Apparatus and method for providing user-tailored emotional coaching based on the user's selection of emotional words

Abstract

The present disclosure relates to a system for providing personalized emotion coaching to a user by considering the user's selection of emotion words. According to the present disclosure, an emotion coaching analysis device collects emotion response information regarding the result of a user's selection of emotion words, behavioral response information regarding the user's behavioral characteristics, and diagnostic environment information regarding the environment in which the emotion response information is input; determines the user's emotional state from the emotion response information, the behavioral response information, and the diagnostic environment information; and generates an emotion analysis result from the emotional state, the emotion response information, and the emotion text input by the user through an emotion analysis UI to which an NLP model is applied.

Description

Technology Field

[0001] The present disclosure generally relates to a system for providing personalized emotion coaching by considering the user's choice of emotion words, and more specifically, to an apparatus and method for diagnosing the user's emotional state by synthesizing emotion word selection information and information collected during the emotion input process, and providing emotion coaching accordingly.

[0002] More specifically, the present disclosure relates to 'Interactive Emotional Coaching' technology that goes beyond passively monitoring a user's emotional data to provide active guidance to enable the user to regulate their emotions and build resilience. Through this, the user can experience personalized emotional coaching services regardless of time and place. Background Technology

[0004] With the advancement of information and communication technology and smart devices, attempts to digitally identify and manage individuals' psychological or emotional states are expanding. In particular, interest in daily emotional recording, diary keeping, and psychological checklists is increasing, centered around smartphones. Consequently, numerous emotion management applications are emerging that allow users to directly record their emotions or respond to surveys regarding their emotional status. These services have the advantage of high potential for utilization in that they lower the barrier to entry for emotion recognition activities and support users in viewing their own emotions objectively.

[0005] Conventional emotion checking and recommendation services have tended to rely solely on emotion words, scores, or simple selection options explicitly entered by the user. In other words, they often remained at the level of simply classifying input text or emotion words to determine the degree of positivity or negativity, or displaying the fluctuations in emotion scores in graphs. This approach had limitations in that it was difficult to adequately reflect the context of emotions the user was unaware of, hidden desires, emotional expression habits, or the background behind the formation of those emotions. Furthermore, existing emotion coaching methods did not utilize subtle behavioral patterns observed during the user's emotion input process or external environmental information at the time of input as factors for emotion interpretation. Consequently, these methods had limitations, such as providing distorted conclusions skewed toward the user's response or failing to adequately explain the emotional state.

[0006] Furthermore, conventional technology has a drawback in that it is limited to a 'monitoring' function that merely notifies the user of their emotional state, failing to provide specific emotional coaching solutions to resolve negative emotions or reinforce positive ones. True emotional coaching must involve a process that helps users accept their emotions as they are, identify the underlying needs, and develop the ability to self-regulate their feelings (Emotional Regulation). However, existing systems have had limitations in algorithmizing these psychological techniques to provide users with customized behavioral guides or cognitive restructuring training.

[0007] Therefore, there is a need to develop sentiment analysis technology capable of estimating emotional states in an advanced manner by collecting and analyzing not only the results of the user's emotional word selection but also external information generated during the input process, and performing the differentiation of factual interpretation, exploration of emotional motivations, and provision of coaching content in stages through a natural language processing-based interface.

[0008] The aforementioned technology is technical information that the inventor possessed for the derivation of the present invention or acquired during the process of deriving the present invention, and does not necessarily refer to known technology disclosed to the general public prior to the filing of the present invention. Prior art literature

[0010] Published Patent Application No. 10-2025-0027468 (Feb. 26, 2025) Registered Patent Application No. 10-2536350 (May 26, 2023) The problem to be solved

[0011] Based on the discussion described above, the present disclosure provides an apparatus and method for diagnosing a user's emotional state by synthesizing information on the user's emotional word selection and information collected during the emotional input process, and providing emotional coaching accordingly.

[0012] In addition, the present disclosure provides an apparatus and method for determining a user's emotional state based on emotional response information, behavioral response information, and environmental information in an emotional coaching analysis system.

[0013] In addition, the present disclosure provides an apparatus and method for quantifying an emotional state by reflecting emotional word selection patterns and intensity in an emotion coaching analysis system.

[0014] In addition, the present disclosure provides an apparatus and method for reflecting user interaction data measured during the behavior input process in an emotion coaching analysis system into an emotional state analysis.

[0015] In addition, the present disclosure provides an apparatus and method for reflecting environmental factors corresponding to the time of emotional input in an emotional coaching analysis system into the process of determining the emotional state.

[0016] In addition, the present disclosure provides an apparatus and method for analyzing emotional text and generating an emotional structure using an NLP-based user interface in an emotion coaching analysis system.

[0017] In addition, the present disclosure provides an apparatus and method for generating and providing user-customized emotion analysis results by synthesizing emotional states and emotional text analysis results in an emotion coaching analysis system.

[0018] In addition, the present disclosure aims to provide an AI-based automated emotion coaching algorithm that helps find psychological stability solely through interaction with the system.

[0019] Furthermore, the present disclosure aims to implement a cyclic emotion coaching process that goes beyond providing one-time analysis results and provides continuous and step-by-step feedback according to the user's emotional changes. means of solving the problem

[0021] According to various embodiments of the present disclosure, a method of operation of an emotion coaching analysis device may include: collecting emotion response information regarding the result of a user’s selection of emotion words, behavioral response information related to the user’s behavioral characteristics, and diagnostic environment information regarding an environment in which the emotion response information is input; determining the user’s emotional state from the emotion response information, the behavioral response information, and the diagnostic environment information; generating an emotion analysis result from the emotional state, the emotion response information, and the emotion text input by the user through an emotion analysis UI to which an NLP model is applied; and transmitting the emotion analysis result to a user terminal.

[0022] According to another embodiment, the emotional response information indicates the result of the user selecting and arranging some of a plurality of predefined emotional words, and the step of determining the emotional state may include: a step of calculating an emotional intensity value by considering the selection frequency and arrangement size of each of the emotional words included in the emotional response information; a step of determining a main emotional state by applying a weight set corresponding to each of the emotional words to the emotional intensity value; a step of selecting an empathetic user with the highest state similarity to the user by using the behavioral response information and the diagnostic environment information; a step of determining a sub-emotional state by confirming the empathetic emotional state of the empathetic user; and a step of determining an emotional state by weighted summing the main emotional state and the sub-emotional state.

[0023] According to another embodiment, the step of generating the emotion analysis result may include: separating the content included in the emotion text into factual content and interpretation content and providing the separation result to the user; selecting a first questionnaire asking for the reason for the emotion response information and providing expression feedback to the user in response to the first user response to the questionnaire; selecting a second questionnaire asking for the user's situation based on the emotion response information and the first user response, and determining the user's emotion motivation based on the second user response to the second questionnaire; and generating an emotion analysis result that explains the user's emotion by synthesizing the emotion state, the separation result, and the emotion motivation.

[0024] According to another embodiment, the method of operation of an emotion coaching analysis device may further include the step of determining coaching content that proposes a behavioral goal to the user in response to the emotional state after transmitting the emotion analysis result to the user terminal, and the step of transmitting the coaching content to the user terminal.

