WO2024063573A1

WO2024063573A1 - Devices, methods and programs for analyzing mental states on basis of medical science and ingredient science

Info

Publication number: WO2024063573A1
Application number: PCT/KR2023/014425
Authority: WO
Inventors: 이기호
Original assignee: 주식회사 메디푸드플랫폼
Priority date: 2022-09-22
Filing date: 2023-09-21
Publication date: 2024-03-28
Also published as: KR102530616B1; KR20240041803A

Abstract

The present disclosure relates to a device and method for analyzing mental states on the basis of ingredient science, in which, by using received body values of a subject being analyzed, with respect to raw-material ingredients involved in the generation of various substances in the body affecting mental state items, raw-material state indices of the subject being analyzed, with respect to the substances in the body, are calculated and, on the basis of this, mental state item scores of the subject being analyzed are calculated, and thus, intake information or content necessary for improving the mental state items of the subject being analyzed can be prescribed.

Description

Mental state analysis devices, methods, and programs based on medical science and elemental science

The present disclosure relates to a method of analyzing a mental state, and more specifically, to a method of analyzing the mental state of an analysis subject based on medical science and elemental science.

Recently, interest in not only physical health but also mental health has increased significantly.

In the case of physical health, depending on the uncomfortable body part or disease, you may use prescribed medicine or take nutritional supplements or foods that can restore physical/physical health.

On the other hand, in the case of mental health, there is no specific solution or only known to require a visit to the hospital.

In fact, many research results have revealed that various body substances, such as hormones, affect various emotions of living things.

However, at present, there is no publicly available technology that can measure how much of these substances exist in a living organism's body and use this to analyze the mental state of the living organism and provide feedback.

The purpose of the embodiments disclosed in the present disclosure is to provide a method and device for analyzing mental states based on composition.

In addition, the embodiment disclosed in the present disclosure receives body numerical values of the analysis target for raw ingredients involved in the production of various body substances that affect mental state items, and inputs them into an artificial intelligence model to provide information on each body substance. We aim to provide a mental state analysis method and device based on composition that can calculate the raw material state index of the analysis object.

In addition, the embodiment disclosed in the present disclosure is a component that can calculate a score for each mental state item of the analysis object based on the degree of influence between each body substance and the mental state item including the psychological state or cognitive ability of the analysis object. We aim to provide a science-based mental state analysis method and device.

In addition, the embodiment disclosed in the present disclosure analyzes the score for each mental state item of the analysis subject calculated in this way, and provides intake information necessary to improve the mental state item of the analysis subject, or ingredients that can prescribe customized content. We aim to provide a science-based mental state analysis method and device.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

A mental state analysis method based on compositional science according to an embodiment of the present disclosure to solve the above-described problem is performed by a mental state analysis device, at least one mental state including the psychological state or cognitive ability of the analysis target. Receiving an analysis subject's body level test values for at least one raw ingredient involved in the production of each body substance affecting the item; Inputting the received body numerical test value into an artificial intelligence model to calculate a raw material condition index of the analysis target for each body substance based on a raw material condition index calculation algorithm; and calculating a comprehensive score for each of the one or more mental state items of the analysis subject, based on the degree of influence between each body substance and the at least one mental state item of the analysis subject.

In addition, the raw material condition index quantifies the degree to which the subject of analysis possesses in the body the raw materials needed to produce each of the body substances.

In addition, the raw material condition index calculation step calculates the raw material condition index of the analysis target for each body substance based on the received body numerical test value and a first weight set for each of the at least one raw material component. can do.

Additionally, the first weight is set according to the role or proportion of each of the at least one raw material component in producing the substance in the body.

In addition, the comprehensive score calculation step may be performed based on the calculated raw state index and a second weight set for the at least one mental state item of each body substance, the at least one mental state item of each body substance. Calculating a score for; and calculating the overall score for each of the one or more mental state items of the analysis subject, based on the score calculated for each body substance.

In addition, the mental state analysis device stores statistical data including test values of body values for a plurality of analysis subjects for the at least one raw material component, and the step of receiving the test values includes the step of receiving the body values. Comparing the test value with the statistical data to calculate a percentile for the test value of the analysis target; And based on the calculated percentile, it may include correcting the body level test value of the analysis target for each of the at least one raw material component.

In addition, in the mental state analysis device, a weight is set for each numerical range for the body numerical test value for the at least one raw material component, and the test value receiving step is performed to determine the value corresponding to the received body numerical test value. Based on the weight set for the range, the received body test value of the analysis target can be corrected.

