Data) at the time of log generation may be received together with the emotion selection information and may be linked to a log identifier that defines the context in which the emotion occurred.

[0081] For example, the computing device (100) may receive an emotion ID corresponding to an emotion item called 'surprise', or may receive a selection input for a user-defined emotion if the user has created and registered a new emotion that is not in an existing category, such as 'bittersweet'. In this case, the computing device (100) may not simply receive the name (Text) of the emotion, but may receive index information to identify which attribute data (such as presets described later) the emotion item is linked to in the database.

[0083] In one embodiment, the computing device (100) can provide preset values ​​for a plurality of emotion axes corresponding to emotion selection information to a user terminal (200) (S120).

[0084] Specifically, the computing device (100) may refer to a previously stored database to extract Emotion Preset data mapped to received Emotion Selection Information (e.g., Emotion ID) and transmit it to a user terminal (200). Here, the Emotion Preset is a data set pre-configured to quantitatively define a specific emotion, and may include a list of N Emotion Axis constituting the emotion and a preset value which is a default value assigned to each axis.

[0085] More specifically, the computing device (100) can render UI objects, such as sliders or gauges, for each of the N emotion axes on the display of the user terminal (200) and provide a control signal to position the initial position of each UI object at the preset value.

[0086] For example, when a user selects 'Surprise', the computing device (100) can call preset data containing three emotion axis information, such as 'Tension', 'Mood', and 'Fun', which are predefined in the emotion item 'Surprise', and default values ​​for each axis (e.g., Tension=80, Mood=50, Fun=50), and transmit it to the user terminal (200). Through this, the user terminal (200) can display the three emotion axis UIs set to the default values ​​on the screen in an initial state where the user has not made any separate input.

[0088] In various embodiments, multiple emotion axes may be set to different numbers depending on the emotion item. That is, the number of axes (N) defining each emotion in the present invention is not fixed, but can be defined variably depending on the complexity or nature of the emotion. Through this, the computing device (100) can provide an optimal dimension that best reflects the unique characteristics of each emotion, unlike the conventional technology that evaluated all emotions as the same item.

[0089] For example, in the case of the simple and clear emotion 'sleepiness', the preset may be configured with only two axes, 'fatigue' and 'motivation', while in the case of the complex emotion 'bittersweetness', the preset may be configured with four or more axes, such as 'satisfaction', 'regret', 'relief', and 'lingering attachment'. Additionally, the configuration of these axes may be added or removed retrospectively based on system updates or user requests to change settings.

[0090] In various embodiments, preset values ​​for multiple emotion axes may be provided by being reset to pre-set initial values ​​whenever a log is generated. That is, the preset value for a specific emotion item may always be provided as a fixed reference value, regardless of how the user has recorded that emotion in the past (history). This prevents the method of automatically changing presets by learning the user's previous input patterns from distorting or biasing the user's current emotion, and allows the amount of change in the emotion felt by the user to be accurately measured as a deviation from an objective reference point (i.e., preset) at each time a log is generated.

[0091] For example, even if the user recorded 'joy' yesterday and saved the tension axis value by adjusting it high from the default value of '50' to '90', when creating a new log by selecting 'joy' again today, the tension axis may be reset to the initial setting value of '50' instead of '90'. Through this, the computing device (100) can clearly distinguish and datafy how much the joy of yesterday (90) and the joy of today (e.g., 50) differ from the reference point (50).

[0093] In one embodiment, when the computing device (100) receives an adjustment input for a preset value, it can generate multi-axis emotional information including values ​​of emotional axes changed according to the adjustment input (S130). Then, the computing device (100) can map and store the multi-axis emotional information in a log (S140).

[0094] Specifically, the computing device (100) can detect user operation signals for UI objects (such as sliders) of each emotion axis displayed through the input interface (e.g., touchscreen) of the user terminal (200). When the user performs a drag or touch input to increase or decrease the value of a specific emotion axis through the user terminal (200), the computing device (100) can change the value of the emotion axis in real time to a numerical value corresponding to the input.

[0095] Subsequently, when a user inputs a 'save' or 'record complete' command through the user terminal (200), the computing device (100) can collect the changed numerical values ​​of each emotion axis confirmed at that time and generate multi-axis emotion information as a single data set. The multi-axis emotion information generated here is mapped to a log identifier (Log ID) and stored in a database; in this process, the original 'preset value' of the corresponding emotion item is maintained without being changed and can be stored only as instance data dependent on the corresponding log.