[0025] According to another embodiment, the method of operation of an emotion coaching analysis device may further include the steps of receiving a follow-up response regarding the coaching content from the user terminal, applying a correction index included in the follow-up response to the emotion state to calculate an updated emotion state, and updating an emotion analysis result using the updated emotion state.

[0026] According to various embodiments of the present disclosure, an emotion coaching analysis system may include an emotion coaching analysis device that collects emotion response information regarding the result of a user’s selection of emotion words, behavioral response information related to the user’s behavioral characteristics, and diagnostic environment information regarding an environment in which the emotion response information is input, determines the user’s emotional state from the emotion response information, the behavioral response information, and the diagnostic environment information, and generates an emotion analysis result from the emotional state, the emotion response information, and the emotion text input by the user through an emotion analysis UI to which an NLP model is applied, an external server that provides data corresponding to a request from the emotion coaching analysis device, and a user terminal that displays the emotion analysis result on a display.

[0027] According to another embodiment, the behavioral response information includes typing speed, scroll speed, input latency, repeated click frequency, input error frequency, repeated sentence deletion frequency, repeated word repetition frequency, and same screen dwell time measured during the process in which the user inputs the emotional response information, and the diagnostic environment information includes the input time, temperature, humidity, weather, location, illuminance, and digital environment congestion at which the emotional response information was input, and the digital environment congestion may indicate the frequency of notifications perceived by the user through digital means.

[0028] Various aspects and features of the invention are defined in the appended claims. Combinations of features of the dependent claims may be appropriately combined with features of the independent claims, not only as explicitly presented in the claims.

[0029] Additionally, one or more selected features of any one embodiment described in this disclosure may be combined with one or more selected features of any other embodiment described in this disclosure, and such alternative combination of features is possible if it at least partially alleviates one or more technical problems discussed in this disclosure or at least partially alleviates technical problems discernible from this disclosure by a person skilled in the art, and furthermore, such combination is possible if the specific combination or permutation of the embodiment features thus formed is not understood by a person skilled in the art to be incompatible.

[0030] In any described example implementation, two or more physically distinct components may alternatively be integrated into a single component if such integration is possible, provided that the same function is performed by the single component thus formed. Conversely, a single component of any embodiment described in this disclosure may alternatively be implemented by two or more distinct components that achieve the same function, where appropriate.

[0031] The objective of certain embodiments of the present invention is to solve, mitigate, or eliminate at least one of the problems and / or disadvantages associated with the prior art, at least partially. Certain embodiments are intended to provide at least one of the advantages described below. Effects of the invention

[0033] The apparatus and method according to various embodiments of the present disclosure can diagnose the user's emotional state by synthesizing the user's emotional word selection information and information collected during the emotional input process, and provide customized emotional coaching accordingly.

[0034] In addition, the apparatus and method according to various embodiments of the present disclosure enable more accurate identification of the user's emotional state by integrating emotional response information, behavioral response information, and environmental information to determine the emotional state.

[0035] In addition, the apparatus and method according to various embodiments of the present disclosure enable the reliability of emotion analysis to be increased by quantifying the emotional state by reflecting the emotion word selection pattern and intensity.

[0036] In addition, the apparatus and method according to various embodiments of the present disclosure enable the reflection of even unconscious emotional expressions by analyzing behavioral signals, such as typing and scrolling, during the user input process.

[0037] In addition, the apparatus and method according to various embodiments of the present disclosure enable consideration of external factors influencing emotion formation by including environmental information at the time of emotion input in the analysis.

[0038] In addition, the apparatus and method according to various embodiments of the present disclosure enable a user to clearly recognize their own emotions by structurally analyzing emotional text through an NLP-based UI.

[0039] In addition, the apparatus and method according to various embodiments of the present disclosure enable personalized emotional coaching by synthesizing emotional states and analysis results to provide context-based emotional feedback to the user.

[0040] Furthermore, the apparatus and method according to various embodiments of the present disclosure go beyond simply classifying emotions to provide an emotion coaching effect that enhances a user's metacognitive ability by providing questions and feedback that enable the user to explore the causes of emotions on their own. Through this, the user can recognize recurring emotional patterns and establish appropriate coping strategies in stressful situations, thereby increasing psychological self-efficacy in the long term.

[0041] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below. Brief explanation of the drawing

[0043] The accompanying drawings, included as part of the detailed description to aid in understanding the embodiments, provide various embodiments and explain the technical features of the various embodiments together with the detailed description. FIG. 1 illustrates an emotion coaching analysis system according to various embodiments of the present disclosure. FIG. 2 illustrates the configuration of an emotion coaching analysis device in an emotion coaching analysis system according to various embodiments of the present disclosure. FIG. 3 illustrates a schematic diagram of an emotion coaching process in an emotion coaching analysis system according to various embodiments of the present disclosure. FIG. 4 illustrates a schematic diagram regarding the process of an emotion coaching analysis device generating an emotion analysis result for emotion coaching in an emotion coaching analysis system according to various embodiments of the present disclosure. FIG. 5 illustrates a flowchart regarding the operation method of an emotion coaching analysis device in an emotion coaching analysis system according to various embodiments of the present disclosure. Specific details for implementing the invention

[0044] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this disclosure. Terms used in this disclosure that are defined in a general dictionary may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure. In some cases, even terms defined in this disclosure are not to be interpreted to exclude the embodiments of this disclosure.

[0045] In the various embodiments of the present disclosure described below, a hardware-based approach is described as an example. However, since the various embodiments of the present disclosure include techniques using both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0046] The present disclosure relates to a system that provides personalized emotion coaching by taking into account the user's choice of emotion words. Specifically, the present disclosure describes a technology for diagnosing a user's emotional state and providing emotion coaching based on the diagnosis by synthesizing emotion word selection information and information collected during the emotion input process.

[0047] Hereinafter, various embodiments are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, since the technical concept of the present disclosure can be modified and implemented in various forms, it is not limited to the embodiments described in this specification. In describing the embodiments disclosed in this specification, if it is determined that specifically describing related prior art could obscure the essence of the technical concept of the present disclosure, such specific description of prior art is omitted. Identical or similar components are assigned the same reference number, and redundant descriptions thereof are omitted.

[0048] When an element is described in this specification as being "connected" to another element, this includes not only cases where they are "directly connected" but also cases where they are "indirectly connected" with another element in between. When an element is described as "comprising" another element, this means that, unless specifically stated otherwise, it does not exclude other elements in addition to the other elements but may include additional elements.

[0049] Some embodiments may be described 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. The functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks of the present disclosure may be implemented as algorithms executed on one or more processors. The functions performed by the functional blocks of the present disclosure may be performed by a plurality of functional blocks, or the functions performed by a plurality of functional blocks in the present disclosure may be performed by a single functional block. Additionally, the present disclosure may employ prior art for electronic configuration, signal processing, and / or data processing, etc.

[0050] Additionally, in this disclosure, expressions such as "greater than" or "less than" have been used to determine whether specific conditions are satisfied or fulfilled; however, this is merely for illustrative purposes and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" with "less than," and conditions described as "greater than and less than" with "greater than and less than."