In addition, based on the calculated overall score, a step of deriving intake information necessary for improving a specific mental state item of the analysis target may be further included.

In addition, the step of deriving the intake information may further include determining the specific mental state item requiring improvement among the at least one mental state item based on the calculated overall score.

In addition, intake information for each of the at least one raw material component may be generated according to the received body level test value and the role or proportion of each of the at least one raw material component in the production of the substance in the body.

In addition, the elemental science-based mental state analysis device according to an embodiment of the present disclosure for solving the above-mentioned problem is a device for analyzing psychological states in each body that affects at least one mental state item including the psychological state or cognitive ability of the object of analysis. A receiving unit that receives a test value of the body level of the analysis target for at least one raw material component involved in the production of a substance; And inputting the received body numerical test value into an artificial intelligence model to calculate the raw material condition index of the analysis object for each body substance based on a raw material condition index calculation algorithm, and calculating the raw material condition index of the analysis target for each body substance and the at least one and a processor that calculates a comprehensive score for each of the one or more mental state items of the analysis subject, based on the degree of influence between the mental state items.

In addition to this, a computer program stored in a computer-readable recording medium for execution to implement the present disclosure may be further provided.

In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the above-described problem solving means of the present disclosure, it provides the effect of providing a mental state analysis method and device based on component theory.

In addition, according to the means for solving the above-described problem of the present disclosure, the body numerical values of the analysis target are received for raw ingredients involved in the production of various substances in the body that affect mental state items, and the values are input into an artificial intelligence model for each The raw material condition index of the analysis target for substances in the body can be calculated.

In addition, according to the above-described problem solving means of the present disclosure, a score for each mental state item of the analysis object is calculated based on the degree of influence between each body substance and the mental state item including the psychological state or cognitive ability of the analysis object. can do.

In addition, according to the means for solving the above-described problem of the present disclosure, the score for each mental state item of the analysis object calculated in this way is analyzed, and intake information or customized content necessary for improving the mental state item of the analysis object is prescribed. can do.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below.

1 is a schematic diagram of a compositional theory-based mental state analysis system according to an embodiment of the present disclosure.

Figure 2 is a block diagram of a mental state analysis device based on component theory according to an embodiment of the present disclosure.

Figure 4 is a flowchart of a compositional analysis-based mental state analysis method according to an embodiment of the present disclosure.

Figure 3 is a diagram illustrating the production of serotonin through the involvement of various raw materials.

Figure 5 is a diagram illustrating test values of the body levels of the subject of analysis for a plurality of raw ingredients involved in the production of serotonin, dopamine, acetylcholine, GABA, and adrenaline.

Figure 6 is a raw material state index for each hormone calculated based on the body level test values of Figure 5, and is a diagram illustrating the degree of influence established between mental state items related to body substances (serotonin, dopamine, acetylcholine, GABA, and adrenaline). am.

Figure 7 is a diagram illustrating derivation of intake information suitable for the analysis subject according to the high and low raw material condition index calculated for body substances and the body level test values of each raw material component.

Figure 8 is a diagram illustrating the creation of a mental chart based on a comprehensive score calculated for one or more mental state items of the analysis target.

Figure 9 is a diagram illustrating the creation of a radial chart based on a comprehensive score calculated for one or more mental state items of the analysis target.

Figure 10 is a diagram illustrating the extraction and prescription of customized content that matches the analysis results of the analysis target from a memory where various mental state improvement content is stored.

Like reference numerals refer to like elements throughout this disclosure. The present disclosure does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present disclosure pertains is omitted. The term 'unit, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'unit, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may consist of one or multiple processors. At this time, one or more processors may be a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU or a VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU. One or more processors control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, when one or more processors are dedicated artificial intelligence processors, the artificial intelligence dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

Predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, created through learning means that the basic artificial intelligence model is learned using a large number of learning data by a learning algorithm, and predefined operation rules or artificial intelligence are set to perform the desired characteristics (or purpose). This means that a model is created. This learning may be performed on the device itself that performs the artificial intelligence according to the present disclosure, or may be performed through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

An artificial intelligence model may be composed of multiple neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and neural network calculation is performed through calculation between the calculation result of the previous layer and the plurality of weights. Multiple weights of multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, a plurality of weights may be updated so that loss or cost values obtained from the artificial intelligence model are reduced or minimized during the learning process. Artificial neural networks may include deep neural networks (DNN), such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, etc., but are not limited to the examples described above.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In this specification, the 'Mental State Analysis Device 100 based on Medical-Science and Ingredient Science according to the present disclosure includes all various devices that can perform computational processing and provide results to the user. . For example, the mental state analysis device 100 according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may take the form of any one.