[0096] For example, assuming that when a user selects the emotion 'joy', the provided preset value of the 'tension' axis is '50', but the user feels very high tension at that moment and manipulates the slider to change the value to '90', the computing device (100) can generate multi-axis emotion information including the adjusted value and the maintained value, such as 'tension=90', 'mood=50 (no change)', and 'fun=50 (no change)', and include and store this in the log data currently being written. Through this, the user's specific state can be numerically differentiated and recorded even under the same label of 'joy'.

[0097] Meanwhile, in step (S130), if there is no adjustment input for the preset value, the computing device (100) can map the preset value to a log as multi-axis emotional information and store it.

[0098] Specifically, the computing device (100) can monitor whether any change signal is detected for the emotion axis UI object from the time the emotion preset value is loaded into the user terminal (200) until a save command is received. If the user wishes to complete the recording without separate numerical adjustments, the computing device (100) can determine that the proposed default preset value matches the user's current emotion state, and can copy the preset value as is to confirm and save it as the multi-axis emotion information of the log.

[0099] For example, if a user selects the emotion of 'calmness' and presses the save button immediately without feeling the need to modify the provided preset values ​​(e.g., tension=10, stability=90), the computing device (100) can save the preset values ​​of 'tension=10' and 'stability=90' as emotion data for the corresponding log.

[0100] Accordingly, the computing device (100) of the present invention can record emotions in a dynamic data structure that includes variable numerical data in log (moment) units, unlike the conventional method of recording emotions only as static text labels such as 'happiness' or 'sadness'. Through this, even if the same emotion is recorded multiple times a day, minute differences such as the intensity, texture, and energy level of the emotion that change at each moment can be precisely accumulated without loss.

[0102] In various embodiments, the computing device (100) can perform a statistical processing operation to derive the user's emotional tendency by analyzing accumulated log data.

[0103] Referring to FIG. 4, when a plurality of logs in which a specific emotion is selected are stored, the computing device (100) can calculate a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to a specific emotion (S150).

[0104] Specifically, the computing device (100) can determine a representative value for each axis by accumulating adjustment values ​​of each emotion axis included in a plurality of adjustment inputs and calculating at least one of an average value, a median value, and a mode value for each axis.

[0105] More specifically, the computing device (100) can search past log data stored in a database based on a specific emotion identifier (e.g., 'joy'), and form a population by extracting numerical data for each emotion axis (e.g., tension, energy, etc.) from multi-axis emotion information included in the searched log data.

[0106] In this case, the computing device (100) can determine a single value representing the emotion axis by applying various statistical techniques, such as calculating the arithmetic mean of the values ​​included in the population to identify the general tendency of the user, using the median to exclude the influence of outliers, or deriving the mode, which is a specific value most frequently set by the user.

[0107] Additionally, the computing device (100) may not only simply integrate and analyze the entire log, but also filter or subdivide the population based on contextual information such as timestamps or location data included in the log to calculate representative values ​​for each axis. Through this, the computing device (100) can reflect that even for the same emotion, the texture or intensity of the emotion felt by the user may differ depending on the situational context, such as 'morning' and 'afternoon', or 'home' and 'work'.

[0108] For example, assume that a user recorded the emotion of 'comfort' 20 times during the past month, and the default preset value of 'Arousal,' the first axis of that emotion, was '20.' If the user recorded 'Arousal' between '10' and '20,' which is similar to the preset, when resting at home, but recorded 'Arousal' at a higher level of around '50' when feeling comfort while working at the company, the computing device (100) can calculate an overall average value of '30' and, at the same time, calculate separate representative values ​​such as 'Comfort at home - Arousal: 15' and 'Comfort at work - Arousal: 50' by distinguishing by context. Through this, the computing device (100) can numerically indicate that the user's 'comfort' has a dual meaning depending on the situation.

[0109] In other words, in addition to emotional information, logs may include situational information such as time information (AM / PM, etc.) and location information (home / work, etc.) at the time of log creation. When personalized emotional information is provided as described below, users can filter specific situational conditions through this information to identify how depression at work differs from depression at home along the axis.

[0110] And, the computing device (100) can generate personalized emotion information for a specific emotion based on a representative value for each axis and provide it to a user terminal (200) (S160).

[0111] Specifically, the computing device (100) can generate at least one of the user's tendency intensity and change trend for a specific emotion as personalized emotion information based on the deviation between the axis-specific representative value and the preset value.