[0051] In the following, emotion and emotional state should be understood as technical concepts that are not merely limited to emotional feelings subjectively perceived and verbally expressed by the user, but include the results calculated and interpreted by the system based on various input signals. That is, the emotional state can be calculated by reflecting not only explicitly provided data, such as emotion words selected or written by the user, user input adjusting the intensity of emotion, and sentences expressing emotion, but also data regarding response characteristics observed during the input process.

[0052] Emotional states can be expressed as quantified values ​​calculated within the system by combining the aforementioned explicit and implicit emotional elements, and can be stored and processed in the form of vector structures, scalar scores, probability-based emotion distributions, or multidimensional emotion spectra. These quantified emotional expressions go beyond simply recording emotions to enable the calculation of emotional intensity, directionality, variability, and correlations between complex emotions, thereby allowing them to be utilized in automated emotion analysis and coaching processes. Furthermore, emotional states can be used to compare a user's emotional state over a specific time axis or to analyze emotional patterns compared with clusters of similar users. They can also serve as input values ​​for subsequent algorithms that evaluate trends in emotional state change, consistency of emotional responses, or stability of the emotional state. In other words, the emotion or emotional state defined in this disclosure may refer to functions as a quantitative, technical emotional expression based on user behavior and environmental data, rather than a simple description of emotion.

[0054] FIG. 1 illustrates an emotion coaching analysis system (100) according to various embodiments of the present disclosure.

[0055] The emotion coaching analysis system (100) indicates a system that provides emotion coaching to a user by diagnosing the user's current emotional state quantitatively and qualitatively and applying it to a conversational model. Specifically, the emotion coaching analysis system (100) indicates a system that collects and analyzes the user's emotional input process in real time and provides personalized emotion coaching based thereon. According to the emotion coaching analysis system (100), when a user provides information regarding their emotional state, an analysis service provider can diagnose the user's emotional state and provide feedback thereon, thereby supporting the user to feel emotional changes. According to one embodiment of the present disclosure, the emotion coaching analysis system (100) may include a user terminal (110), an external server (130), an emotion coaching analysis device (150), and a network (170).

[0056] The user terminal (110) indicates an electronic device capable of interacting with the user and can perform the function of collecting emotion words, emotion text, and input patterns on the emotion UI selected by the user through an interface displayed on the display. Additionally, the user terminal (110) can measure behavioral response data such as typing speed, input delay, scroll speed, frequency of repeated clicks, and screen dwell time occurring during the emotion response input process, and can collect diagnostic environment information such as the time of emotion input, illumination, and frequency of notification reception, and transmit it to the emotion coaching analysis device (150). Additionally, the user terminal (110) can receive the emotion analysis results and display them on the screen, and can collect additional question response inputs from the user. According to one embodiment of the present disclosure, the user terminal (110) may include a fixed terminal implemented as a computer device or a mobile terminal. Specifically, the user terminal (110) may indicate a smartphone, mobile phone, navigation, computer, laptop, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), or tablet PC.

[0057] The external server (130) directs a server device that stores additional data or linked data necessary for emotion coaching analysis. The external server (130) may store or manage an emotion word dictionary, an emotion weight table, behavioral response reference information, user history data, or a database regarding a group of empathetic users with emotion patterns similar to the user. Additionally, the external server (130) may improve the accuracy of emotion judgment and the quality of analysis results by providing emotion model updates, pre-trained NLP models, emotion state mapping tables, etc., to the emotion coaching analysis device (150). According to one embodiment of the present disclosure, the external server (130) may be implemented as a computer device or a plurality of computer devices that provide commands, code, files, content, services, etc.

[0058] The emotion coaching analysis device (150) refers to an electronic device that performs the function of determining a user's emotional state by comprehensively processing emotional response information, behavioral response information, and diagnostic environment information, and providing emotion coaching by applying an NLP model through an emotion analysis UI. The emotion coaching analysis device (150) can determine a user's emotional state by calculating a main emotional state based on the emotional word selection patterns and intensity of the emotional response information, determining a sub-emotional state using behavioral response information and diagnostic environment information, and weighted summing these. Subsequently, the emotion coaching analysis device (150) can generate a questionnaire, obtain user answers, and derive emotional motivation through an NLP-based emotion analysis UI, and generate an emotion analysis result including the direction of emotional state change and appropriate coaching insights, and provide it to the user. According to one embodiment of the present disclosure, the emotion coaching analysis device (150) may also include a fixed terminal implemented as a computer device or a mobile terminal.

[0059] As illustrated in FIG. 1, the components of the emotion coaching analysis system (100) can be connected via a network (170). According to one embodiment of the present disclosure, the network (170) refers to a connection structure capable of exchanging information between each node, such as a plurality of terminals and servers. Examples of such networks include, but are not limited to, RF, 3GPP (3rd generation partnership project) networks, LTE (long term evolution) networks, 5GPP (5th generation partnership project) networks, WIMAX (world interoperability for microwave access) networks, the internet, LAN (local area network), Wireless LAN (wireless local area network), WAN (wide area network), PAN (personal area network), Bluetooth networks, NFC networks, satellite broadcasting networks, analog broadcasting networks, DMB (digital multimedia broadcasting) networks, etc.

[0060] According to the emotion coaching analysis system (100), a user terminal (110), an external server (130), and an emotion coaching analysis device (150) can interact with each other to collect user emotion data and provide analysis results. Here, the emotion coaching analysis device (150) can estimate the emotion state by integrally processing the collected emotion response information, behavioral response information, and diagnostic environment information, and can analyze emotion text in a structured manner through a natural language processing-based user interface. Below, the specific configuration and operation of the emotion coaching analysis device (150) are described in detail.

[0061] Here, emotional coaching may refer to a series of psychological training processes in which the user clearly recognizes their emotions, expresses them appropriately, resolves negative emotions, or strengthens positive emotions. The present system (100) can automate and perform the entire process of such emotional coaching. The emotional coaching analysis device (150) is not merely a data processing device, but can operate as a virtual emotional coaching agent dedicated to the user's mental care. However, it is not necessarily limited to these matters.

[0063] FIG. 2 illustrates the configuration (200) of an emotion coaching analysis device (150) in an emotion coaching analysis system (100) according to various embodiments of the present disclosure.

[0064] Terms such as '...part' and '...unit' used below refer to a unit that processes at least one function or operation, and this may be implemented as hardware or software, or a combination of hardware and software. The emotion coaching analysis device (150) may include a memory (210), a processor (220), a communication unit (230), an input / output interface (240), and a display unit (250).

[0065] The memory (210) temporarily or permanently stores data such as basic programs, application programs, and setting information for the operation of the emotion coaching analysis device (150). The memory (210) may include a non-perishable permanent mass storage device such as RAM (random access memory), ROM (read only memory), and a disk drive, but the present invention is not limited thereto. These software components may be loaded from a computer-readable recording medium separate from the memory (210) using a drive mechanism. This separate computer-readable recording medium may include computer-readable recording media such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. According to an embodiment, the software components may be loaded into the memory (210) through a communication unit (230) rather than a computer-readable recording medium. Additionally, the memory (210) may provide stored data upon request from the processor (220).