As such, in the embodiment of the present disclosure, the mental state analysis device 100 may include a server device and may provide a mental state analysis service through the web or a mental state analysis application.

Here, the computer may include, for example, a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

The server device is a server that processes information by communicating with external devices, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.

The portable terminal 200 is, for example, a wireless communication device that guarantees portability and mobility, including Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), and Personal Handyphone System (PHS). ), 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, smart phone (Smart Phone), all types of handheld wireless communication devices, watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD), etc. It may include the same wearable device.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

Figure 1 is a schematic diagram of a component theory-based mental state analysis system 10 according to an embodiment of the present disclosure.

The mental state analysis device 100 according to an embodiment of the present disclosure analyzes input data using an AI Model, and generates and calculates output data according to the analysis results.

In an embodiment of the present disclosure, the input data is an internal body numerical test value measured for the analysis object, and the mental state analysis device 100 uses an artificial intelligence model to determine the raw material condition index for the analysis object based on the internal body numerical test value. can be calculated, and scores for mental state items can be calculated.

In addition, the mental state analysis device 100 can ultimately provide mental state analysis services such as providing mental state item analysis results for the analysis target, customized content prescription, and providing necessary intake information to the analysis target.

In the embodiments of the present disclosure, the subject of analysis may be a living organism, and may specifically include humans and animals.

Above, the mental state analysis system 10 according to an embodiment of the present disclosure has been briefly described, and will be described in more detail below with reference to other drawings.

Figure 2 is a block diagram of a component theory-based mental state analysis device 100 according to an embodiment of the present disclosure.

Referring to FIG. 2 , the mental state analysis device 100 according to an embodiment of the present disclosure includes a processor 110, a communication unit 130, a memory 150, and an input/output unit 170.

However, in some embodiments, the mental state analysis device 100 may include fewer or more components than the components shown in FIG. 2 .

The processor 110 uses a memory 150 to store data about an algorithm for controlling the operations of components within the mental state analysis device 100 or a program that reproduces the algorithm, and the data stored in the memory 150. It may be implemented with at least one processor 110 that performs the above-described operations. At this time, the memory 150 and the processor 110 may each be implemented as separate chips. Alternatively, the memory 150 and processor 110 may be implemented as a single chip.

In addition, the processor 110 may control any one or a combination of the above-described components in order to implement various embodiments according to the present disclosure described in the drawings below on the mental state analysis device 100. .

In an embodiment of the present disclosure, the receiver may receive an analysis target's body level test value for at least one raw ingredient involved in the production of each substance in the body that affects at least one mental state item.

In an embodiment of the present disclosure, when the mental state analysis device 100 is used alone, it can be applied as a configuration of a receiving unit, and when the mental state analysis device 100 is configured to include a server device, the receiving device includes the communication unit 130. ) may be applied or may include a communication unit 130.

The communication unit 130 can communicate with the user terminal 200, the medical staff's terminal 200, an external server (e.g., a server at a medical institution), etc., and can receive body numerical test values of the analysis target and transmit output data. This can provide mental state analysis results for the analysis target (user).

The communication unit 130 may include one or more components that enable communication with an external device, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module. It can be included.

Wired communication modules include various wired communication modules such as Local Area Network (LAN) modules, Wide Area Network (WAN) modules, or Value Added Network (VAN) modules, as well as USB (Universal Serial Bus) modules. ), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, wireless communication modules include GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), and UMTS (universal mobile telecommunications system). ), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, 6G, etc. may include a wireless communication module that supports various wireless communication methods.

The wireless communication module may include a wireless communication interface including an antenna and a transmitter that transmits communication signals. Additionally, the wireless communication module may further include a signal conversion module that modulates a digital control signal output from the control unit through a wireless communication interface into an analog wireless signal under the control of the control unit.

The short-range communication module is for short-range communication and includes Bluetooth (Bluetooth), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, and NFC (Near Field). Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technology can be used to support short-distance communication.

The memory 150 is a storage means of the mental state analysis device 100 according to an embodiment of the present disclosure, and may store various commands, algorithms, calculation formulas, and artificial intelligence models for executing a mental state analysis method.

In addition, in-body numerical test values of the analysis target received through the communication unit 130 under the control of the processor 110, raw material condition index, score, comprehensive score, analysis results, customized content prescription results, and derivation calculated for the analysis target. Details such as intake information, etc. may be stored.