[0112] More specifically, the computing device (100) can calculate a deviation by subtracting a preset value (reference data) of the corresponding emotion item from the calculated representative value (user data) for each axis. Then, the computing device (100) can identify an emotion axis in which the absolute value of the deviation is greater than or equal to a preset threshold and determine that the axis is a major factor in determining the user's emotional tendency.

[0113] Additionally, the computing device (100) can generate personalized emotion information by converting these quantitative analysis results into a report in the form of natural language sentences that are easy for the user to understand, or by rendering them into a graph interface that visually contrasts reference data and user data. Furthermore, the computing device (100) can track changes in representative values ​​by period (e.g., last month vs. this month) to include additional trend information on how the user's emotion definition is changing over time.

[0114] For example, assuming that the preset value of the 'Energy' axis for the emotion of 'sadness' is set to '20 (very low)', but the representative value of the 'Energy' axis is calculated to be '70 (somewhat high)' as a result of analyzing the user's log, the computing device (100) can generate and provide a personalized analysis message such as, "Your sadness shows higher energy than usual. Is there any anger or frustration mixed in?"

[0115] For example, referring to FIG. 5, the computing device (100) can provide personalized emotional information regarding the emotion of 'Joy' in the form of a radar chart through the screen of the user terminal (200). As illustrated, on the screen of the user terminal (200), a first polygonal graph (e.g., default preset) connecting preset values ​​based on three axes of 'Tension', 'Mood', and 'Fun', and a second polygonal graph (e.g., my average) connecting the user's representative values ​​for each axis can be displayed superimposed with different colors or line styles. Through this, the user can intuitively understand that there is a tendency to record 'Joy' when feeling relatively higher tension than others.

[0116] Accordingly, the computing device (100) of the present invention can help improve the user's self-objectification and metacognitive abilities by going beyond simply listing past emotional records and redefining the standards and patterns of emotions that the user unconsciously possesses into visual materials that can be compared with objective data.

[0118] According to various embodiments of the present invention, a computing device (100) can recommend an emotion item based on an N-axis value before selecting an emotion item. Here, the N-axis value may refer to a set of numerical data input for the components of the emotion felt by the user (e.g., energy, pleasant / unpleasant, tension, etc.) when no specific emotion label is assigned.

[0119] Specifically, the computing device (100) may receive an N-axis value input including at least one emotion axis value from a user terminal (200) before receiving emotion selection information.

[0120] More specifically, the computing device (100) may first provide an input interface composed of multiple emotion axes (e.g., X-axis - energy, Y-axis - mood, etc.) instead of a menu for selecting an emotion name on the display of the user terminal (200). When the user manipulates the sliders or coordinates of each axis to correspond to their current state, the computing device (100) may obtain an N-axis value input, which is a combination of numerical values ​​corresponding to the manipulation. That is, the N-axis value can be entered when the user finds it difficult to define their emotion in specific words (e.g., alexithymia) or when they wish to first numerically express a complex emotional state.

[0121] For example, a computing device (100) can receive a two-dimensional N-axis value input from a user, including values ​​of ‘20 (low)’ for the ‘Energy’ axis and ‘90 (very high)’ for the ‘Stress’ axis.

[0122] Additionally, the computing device (100) can calculate the similarity between the input N-axis value and the preset values ​​for a plurality of previously stored emotion items.

[0123] Specifically, the computing device (100) can calculate similarity by calculating the distance between the received N-axis value input (input vector) and the preset values ​​(reference vectors) of each emotion item stored in the database. Here, vector analysis techniques such as Euclidean distance, cosine similarity, or Manhattan distance may be used as similarity calculation algorithms.

[0124] Additionally, the computing device (100) may calculate a similarity optimized for the user's individual recording pattern by comparing not only the basic preset value, but also the user's 'personalized axis representative value' calculated earlier (at step S150, etc.).

[0125] For example, the computing device (100) can search for emotion presets that are closest to the input vector [Energy: 20, Stress: 90]. For example, if the preset for 'sadness' is defined as [20, 40], the preset for 'anger' as [90, 80], and the preset for 'burnout' as [15, 95], the computing device (100) can calculate that the 'burnout' item has the highest similarity to the input value, with the smallest error.

[0126] And, the computing device (100) can provide one or more emotion items to the user terminal (200) in order of highest calculated similarity.