[0066] The processor (220) controls the overall operations of the emotion coaching analysis device (150). For example, the processor (220) can control the transmission and reception of signals through the communication unit (230). Additionally, the processor (220) can be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (220) by the memory (210) or the communication unit (230). For example, the processor (220) can be configured to execute instructions received according to program code stored in a recording device such as the memory (210). For example, the processor (220) can control the emotion coaching analysis device (150) to perform operations according to various embodiments described below.

[0067] According to one embodiment of the present disclosure, a processor (220) collects emotional response information regarding the result of selecting an emotional word of the user, behavioral response information regarding the user's behavioral characteristics, and diagnostic environment information regarding the environment in which the emotional response information is input, determines the emotional state of the user from the emotional response information, behavioral response information, and diagnostic environment information, and controls the generation of an emotional analysis result from the emotional state, emotional response information, and emotional text input by the user through an emotional analysis UI to which an NLP model is applied, and transmits the emotional analysis result to a user terminal.

[0068] Additionally, the processor (220) can implement an artificial neural network model structure. That is, the artificial neural network model can be implemented in hardware or software through the processor (220). The artificial neural network can be trained using big data related to the user's emotional state. This training may be performed, for example, on the emotion coaching analysis device (150) itself to which the artificial intelligence model is applied, or through a separate training server. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above.

[0069] An artificial neural network model may include multiple artificial neural network layers. The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, or a combination of two or more of these, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0070] Throughout this specification, a neural network may be composed of a set of interconnected computational units referred to as nodes. These nodes may also be referred to as neurons. An artificial neural network is composed of a plurality of nodes, and the nodes may be interconnected by one or more links. Within the neural network, two or more nodes connected via links may form a relative relationship between an input node and an output node. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As previously described, the input node versus output node relationship may be generated around links. One or more output nodes may be connected to a single input node via links. In an input node and output node relationship connected via a single link, the value of the output node may be determined based on data input to the input node. Here, the nodes interconnecting the input node and the output node may have weights. The weights may be variable and may be varied by a user or an algorithm to enable the neural network to perform the desired function.

[0071] The artificial neural network according to the present disclosure may be implemented as an NLP model, and the NLP model may refer to a publicly available large language model (LLM) capable of inferring relationships between words within a large amount of text data. Since the NLP model can analyze and extract meaningful information from text, it is currently being operated in various forms such as machine translation, business classification, and chatbots. The NLP model according to the present disclosure may refer to a model that provides a sentiment analysis UI (user interface), and may refer to a model trained to generate and output a sentiment analysis result for a user from the user's sentiment state and sentiment response information, and the sentiment text entered by the user.

[0072] The communication unit (230) performs functions for transmitting and receiving signals through a wireless channel. All or part of the communication unit (230) may be referred to as a transmitting unit, a receiving unit, or a transmitting and receiving unit. The communication unit (230) may provide a function for the emotion coaching analysis device (150) and at least one other node to communicate with each other through a communication network. According to one embodiment of the present disclosure, when the processor (220) of the emotion coaching analysis device (150) generates a request signal according to program code stored in a recording device such as a memory (210), the request signal may be transmitted to at least one other node through a communication network under the control of the communication unit (230). Conversely, a control signal, command, content, file, etc. provided under the control of the processor of at least one other node may be received by the emotion coaching analysis device (150) through the communication unit (230).

[0073] The input / output interface (240) may be a means for interfacing with an input / output device (not shown). In this case, the input device may be provided in the form of, for example, a device such as a keyboard or a mouse, and the output device may be provided in the form of a device such as a display unit for displaying images. As another example, the input / output interface (240) may be a means for interfacing with a device in which the functions for input and output are integrated into one, such as a touchscreen. Specifically, the processor (220) of the emotion coaching analysis device (150) may display a service screen or content configured using data provided by a server in processing instructions of a computer program loaded in memory (210) on a display through the input / output interface (240). According to one embodiment of the present disclosure, the input / output interface (240) may include a means for interfacing with a display unit (250). The input / output interface (240) can receive user input for a web browsing window displayed on the display unit (250), and can receive output data to be output through the display unit (250) in response to the aforementioned user input from the processor (220).

[0074] The display unit (250) indicates a display module comprising one or more displays. Each of the one or more displays included in the display unit (250) may individually display independent content, and the one or more displays described above may be combined to display a single content. According to one embodiment of the present disclosure, the one or more displays included in the display unit (250) may include physically separated multiple displays, may be physically combined multiple displays, or may be displays capable of dividing and using a single screen.

[0076] FIG. 3 illustrates a schematic diagram (300) of an emotion coaching process in an emotion coaching analysis system (100) according to various embodiments of the present disclosure.

[0077] The emotion coaching process is a procedure in which an emotion coaching analysis device (150) provides emotion coaching to a user, and can be divided into an emotion diagnosis process (310), an EDS mapping process (320), and a REALS model analysis process (330).

[0078] The emotion diagnosis process (310) instructs a process to collect emotion-related data provided by the user and to quantitatively and qualitatively identify the current emotional state based on this data. In the emotion diagnosis process (310), the emotion coaching analysis device (150) receives the result of selecting emotion words, the input for adjusting emotion intensity, and visual arrangement information in the form of an emotion pie, which are input through the user terminal (110), and analyzes the number of emotion words, the distribution of emotions, and the balance between emotions to derive the structural characteristics of the emotional state perceived by the user. Additionally, by calculating the ratio of positive or negative emotions among emotion words, the excessive dominance of a specific emotion, and the diversity of emotional expressions, the user's level of emotion awareness and psychological balance can be diagnosed.

[0079] Additionally, the emotion diagnosis process (310) may include an extended analysis function to consider additional information that influences the formation of the user's emotional state, in addition to explicit emotional response information. In particular, by reflecting unintentional behavioral response information such as typing speed, input delay, scroll speed, and frequency of typos, as well as diagnostic environment information such as the time, place, lighting, weather conditions, and frequency of digital notifications at the time of emotion input, the difference between the emotion perceived by the user and the actual emotional response can be detected. Through this, the emotion coaching analysis device (150) can produce a more accurate emotional state that considers unconscious emotional signals along with the outwardly expressed emotion.

[0080] The EDS mapping process (320) directs a process for deriving psychological connections between emotion, desire, and self based on the results of the emotion diagnosis. The emotion coaching analysis device (150) uses the emotional state and emotional text extracted through the emotion diagnosis process as input variables to infer hidden motives or desires reflected by the emotional state and map them to background factors of the emotional experience. Through this, it can support the user in recognizing that the emotion they are facing is not merely an emotional reaction, but is connected to specific values ​​or psychological needs. Furthermore, the EDS mapping process (320) not only performs an interpretation that moves from emotion to desire, but can also analyze how the inferred desire or value is connected to the user's personal identity or disposition. For example, anger leads to a 'desire for respect' and can ultimately be combined with a 'self that values ​​fair treatment.' The emotion coaching analysis device (150) derives these connections by utilizing emotion-desire-value relationship tables and similar user patterns stored in a database, thereby structurally presenting the psychological destination that the user's emotion ultimately aims for.