[0127] Specifically, the computing device (100) can sort emotion items according to the calculated similarity score and extract the top K (e.g., 3) emotion items to generate a recommendation list. Then, through the user terminal (200), a query message such as "Is the emotion you are feeling right now one of [burnout], [exhaustion], or [lethargy]?" or a selectable chip-shaped UI can be provided to induce the user to finally complete the log recording by selecting one of these.

[0128] For example, the computing device (100) can recommend 'Burnout' with a similarity of 98% as the first priority and 'Frustration' with a similarity of 85% as the second priority and display them at the top of the screen. Accordingly, when the user selects the recommended 'Burnout', the previously entered N-axis value [20, 90] can be automatically mapped and saved as multi-axis emotion information of the corresponding log.

[0129] Accordingly, the computing device (100) of the present invention can lower the entry barrier to emotion recording and expand the user's vocabulary by assisting the user in finding the most appropriate emotion word in reverse through intuitive numerical manipulation, even in ambiguous situations where it is difficult to express their emotions in accurate language.

[0131] According to various embodiments of the present invention, the computing device (100) can update preset values ​​based on statistics.

[0132] Referring to FIG. 6, the computing device (100) can calculate a statistical representative value for each axis based on a plurality of multi-axis emotional information generated by a plurality of user terminals (200) (S210).

[0133] Specifically, the computing device (100) can collect a vast amount of emotional log data generated by an unspecified number of users to build big data, and analyze the data distribution for each axis using multi-axis emotional information mapped to a specific emotional item (e.g., 'depression') as a population.

[0134] In this case, the computing device (100) may calculate the statistical representative values ​​for each axis by performing a preprocessing process to remove outliers corresponding to the upper and lower n% of the entire distribution in order to increase the reliability of the data, and then calculating the arithmetic mean or weighted mean of the remaining data. Here, the statistical representative values ​​for each axis may refer to collective intelligence-based reference values ​​that quantify social conventions or universal emotional states that the majority of users empathize with regarding a specific emotion.

[0135] For example, the computing device (100) can analyze data entered by tens of thousands of users over the past year while recording an emotion item called ‘Monday blues’. If the analysis results show that the existing preset value of the ‘fatigue’ axis was ‘50’, but the average value of the actual users’ input was ‘85’, the computing device (100) can calculate ‘85’ as a new statistical representative value for the ‘Monday blues-fatigue’ axis.

[0136] And, the computing device (100) can update the preset value set for a specific emotion corresponding to a plurality of multi-axis emotion information based on the statistical representative value for each axis (S220).

[0137] Specifically, the computing device (100) can compare the statistical representative values ​​for each axis calculated periodically (e.g., on the 1st of every month) or in real time with existing preset values, and if the difference between the two values ​​exceeds a preset threshold range, update the preset values ​​in the database to the statistical representative values.

[0138] Through this, the computing device (100) can manage the preset values ​​for each emotion to evolve dynamically according to the flow of the times or changes in users' perceptions. Additionally, the updated preset values ​​can be distributed as new initial values ​​to all user terminals (200) that subsequently select the corresponding emotion item or to user terminals (200) that have agreed to the update.

[0139] For example, the computing device (100) can overwrite the existing value '50' by reflecting the previously calculated statistical representative value of 'Monday blues-fatigue', '85', into the database. Accordingly, for a user who intends to write a log by selecting 'Monday blues' after the update, the 'fatigue' slider can be provided with the position of '85' instead of '50'. Accordingly, since the value is already set to the level (85) felt by the majority of people, the user can quickly and conveniently record their condition with minimal fine-tuning without needing to manipulate the slider extensively.

[0140] Accordingly, the computing device (100) of the present invention can maintain the up-to-dateness of emotion definitions by reflecting changes in the social meaning of emotion words, and can reduce the time and effort required for input operations by providing initial values ​​that correspond to the actual input patterns of users.

[0142] According to various embodiments of the present invention, the computing device (100) may provide a customization function that creates a new unique emotion definition of its own when the user is not satisfied with the existing preset.

[0143] Referring to FIG. 7, the computing device (100) may receive a request to create a new emotion from a user terminal (200), which includes an emotion name, an emotion image, one or more emotion axes, and one or more preset values ​​for each emotion axis (S310).

[0144] Specifically, the computing device (100) can receive data packets input through an 'emotion creation' interface provided to the user terminal (200). The user can input the name of the emotion to be defined in the interface, upload an icon or image file to visually represent the emotion, or select one from the system library. Additionally, the user can set the number and names of axes by adding or deleting components (axes) that constitute the emotion, and can specify the initial value serving as the standard for each axis through slider operation, etc.