[0081] The REALS model analysis process (330) directs the emotion coaching analysis device (150) to perform a five-stage coaching flow of recognition, expression, awareness, linking, and shift based on the emotional state and EDS mapping results. In the REALS model analysis process (330), the emotion coaching analysis device (150) utilizes a natural language processing engine to analyze user text and automatically performs the distinction between facts and interpretations, verification of emotional expressions, exploration of emotional motivations, and induction of perspective shifts. The system can support the user in viewing emotions more objectively and becoming aware of their background and causes through processes such as generating questions at each stage, interpreting user responses, and restructuring text.

[0082] The above REALS model analysis process (330) functions as the core emotion coaching engine of the present disclosure. Specifically, the emotion coaching analysis device (150) can analyze the emotion text entered by the user to determine whether the user perceives the current situation in a negatively distorted way, and can perform a Socratic question-and-answer algorithm to correct it.

[0083] For example, the coaching stage is the labeling stage where the user names their emotions, A distancing stage that separates the emotion from the situation in which it occurred to objectify it, The stage of empathy and acceptance that acknowledges the validity of emotions, It may include a stage of exploring the underlying need that triggered the emotion and seeking alternatives to resolve it in a positive direction. However, it is not necessarily limited to this stage.

[0084] The emotion coaching analysis device (150) can automate these psychological counseling techniques through text mining and generative AI models and provide them to the user in the form of real-time chat.

[0085] The coaching content provided here may include specific behavioral activation missions based on psychological theories, rather than simple advice. For example, if the emotional state (440) is analyzed as 'high stress' and 'low self-acceptance,' the emotional coaching analysis device (150) may provide immediate action guides such as 'saying a kind word to myself today' or '5 minutes of mindful breathing.'

[0086] Additionally, the emotion coaching analysis device (150) can generate a step-by-step coaching curriculum based on the user's emotional change trends. Instead of one-time coaching, it establishes an emotion care plan on a weekly or monthly basis to perform habit-forming coaching that helps the user continuously improve their emotional habits. The content provided during this process may include meditation audio, cognitive behavioral therapy (CBT)-based worksheets, writing templates for emotional relief, etc. However, it is not necessarily limited to these components.

[0087] The emotion coaching process described in FIG. 3 may refer to a logical structure for the emotion coaching analysis device (150) to perform the entire process step-by-step, from interpreting the emotion state to inducing behavioral change. Below, the specific components and algorithm processing flow of the emotion coaching analysis device (150), which generates an emotion state based on user response information and collected information, and produces an emotion analysis result through text analysis and question-and-answer processes, are described in detail.

[0089] FIG. 4 illustrates a schematic diagram (400) regarding the process in which an emotion coaching analysis device (150) generates an emotion analysis result (460) for emotion coaching in an emotion coaching analysis system (100) according to various embodiments of the present disclosure.

[0090] The emotion coaching analysis device (150) can diagnose the user's emotions using emotion response information (410), behavioral response information (420), and diagnostic environment information (430) to determine the emotional state (440). Subsequently, the emotion coaching analysis device (150) can communicate with the user through an emotion analysis UI and collect emotion text (450) regarding the emotions described by the user. Based on an NLP model, it can generate an emotion analysis result (460) that explains the user's emotional state by synthesizing the emotional state (440), emotion text (450), and emotion response information (410). By transmitting the generated emotion analysis result (460) to the user terminal (110), the emotion coaching analysis device (150) can support the user in recognizing their emotional state and receiving a program for emotional change.

[0091] Specifically, the emotion coaching analysis device (150) collects emotion response information (410) regarding the result of the user’s selection of emotion words, behavioral response information (420) regarding the user’s behavioral characteristics, and diagnostic environment information (430) regarding the environment in which the emotion response information is input. The emotion coaching analysis device (150) may provide the user with an emotion request interface that requests the user to select and place at least one of a plurality of predefined emotion words, and through the emotion request interface, it may obtain emotion response information (410) regarding the result of the selection and placement of emotion words, behavioral response information (420) regarding the user’s behavioral response measured during the emotion selection process, and diagnostic environment information (430) regarding the emotion selection environment. That is, the user terminal (110) may provide the emotion request interface to the user and generate emotion response information (410) regarding the response content entered by the user, behavioral response information (420) regarding the behavioral characteristics confirmed during the response input process, and diagnostic environment information (430) regarding external environmental factors confirming the emotion request interface, and transmit them to the emotion coaching analysis device (150).

[0092] The emotion response information (410) may indicate the result of a user selecting and arranging some of a predefined set of emotion words according to the emotion request interface. The emotion request interface may display a predefined set of words, and the emotion response information (410) may indicate the result of a user selecting a pre-set number of words that match their emotional state from among the words and arranging them by adjusting the size of each word.

[0093] Behavioral response information (420) may indicate behavioral characteristics identified during the process of a user inputting emotional response information (410). According to one embodiment of the present disclosure, the behavioral response information (420) may include typing speed, scroll speed, input delay time, repeated click frequency, input error frequency, sentence deletion repetition frequency, same word repetition frequency, and same screen dwell time measured during the process of a user inputting emotional response information (410).

[0094] Diagnostic environment information (430) may indicate information regarding the temporal and spatial environment in which the user inputs emotional response information (410). According to one embodiment of the present disclosure, diagnostic environment information (430) may include the input time, temperature, humidity, weather, location, illuminance, and digital environment congestion level at which the emotional response information (410) was input. Here, the digital environment congestion level may indicate the frequency of notifications perceived by the user through digital means. That is, the digital environment congestion level may indicate the average daily number of notifications to the user terminal (110).

[0095] The emotion coaching analysis device (150) determines the user's emotional state (440) from emotional response information (410), behavioral response information (420), and diagnostic environment information (430). The emotion coaching analysis device (150) can diagnose the user's emotional state (440) by synthesizing the collected information and classifying and analyzing the user's emotions by characteristic.

[0096] The emotional state (440) may indicate a matrix vector in which the user's emotional characteristics are classified by type and expressed as indices. That is, the emotional state (440) may indicate a vector in which the user's emotional characteristics are quantified, such as whether the user's emotion is positive or negative, whether aroused or stable, and the level of stress. According to one embodiment of the present disclosure, the emotional state may indicate a matrix vector of size 1 x 5 including a positive emotion index, an arousal index, a stress index, an avoidance tendency index, and a self-acceptance index, and all indices included in the emotional state (440) may be expressed as numerical values ​​greater than 0 and less than 1.

[0097] To determine the emotional state (440), the emotional coaching analysis device (150) can calculate the main emotional state according to the intensity value of each type of emotion using emotional response information (410), calculate the sub-emotional state according to the state of an empathetic user similar to the user using behavioral response information (420) and diagnostic environment information (430), and determine the emotional state (440) by weighted summing the main emotional state and the sub-emotional state.