[0145] For example, a computing device (100) may receive a generation request from a user that includes an emotion name "Burning Friday," an image of a "flame-shaped icon," and three emotion axis definition data set to [Excitement: 90, Fatigue: 80, Anticipation: 100].

[0146] And, the computing device (100) can generate a custom emotion in response to a request to generate a new emotion (S320).

[0147] Specifically, the computing device (100) can create a new emotion object in a database based on received request information and assign a unique identifier (Unique ID) to identify it. Additionally, the computing device (100) can store the uploaded image in a file server, map the path to the identifier, and structure and store the set emotion axis and preset values ​​as attribute data for the corresponding emotion.

[0148] For example, the computing device (100) can assign an ID 'CUST_EMO_001' to the emotion of 'Friday night' and store it in a personal library by linking it with the user's account information.

[0149] Additionally, the computing device (100) can add a user-defined emotion to an emotion item related to emotion selection information so that a user-defined emotion can be selected when generating a log (S330).

[0150] Specifically, the computing device (100) can include the generated user-defined emotion in the emotion candidate list that is called when the log creation screen is subsequently called on the user terminal (200) and transmit it. Here, the user-defined emotion may be placed in a separate section to be visually distinguished from the default emotion, or may be displayed with a tag such as 'My' attached.

[0151] For example, when a user enters the emotion selection screen to write a diary, a user-created 'Friday Night' icon appears activated next to basic emotions such as 'Joy' and 'Sadness', and if this is selected, the previously set 3-axis slider for [Excitement, Fatigue, Anticipation] can be provided with initial values ​​[90, 80, 100].

[0152] In various embodiments, user-defined emotions may be configured to be shareable with other user terminals (200).

[0153] Specifically, the computing device (100) can generate and provide a URL link, QR code, or searchable hashtag for external sharing regarding the generated user-defined emotion.

[0154] When another user accesses the emotion through the link or code or searches for it, the computing device (100) may provide a function to copy and download the emotion name, image, axis configuration, and preset value defined by the original creator to the recipient's terminal. Through this, the definition of the emotion can be propagated and shared beyond the individual to others.

[0155] For example, if a user shares a complex emotion they created called ‘Learning Time Off’ (axis: liberation 100, fatigue 90) on social media, other parents who see it can press the ‘Import’ button to add ‘Learning Time Off’ to their emotion list and immediately use the preset to record their status.

[0156] Accordingly, the computing device (100) of the present invention overcomes the limitations of a limited vocabulary provided unilaterally, provides a creative opportunity for the user to proactively define and name their own emotional state, and can enrich and expand the ecosystem of emotional expression through the sharing of emotional definitions among users.

[0158] According to various embodiments of the present invention, a computing device (100) can detect numerical imbalances or contradictions within multi-axis emotional information input by a user and perform in-depth analysis.

[0159] Referring to FIG. 8, the computing device (100) can calculate the deviation between the values ​​of a plurality of emotion axes included in multi-axis emotion information (S410).

[0160] Specifically, the computing device (100) can extract multiple emotion axis values ​​constituting multi-axis emotion information included in a single log, and calculate the difference (Range), variance, or standard deviation between the maximum and minimum values ​​among the values ​​to calculate a deviation, which is a numerical value representing the degree of imbalance between emotion axes.

[0161] Alternatively, the computing device (100) may individually calculate and analyze the difference in values ​​between two specific axes (e.g., positive mood vs. negative physical reaction) defined in advance as having opposite polarities.

[0162] For example, assuming that a user has selected the emotion of 'happiness' but has set the 'mental satisfaction' axis to '90 (very high)' and the 'physical energy' axis to '10 (very low)' in the detailed axis settings, the computing device (100) can calculate the difference between the two axis values, '80', as the deviation value of the corresponding log.

[0163] Additionally, the computing device (100) can classify multi-axis emotional information into a specific emotional pattern when the deviation is greater than or equal to a preset threshold (S420).

[0164] Specifically, the computing device (100) can compare the calculated deviation value with a previously stored reference threshold. As a result of the comparison, if the deviation value exceeds the threshold, the computing device (100) can determine that the log contains a state that deviates from the consistency of general emotions, that is, a complex or contradictory emotional state, and classify it as an unusual emotional pattern. Here, the unusual emotional pattern may be a concept that includes a complex emotional state that cannot be fully explained by a single emotional name, or a cognitive dissonance state in which the psychological state and the physical state are at odds.

[0165] For example, with the threshold set to '50', the previously calculated deviation value '80' exceeds the threshold, so the computing device (100) can identify the 'happiness' log as a log containing a peculiar emotional pattern rather than ordinary happiness. That is, the computing device (100) can recognize that the user is in a state of exhausted happiness where the user thinks they are happy in their head but their body is extremely tired.

[0166] And, the computing device (100) can map and store specific emotion information corresponding to specific emotion patterns into a log (S430).

[0167] Specifically, for a log classified as a specific emotion pattern, the computing device (100) may generate a derived emotion label or an in-depth analysis comment that specifically designates the pattern in addition to a simple emotion name, and store this by tagging it in the log as specific emotion information. The specific emotion information generated here may be automatically generated by referring to a predefined database (e.g., "Satisfaction High + Energy Low = Lethargy / Exhaustion") or determined through a query to the user such as "Is the emotion you are feeling right now closer to 'bittersweet'?"

[0168] For example, the computing device (100) can map auxiliary tags such as discharged or love-hate to specific emotional information under a basic label of happiness for the preceding log. Subsequently, when the user views the corresponding date in the calendar view, a separate warning mark or special color is displayed on the happiness icon, allowing the user to intuitively recognize that the date was an emotionally complex day.

[0169] Accordingly, the computing device (100) of the present invention can support the user in discovering and managing emotional dissonance or deep-seated states that the user was not aware of by recognizing contradictory or complex psychological states through numerical deviation analysis even when the user selects a single emotion word.

[0171] According to an additional embodiment of the present invention, a computing device (100) can provide a dynamic preset that generates a plurality of emotion axes optimized for the situation in real time by analyzing situation description information in the form of text entered by a user through an artificial intelligence model.

[0172] Specifically, the computing device (100) receives text data in the form of paragraphs (i.e., logs) describing the user's current situation or state of mind from the user terminal (200), and inputs this into a natural language processing (NLP) engine or a large language model (LLM) to extract key emotional keywords and emotional nuances included in the context. Furthermore, based on the extracted information, the computing device (100) can dynamically define multiple temporary emotional axes for the logs only, rather than fixed presets that are already stored.

[0173] More specifically, the computing device (100) can perform semantic analysis on text data to identify principal components constituting a complex emotional state. Then, the computing device (100) can map each identified component to a label of an emotional axis to generate N dynamic emotional axes, and generate variable input UI objects (such as sliders) corresponding to them and provide them to a user terminal (200).

[0174] For example, when a computing device (100) receives text input from a user saying, “I’m so embarrassed that I messed up the presentation, but I think I can do better next time,” it can detect conflicting or complex emotional elements such as “shame about failure” and “will for the future” in the sentence.

[0175] In this case, instead of providing a single category preset such as existing ‘sadness’ or ‘depression’, the computing device (100) can generate and provide three new emotional axes [embarrassment], [gritness], and [hope] based on the analysis results.

[0176] Accordingly, by setting the 'Confused' slider displayed on the screen to '90' and the 'Stubbornness' slider to '80' and saving, the user can precisely quantify and record the complex psychological state at the time, which could not be expressed by existing emotion words.

[0177] Therefore, the computing device (100) can improve the user’s emotion recording experience by not fitting the user’s emotions into a category of predefined emotion categories, but by presenting a customized evaluation criterion (axis) that is more suitable for the situation through artificial intelligence.

[0179] According to an additional embodiment of the present invention, a computing device (100) can generate multidimensional statistical information by classifying collected multi-axis emotional information into temporal, spatial, and user attribute-based hierarchies.

[0180] To this end, the computing device (100) can classify users into a resident group and a visitor group based on location information included in log data collected from multiple user terminals. Additionally, the computing device (100) can generate comparative analysis data by calculating the average value of an emotional axis (e.g., tension, vitality, etc.) for a specific place or time for each group.

[0181] Additionally, the computing device (100) can subdivide spatial information into units of country, city, neighborhood, and specific store (POI), and stratify time information by month, season, and day of the week to derive an emotional activity index that matches each unit. Here, the emotional activity index refers to a normalized value based on the average, density, or cumulative frequency of emotional axis values ​​collected within a specific unit (time / space), and may be a quantitative indicator representing the dominance of a specific emotion or the collective energy level in the corresponding time and space.