[0098] To this end, the emotion coaching analysis device (150) calculates an emotion intensity value by considering the selection frequency and batch size of each emotion word included in the emotion response information (410). The emotion coaching analysis device (150) can calculate the emotion intensity value of a word corresponding to the size of the emotion word selected by the user. Here, the emotion intensity value may be expressed as a numeric value greater than 0 and less than 1, and the emotion coaching analysis device (150) can calculate the emotion intensity value by extracting an intensity value corresponding to the size of the selected emotion word from a table indicating intensity values ​​by word size that is stored in advance in memory (210).

[0099] Subsequently, the emotion coaching analysis device (150) determines the main emotion state by applying a weight set corresponding to each emotion word to the emotion intensity value. Since multiple emotion words provided through the emotion request interface are predefined, the emotion coaching analysis device (150) can store in memory (210) a weight set indicating how much each emotion word influences the degree of positivity, the degree of arousal, the degree of stress, the degree of avoidance tendency, and the degree of self-acceptance. Here, all weights included in the weight set can be expressed as numerical values ​​greater than 0 and less than 1. For each emotion word selected according to the emotion response information (410), the emotion coaching analysis device (150) can apply a weight set to the emotion intensity value according to size to compute a 1 X 5 vector composed of parameters identical to the emotion state (440), and can compute a 1 X 5 main emotion state by summing all input emotion words and calculating the average.

[0100] After calculating the main emotional state, the emotion coaching analysis device (150) can select the empathy user with the highest state similarity to the user by using behavioral response information (420) and diagnostic environment information (430). The emotion coaching analysis device (150) can represent user characteristics indicating the user's emotions as a vector by listing parameters included in the behavioral response information (420) and diagnostic environment information (430). According to one embodiment of the present disclosure, user characteristics may indicate a vector including typing speed, scroll speed, input delay time, repeated click frequency, input error frequency, sentence deletion repetition frequency, same word repetition frequency, same screen dwell time, input time, temperature, humidity, weather, location, illuminance, and digital environment congestion.

[0101] Additionally, the emotion coaching analysis device (150) may request an external server (130) to receive comparison characteristics regarding the characteristics of multiple other users utilizing the emotion coaching analysis service. Here, the comparison characteristics may indicate a vector composed of parameters identical to the user characteristics for each of the multiple other users.

[0102] Afterward, the emotion coaching analysis device (150) can calculate the state similarity between each of the comparison characteristics and the user characteristic. Here, the state similarity can be determined based on <Equation 1>.

[0103]

[0104] Referring to <Mathematical Formula 1>, Sta_Sim i ε is the state similarity between the user characteristic and the i-th comparison characteristic, c1 is a preset constant greater than 0 and less than 1 indicating the importance of time and place, w is a value of 0 or 1 indicating whether the weather is the same for the user characteristic and the i-th comparison characteristic, n wn is the number of weather types, p is a value of 0 or 1 indicating whether the location is the same for the user characteristic and the i-th comparison characteristic, n p is the number of types of places, M f is the number of elements with the same value for the user characteristic and the i-th comparison characteristic, M g is the number of elements with different values ​​for the user characteristic and the i-th comparison characteristic, M is the number of elements represented by numbers within the user characteristic, CerU k is the value of the k-th element within the user attribute, SymU k It can indicate the value of the k-th element within the comparison feature.

[0105] The emotion coaching analysis device (150) can extract the comparison characteristic with the greatest state similarity among the comparison characteristics and select the user corresponding to the corresponding comparison characteristic as the empathy user. Subsequently, the emotion coaching analysis device (150) checks the empathy emotional state of the empathy user and determines the sub-emotional state. The emotion coaching analysis device (150) can request an external server (130) to receive the empathy emotional state indicating the emotional state of the empathy user and determine it as the sub-emotional state.

[0106] The emotion coaching analysis device (150) determines the emotion state by weighted summing the main emotion state and the sub emotion state. Each of the main emotion state and the sub emotion state can be expressed as a numerical value greater than 0 and less than 1 with respect to the positive emotion index, arousal index, stress index, avoidance tendency index, and self-acceptance index, and the emotion coaching analysis device (150) can determine the emotion state (440) by summing them while considering a preset weighting ratio. According to one embodiment of the present disclosure, the weighting ratio can be set to 7:3, and the emotion coaching analysis device (150) can calculate the emotion state (440) by multiplying the main emotion state and the sub emotion state by 0.7 and 0.3, respectively, and summing them.

[0107] After determining the emotional state (440), the emotional coaching analysis device (150) generates an emotional analysis result (460) from the emotional state (440), emotional response information (410), and emotional text (450) entered by the user through an emotional analysis UI to which an NLP model is applied. The emotional coaching analysis device (150) can control the emotional analysis UI to be displayed on the user terminal (110) and can exchange data with the user terminal (110) through the emotional analysis UI. In this process, the emotional coaching analysis device (150) can collect emotional text (450) regarding the user's own emotions described by the user and can perform emotional analysis on the user through step-by-step coaching.

[0108] First, the emotion coaching analysis device (150) separates the content included in the emotion text (450) into factual content and interpretation content, and provides the separation result to the user. The emotion coaching analysis device (150) can separate factual content and interpretation content by analyzing the meaning of the content input as the emotion text (450) using an NLP module. For example, if the emotion text (450) contains the content "I am angry because the team leader ignored me," the emotion coaching analysis device (150) can separate "the team leader's behavior" as factual content and "being ignored" as interpretation content, and can present the separation result to the user through an emotion analysis UI. The emotion coaching analysis device (150) can support the user in recognizing their situation more objectively by checking the separation result.

[0109] Subsequently, the emotion coaching analysis device (150) selects a first questionnaire asking for the reason regarding the emotion response information (410) and provides expression feedback to the user in response to the first user response to the questionnaire. The emotion coaching analysis device (150) can identify the emotion words included in the emotion response information (410) and select a first questionnaire asking for the reason for selecting the emotion words. The emotion coaching analysis device (150) can provide the first questionnaire to the user through an emotion analysis UI and can provide emotion feedback by verifying the first user response to the first questionnaire. For example, if the emotion response information (410) includes 'anger', the emotion coaching analysis device (150) can select a first questionnaire such as 'How do you feel about yourself being angry?' and can identify secondary emotions such as shame or rejection through the first questionnaire. If the user rejects their negative emotions, the emotion coaching analysis device (150) can provide emotion feedback that induces acceptance of the emotions through a message such as 'Emotions are a natural signal'. Through this, the emotion coaching analysis device (150) can support the user in accepting emotions.

[0110] Subsequently, the emotion coaching analysis device (150) selects a second questionnaire that asks about the user's situation based on the emotion response information (410) and the first user response, and determines the user's emotional motivation based on the second user response to the second questionnaire. The emotion coaching analysis device (150) can determine whether the user accepts the emotion through the first user response and select a second questionnaire corresponding thereto. Additionally, the emotion coaching analysis device (150) can determine the user's emotional motivation based on the second user response to the second questionnaire. For example, if the user does not accept their emotion regarding the first user response, the emotion coaching analysis device (150) can select a question that links the emotion to a desire as the second questionnaire, such as, "It seems that the emotion of anger is telling you that there is something you want to protect. What is it?" The emotion coaching analysis device (150) receives the second user response to this second questionnaire and can extract the emotional motivation that has the greatest influence on the user's emotional state (440) based on the second user response.