[0182] More specifically, the computing device (100) can generate a regional sentiment map representing the dominant sentiment atmosphere of the region by time of day by analyzing the trend of change in average values ​​of multi-axis sentiment information collected within a specific spatial unit (e.g., administrative district, commercial district). For trend analysis, the computing device (100) can track the time-series fluctuations of average sentiment values ​​calculated in a specific time window unit, or detect an upward slope or downward slope above a threshold by comparing the current data with the cumulative average data from the same point in the past (e.g., the same day of the week last week). That is, the upward slope or downward slope refers to a quantitative rate of change indicating how rapidly the emotional activity of the space rises or subsides / stabilizes starting from a specific point in time, and this can be used to identify changes in environmental contexts such as the start and end of a specific event or a sudden change in the flow of people.

[0183] In this case, the computing device (100) can perform filtering that selectively reflects data of users who have actually experienced the space rather than users who merely pass through, by applying weights to the user's dwell time and movement patterns identified through communication logs, etc., in addition to simply calculating the overall average.

[0184] For example, when a computing device (100) analyzes data about a place called Beach A, it can separate the average tension value of a group of users residing in the area (e.g., 40 - normal calm) and the average tension value of a group of visitors from other areas (e.g., 85 - high excitement) to produce statistics.

[0185] Through this, the computing device (100) can recognize the three-dimensional nature of the place, which is a peaceful walking path for residents but a lively hot spot for tourists, rather than a simple average value. In this case, the computing device (100) can analyze the user's personal history and provide a personalized place recommendation report such as, "When you visit 'Seongsu-dong Cafe Street,' your average tension tends to increase by 30% compared to usual."

[0186] In addition, the computing device (100) can provide marketing insights to the store owner, such as, "Your store's visitors' 'mood' levels are measured to be highest around 2 PM, so it is effective to run promotions during this time."

[0187] In various embodiments, the computing device (100) can provide an extended feedback service by linking the calculated multidimensional statistical information and real-time user status with a smart city solution or an external content platform.

[0188] Specifically, the computing device (100) can perform a city stress management function by monitoring the emotional indicators of users within a specific area in real time and, when a collective emotional change (e.g., a surge in stress index) is detected, controlling the IoT infrastructure of the area to actively improve the environment.

[0189] For example, when the stress level of users clustered in a specific square in the city center exceeds a preset threshold, the computing device (100) can generate a control signal in conjunction with a smart city control server or a local IoT hub system to adjust the streetlight brightness of the area to a warm color tone or play calming music through public speakers.

[0190] In various embodiments, the computing device (100) can perform state-based advertising that recommends customized content or products optimized for statistically analyzed user emotional tendencies and current emotional axis values.

[0191] Specifically, the computing device (100) can predict the category with the highest probability of acceptance in the current emotional state based on the user's past reaction history statistics, and select and provide content that matches it.

[0192] For example, the computing device (100) can increase advertising efficiency by displaying dynamic leisure products or activity reservation information when the user is judged to be in an excited state, and by prioritizing the display of advertisements for books, meditation applications, or healing products when the user is judged to be in a calm state.

[0193] In various embodiments, the computing device (100) can provide a customized routine guide from a wellness perspective by analyzing the statistical correlation between the user's location information and emotional patterns.

[0194] Specifically, the computing device (100) can generate behavioral suggestions based on past data when it is determined that a specific behavior or place visit has a statistically significant association with a positive change in the emotional axis (e.g., tension increase).

[0195] For example, the computing device (100) can provide a specific action suggestion to a user with low tension, such as, "If you take a walk in Park A, which is a 5-minute walk from your current location, your average tension will increase by about 15%."

[0196] Accordingly, the computing device (100) of the present invention can convert an individual’s emotional state into a quantitative statistical indicator by combining it with an objective context of location and time, thereby providing the individual with retrospective data for self-understanding and providing a social data indicator that can increase the value of space to a company or local government.

[0198] Meanwhile, embodiments according to the present disclosure may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded on a computer-readable medium. In this case, the medium may include, but is not limited to, magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0199] Meanwhile, the above computer program may be one specifically designed and configured for the present disclosure or one known and available to those skilled in the art of computer software. Examples of computer programs may include machine code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0200] According to one embodiment, the method according to various embodiments of the present disclosure may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0201] Unless explicitly stated otherwise, the steps constituting the method according to the present disclosure may be performed in a suitable order. The present disclosure is not necessarily limited by the order in which the steps are described. The use of any examples or exemplary terms (e.g., etc.) in the present disclosure is merely for the purpose of describing the present disclosure in detail and, unless limited by the claims, the scope of the present disclosure is not limited by such examples or exemplary terms. Furthermore, those skilled in the art will understand that various modifications, combinations, and changes may be made according to design conditions and factors within the scope of the claims or equivalents to which they are added.

[0202] Accordingly, the scope of the present disclosure should not be limited to the embodiments described above, and all scopes equivalent to or equivalently modified from the claims set forth below, as well as the claims set forth below, shall be considered to fall within the scope of the scope of the present disclosure.

Claims

Claim 1 A method for recording emotions based on multi-axis emotions presets, performed by a computing device comprising at least one processor, comprising: receiving emotion selection information as a result of generating a log at a user terminal; providing preset values ​​for a plurality of emotion axes corresponding to the emotion selection information to the user terminal; generating multi-axis emotion information including values ​​of emotion axes changed according to the adjustment input when an adjustment input for the preset values ​​is received; and mapping and storing the multi-axis emotion information in the log; wherein the adjustment input for the preset values ​​includes an operation to increase or decrease the preset values ​​for each of the plurality of emotion axes to correspond to the current emotion state of the user by input through a user interface that is individually operable for each of the plurality of emotion axes. Claim 2 A multi-axis emotion preset-based emotion recording method according to claim 1, further comprising: a step of calculating a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to the specific emotion when a plurality of logs for which a specific emotion is selected are stored; and a step of generating personalized emotion information for the specific emotion based on the representative value for each axis and providing it to the user terminal. Claim 3 In claim 2, the step of calculating a representative value for each axis based on a plurality of adjustment inputs for a plurality of emotion axes corresponding to the specific emotion includes: a step of determining the representative value for each axis by accumulating the adjustment values ​​of each emotion axis included in the plurality of adjustment inputs and calculating at least one of an average value, a median value, and a mode value for each axis; and the step of generating personalized emotion information for the specific emotion based on the representative value for each axis and providing it to the user terminal includes: a step of generating at least one of the user's tendency intensity and change trend for the specific emotion as the personalized emotion information based on the deviation between the representative value for each axis and the preset value. Claim 4 A multi-axis emotion preset-based emotion recording method according to claim 1, wherein the plurality of emotion axes can be set to a different number depending on the emotion item, and the preset values ​​for the plurality of emotion axes are provided by being reset to preset initial values ​​whenever a log is generated. Claim 5 A method for recording emotions based on a multi-axis emotion preset according to claim 1, further comprising: a step of calculating a statistical representative value for each axis based on a plurality of multi-axis emotion information generated by a plurality of user terminals; and a step of updating a preset value set for a specific emotion corresponding to the plurality of multi-axis emotion information based on the statistical representative value for each axis. Claim 6 A multi-axis emotion preset-based emotion recording method according to claim 1, wherein the method further comprises: receiving a new emotion generation request from the user terminal including an emotion name, an emotion image, one or more emotion axes, and preset values ​​for each of the one or more emotion axes; generating a user-defined emotion according to the new emotion generation request; and adding the user-defined emotion to an emotion item related to the emotion selection information so that the user-defined emotion can be selected when generating the log, wherein the user-defined emotion is shareable with another user terminal. Claim 7 A multi-axis emotion preset-based emotion recording method, further comprising: a step of receiving an N-axis value input including at least one emotion axis value from a user terminal prior to receiving emotion selection information from a user terminal; a step of calculating a similarity between the N-axis value input and a plurality of previously stored emotion item preset values; and a step of providing one or more emotion items to the user terminal in order of the calculated similarity. Claim 8 A multi-axis emotion preset-based emotion recording method according to claim 1, further comprising: a step of calculating a deviation between the values ​​of a plurality of emotion axes included in the multi-axis emotion information; a step of classifying the multi-axis emotion information into a specific emotion pattern when the deviation is greater than or equal to a preset threshold; and a step of mapping and storing the specific emotion information corresponding to the specific emotion pattern into the log. Claim 9 A device comprising: a memory for storing one or more instructions; and a processor for executing the one or more instructions stored in the memory, wherein the processor performs the method of claim 1 by executing the one or more instructions. Claim 10 A computer-readable recording medium storing a computer program for executing the method of either claim 1 or claim 7 using a computer. Claim 11 A device comprising: a memory for storing one or more instructions; and a processor for executing the one or more instructions stored in the memory, wherein the processor performs the method of claim 7 by executing the one or more instructions.

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