[0111] Subsequently, the emotion coaching analysis device (150) synthesizes the emotional state (440), classification results, and emotional motivation to generate an emotion analysis result (460) that explains the user's emotions. The emotion coaching analysis device (150) can generate the emotion analysis result (460) by verifying the meaning of the emotional motivation and using an NLP model to generate an explanatory text that explains the emotional status according to the user's emotional state (440), an explanation of the emotional response information (410), and a recommended solution according to the emotional motivation. That is, the emotion analysis result (460) instructs text that diagnoses the user's emotional state, derives the cause according to the emotional motivation, and presents a solution thereto. The emotion coaching analysis device (150) can provide emotion coaching to the user by generating the emotion analysis result (460) using an NLP model and guiding the user.

[0112] The emotion coaching analysis device (150) can provide coaching content that suggests behavioral goals to the user beyond the emotion analysis result (460). After transmitting the emotion coaching analysis device (150) the emotion analysis result (460) to the user terminal (110), the device can determine coaching content that suggests the user's subsequent behavioral method by considering the emotional state (440) within the emotion analysis result (460).

[0113] To this end, the emotion coaching analysis device (150) can store multiple coaching contents corresponding to the range of indices included in the emotional state (440) in the memory (210). That is, the emotion coaching analysis device (150) can classify the positive emotion index, arousal index, stress index, avoidance tendency index, and self-acceptance index by numerical range according to predefined criteria for each, and store coaching contents corresponding to each numerical value. Subsequently, the emotion coaching analysis device (150) can select coaching contents corresponding to each of the indices included in the user's emotional state (440). Accordingly, five coaching contents can be selected corresponding to the five indices included in the emotional state (440). Subsequently, the emotion coaching analysis device (150) can transmit the selected coaching contents to the user terminal (110) to enable the user to immediately check the contents without a separate additional search.

[0114] The emotion coaching analysis device (150) can continuously update the emotion analysis results. After providing coaching content to a user, the emotion coaching analysis device (150) can receive a follow-up response from the user who viewed the coaching content. Here, the follow-up response is a correction index determined by the user themselves and may include a user positive emotion correction ratio, an arousal correction ratio, a stress correction ratio, an avoidance tendency correction ratio, and a self-acceptance correction ratio. After receiving the follow-up response, the emotion coaching analysis device (150) can update the emotion state (440) by applying the correction index included in the follow-up response to the emotion state (440). Subsequently, the emotion coaching analysis device (150) can generate an updated emotion analysis result by conducting an emotion analysis according to the same method using the updated emotion state. Through this, the emotion coaching analysis device (150) can generate an emotion analysis result that more accurately describes the characteristics of the user.

[0116] FIG. 5 illustrates a flowchart (500) regarding the operation method of an emotion coaching analysis device (150) in an emotion coaching analysis system (100) according to various embodiments of the present disclosure.

[0117] Referring to FIG. 5, in step (501), the emotion coaching analysis device (150) collects emotion response information (410) regarding the result of the user’s selection of emotion words, behavioral response information (420) regarding the user’s behavioral characteristics, and diagnostic environment information (430) regarding the environment in which the emotion response information is input. The emotion coaching analysis device (150) can collect emotion response information (410), behavioral response information (420), and diagnostic environment information (430) from the user terminal (110) through an emotion request UI.

[0118] According to one embodiment of the present disclosure, the emotional response information (410) may indicate the result of a user selecting and arranging some of a plurality of predefined emotional words. Additionally, the behavioral response information (420) includes typing speed, scroll speed, input delay time, repeated click frequency, input error frequency, sentence deletion repetition frequency, same word repetition frequency, and same screen dwell time measured during the process of the user inputting the emotional response information, and the diagnostic environment information (430) may include the input time at which the emotional response information was entered, temperature, humidity, weather, location, illuminance, and digital environment congestion. Here, the digital environment congestion may indicate the frequency of notifications perceived by the user through digital means.

[0119] In step (503), the emotion coaching analysis device (150) determines the user's emotional state (440) from emotional response information (410), behavioral response information (420), and diagnostic environment information (430).

[0120] According to one embodiment of the present disclosure, the emotion coaching analysis device (150) calculates an emotion intensity value by considering the selection frequency and batch size of each emotion word included in the emotion response information (410), determines a main emotion state by applying a weight set corresponding to each of the multiple emotion words to the emotion intensity value, selects the empathy user with the highest state similarity to the user by using behavior response information (420) and diagnostic environment information (430), determines a sub emotion state by confirming the empathy emotion state of the empathy user, and determines an emotion state by weighted summing the main emotion state and the sub emotion state.

[0121] In step (505), the emotion coaching analysis device (150) generates an emotion analysis result (460) from an emotion state (440), emotion response information (410), and emotion text (450) entered by the user through an emotion analysis UI to which an NLP model is applied.

[0122] According to one embodiment of the present disclosure, an emotion coaching analysis device (150) may divide the content included in an emotion text (450) into factual content and interpretation content, provide the division result to the user, select a first questionnaire asking for the reason for the emotion response information (410), provide expression feedback to the user in response to the first user response to the questionnaire, select a second questionnaire asking for the user's situation based on the emotion response information and the first user response, determine the user's emotion motivation based on the second user response to the second questionnaire, and generate an emotion analysis result (460) that explains the user's emotion by synthesizing the emotion state (440), the division result, and the emotion motivation.

[0123] In step (507), the emotion coaching analysis device (150) transmits the emotion analysis result (460) to the user terminal (110). By providing the emotion coaching analysis device (150) with the emotion analysis result (460), it can provide customized emotion coaching to the user.

[0124] The emotion coaching analysis device (150) may provide coaching content that suggests behavioral goals regarding a behavioral pattern to the user. According to one embodiment of the present disclosure, the emotion coaching analysis device (150) may, after transmitting the emotion analysis result (460) to the user terminal (110), determine coaching content that suggests behavioral goals to the user by considering the emotional state (440), and transmit the coaching content to the user terminal (110).

[0125] The emotion coaching analysis device (150) can update the emotion analysis result (460) using a follow-up response to the coaching content. According to one embodiment of the present disclosure, the emotion coaching analysis device (150) receives a follow-up response to the coaching content from the user terminal (110), applies a correction index included in the follow-up response to the emotion state to calculate an updated emotion state, and updates the emotion analysis result using the updated emotion state.

[0127] Methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software. A method according to an embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium, or may be implemented as a computer program stored on a computer-readable recording medium in combination with hardware.

[0128] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the embodiments described in the claims or specification of this disclosure.

[0129] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0130] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0131] In the specific embodiments of the present disclosure described above, the components included in the disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0132] Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, it is understood that various modifications are possible within the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof. Explanation of the symbols

[0134] 110 User terminal 130 External server 150 Emotion Coaching Analysis Device 170 Network 210 memory 220 processor 230 Communications Unit 240 Input / Output Interface 250 Display Unit 310 Emotion Diagnosis Process 320 EDS Mapping Process 330 REALS Model Analysis Process 410 Emotional Response Information 420 Behavioral Response Information 430 Diagnostic Environment Information 440 Emotional State 450 Sentiment Text 460 Sentiment Analysis Results

Claims

Claim 1 A method of operation of an emotion coaching analysis device comprises: collecting emotion response information regarding the result of a user’s selection of emotion words, behavioral response information related to the user’s behavioral characteristics, and diagnostic environment information regarding the environment in which the emotion response information is input; determining the user’s emotional state from the emotion response information, the behavioral response information, and the diagnostic environment information; generating an emotion analysis result from the emotional state, the emotion response information, and the emotion text input by the user through an emotion analysis UI (user interface) to which a natural language processing (NLP) model is applied; and transmitting the emotion analysis result to a user terminal, wherein the emotion response information indicates the result of the user selecting and arranging some of a plurality of predefined emotion words, and the step of determining the emotional state comprises: calculating an emotion intensity value by considering the selection frequency and arrangement size of each emotion word included in the emotion response information; determining a main emotion state by applying a weight set corresponding to each emotion word to the emotion intensity value; selecting an empathy user with the highest state similarity to the user using the behavioral response information and the diagnostic environment information; and determining a sub emotion state by confirming the empathy emotion state of the empathy user. and includes the step of determining an emotional state by weighted summing the main emotional state and the sub-emotional state, wherein the emotional state indicates a matrix vector including a positive emotion index, an arousal index, a stress index, an avoidance tendency index, and a self-acceptance index, the indices included in the emotional state are expressed as numerical values ​​greater than 0 and less than 1, and the state similarity is determined based on Equation 1 (Equation 1). The above Sta_Sim i ε is the state similarity between the user characteristic and the i-th comparison characteristic, c is a preset constant greater than 0 and less than 1 indicating the importance of time and place, w is a value of 0 or 1 indicating whether the weather is the same for the user characteristic and the i-th comparison characteristic, and n w n is the number of weather types, p is a value of 0 or 1 indicating whether the location is the same for the user characteristic and the i-th comparison characteristic, n p is the number of types of places, M f is the number of elements with the same value for the user characteristic and the i-th comparison characteristic, M g is the number of elements with different values ​​for the user characteristic and the i-th comparison characteristic, M is the number of elements represented by numbers within the user characteristic, CerU k is the value of the k-th element within the user attribute, SymU k A method comprising the steps of: indicating the value of the k-th element within a comparison characteristic, and generating the sentiment analysis result, wherein the step of generating the sentiment analysis result comprises: separating the content included in the sentiment text into factual content and interpretation content, and providing the separation result to the user; selecting a first questionnaire asking for the reason for the sentiment response information, and providing expression feedback to the user in response to the first user response to the questionnaire; selecting a second questionnaire asking for the situation of the user based on the sentiment response information and the first user response, and determining the sentiment motivation of the user based on the second user response to the second questionnaire; and generating a sentiment analysis result that explains the user's sentiment by synthesizing the sentiment state, the separation result, and the sentiment motivation. Claim 2 delete Claim 3 delete Claim 4 A method according to claim 1, further comprising: a step of determining coaching content that proposes a behavioral goal to the user in consideration of the emotional state after transmitting the emotion analysis result to the user terminal; and a step of transmitting the coaching content to the user terminal. Claim 5 A method according to claim 4, further comprising: receiving a follow-up response to the coaching content from the user terminal; applying a correction index included in the follow-up response to the emotional state to calculate an updated emotional state; and updating an emotional analysis result using the updated emotional state. Claim 6 An emotion coaching analysis device that collects emotion response information regarding the result of a user’s selection of emotion words, behavioral response information related to the user’s behavioral characteristics, and diagnostic environment information regarding the environment in which the emotion response information is input, determines the user’s emotional state from the emotion response information, the behavioral response information, and the diagnostic environment information, and generates an emotion analysis result from the emotional state, the emotion response information, and the emotion text input by the user through an emotion analysis UI to which an NLP model is applied; and an external server that provides data corresponding to a request from the emotion coaching analysis device. The system includes a user terminal that displays the emotion analysis results on a display, wherein the emotion response information indicates the result of the user selecting and arranging some of a plurality of predefined emotion words, and the emotion coaching analysis device calculates an emotion intensity value by considering the selection frequency and arrangement size of each emotion word included in the emotion response information, determines a main emotion state by applying a weight set corresponding to each emotion word to the emotion intensity value, selects an empathy user with the highest state similarity to the user using the behavioral response information and the diagnostic environment information, determines a sub emotion state by confirming the empathy emotion state of the empathy user, and determines an emotion state by weighted summing the main emotion state and the sub emotion state, wherein the emotion state indicates a matrix vector including a positive emotion index, an arousal index, a stress index, an avoidance tendency index, and a self-acceptance index, wherein the indices included in the emotion state are expressed as numerical values ​​greater than 0 and less than 1, and the state similarity is determined based on Equation 1 (Equation 1). The above Sta_Sim i ε is the state similarity between the user characteristic and the i-th comparison characteristic, c is a preset constant greater than 0 and less than 1 indicating the importance of time and place, w is a value of 0 or 1 indicating whether the weather is the same for the user characteristic and the i-th comparison characteristic, and n w n is the number of weather types, p is a value of 0 or 1 indicating whether the location is the same for the user characteristic and the i-th comparison characteristic, n p is the number of types of places, M f is the number of elements with the same value for the user characteristic and the i-th comparison characteristic, M g is the number of elements with different values ​​for the user characteristic and the i-th comparison characteristic, M is the number of elements represented by numbers within the user characteristic, CerU k is the value of the k-th element within the user attribute, SymU k The emotion coaching analysis device indicates the value of the k-th element within the comparison characteristic, and the emotion coaching analysis device distinguishes the content included in the emotion text into factual content and interpretation content, provides the distinction result to the user, selects a first questionnaire asking for the reason for the emotion response information, provides expression feedback to the user in response to the first user response to the questionnaire, selects a second questionnaire asking for the user's situation based on the emotion response information and the first user response, determines the user's emotion motivation based on the second user response to the second questionnaire, and generates an emotion analysis result explaining the user's emotion by synthesizing the emotion state, the distinction result, and the emotion motivation. Claim 7 In claim 6, the behavioral response information includes a typing speed, scroll speed, input delay time, repeated click frequency, input error frequency, sentence deletion repetition frequency, same word repetition frequency, and same screen dwell time measured during the process of the user inputting the emotion response information, and the diagnostic environment information includes the input time of inputting the emotion response information, temperature, humidity, weather, location, illuminance, and digital environment congestion, and the digital environment congestion indicates the frequency of notifications perceived by the user through digital means, an emotion coaching analysis system. Claim 8 In claim 6, the emotion coaching analysis device determines coaching content that proposes a behavioral goal to the user by considering the emotion state, and transmits the coaching content to the user terminal. Claim 9 An emotion coaching analysis system according to claim 8, wherein the emotion coaching analysis device receives a follow-up response to the coaching content from the user terminal, calculates an updated emotion state by applying a correction index included in the follow-up response to the emotion state, and updates the emotion analysis result using the updated emotion state.

Images