The memory 150 can store data supporting various functions of the mental state analysis device 100 and a program for the operation of the control unit, and can store input/output data (e.g., music files, still images, and videos). etc.), a plurality of application programs (application programs or applications) running on the mental state analysis device 100, data for operation of the mental state analysis device 100, and commands can be stored. At least some of these applications may be downloaded from an external server via wireless communication.

The memory 150 includes a flash memory type, a hard disk type, a solid state disk type, an SDD type (Silicon Disk Drive type), and a multimedia card micro type. micro type), card type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically erasable) It may include at least one type of storage medium among programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. Additionally, the memory 150 is separate from the mental state analysis device 100, but may be a database connected by wire or wirelessly.

The input/output unit 170 of the mental state analysis device 100 according to an embodiment of the present disclosure may be comprised of an input unit and an output unit.

Accordingly, the mental state analysis device 100 receives and inputs, through an input unit, a numerical test value of the analysis target's body temperature for at least one raw material component involved in the production of each body substance affecting at least one mental state item. In addition, data and materials necessary for analyzing the mental state of the subject of analysis can be input or received through the input unit and the receiver.

In addition, the mental state analysis apparatus 100 can provide analysis results of the analysis target by outputting various data/information calculated/derived and generated data/information about the analysis target through the output unit.

*The input unit is for inputting video information (or signal), audio information (or signal), data, or information input from the user, and may include at least one of at least one camera, at least one microphone, and a user input unit. there is. Voice data or image data collected from the input unit can be analyzed and processed as a user's control command.

The input unit is for receiving information from the user. When information is input through the user input unit, the control unit can control the operation of the mental state analysis device 100 to correspond to the input information. This user input unit includes hardware-type physical keys (e.g., buttons, dome switches, jog wheels, jog switches, etc. located on at least one of the front, back, and sides of the mental state analysis device 100) and May include software-enabled touch keys. As an example, the touch key consists of a virtual key, soft key, or visual key displayed on a touch screen-type display unit through software processing, or is displayed on the touch screen. It may be composed of touch keys placed in other parts. Meanwhile, the virtual key or visual key can be displayed on the touch screen in various forms, for example, graphic, text, icon, video or these. It can be made up of a combination of .

The output unit is intended to generate output related to vision, hearing, or tactile sensation, and may include at least one of a display unit, an audio output unit, a haptip module, and an optical output unit. A touch screen can be implemented by forming a layered structure with the touch sensor or being integrated with the display unit. This touch screen can function as a user input unit that provides an input interface between the mental state analysis device 100 and the user, and at the same time, can provide an output interface between the mental state analysis device 100 and the user.

The mental state analysis apparatus 100 according to an embodiment of the present disclosure may further include a display unit, and the display unit displays (outputs) information processed by the mental state analysis apparatus 100. For example, the display unit displays execution screen information of an application program (for example, an application) running on the mental state analysis device 100, or UI (User Interface) and GUI (Graphic User Interface) information according to this execution screen information. It can be displayed.

The audio output unit may output audio data received through the communication unit 130 or stored in the memory 150, or output audio signals related to functions performed by the mental state analysis device 100. This sound output unit may include a receiver, speaker, buzzer, etc.

The mental state analysis apparatus 100 according to an embodiment of the present disclosure may further include an interface unit, and the interface unit serves as a passageway for various types of external devices connected to the mental state analysis apparatus 100. This interface unit connects devices equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module (SIM). It may include at least one of a port, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. The mental state analysis device 100 can perform appropriate control related to external devices connected to the interface unit.

Referring to FIG. 3, it will be explained that serotonin is produced through the involvement of various raw materials.

First, L-tryptophan is produced through proteins and auxiliary materials (zinc, B1, B6), and in this process, stomach acid (Stomach Acid) acts as an inhibitor that interferes with the production of L-tryptophan.

At this time, L-tryptophan is involved as a main ingredient in the production of serotonin.

Second, auxiliary materials (calcium, heme iron, B3, B9) are involved in L-tryptophan to produce 5-hydroxy-tryptophan, and in this process, Tryptophan Hydroxylase (enzyme) acts as an inhibitor that interferes with the production of 5-hydroxy-tryptophan. Get involved.

Third, SEROTONIN is produced by auxiliary materials (zinc, magnesium, B6, vitamin C) involved in 5-hydroxy-tryptophan, and in this process, Dopa Decarboxylase is involved as an inhibitor that interferes with the production of SEROTONIN.

In addition, serotonin produced in this way is commonly known to affect the happiness of the subject of analysis, but it also affects various mental state items such as satisfaction, concentration, memory, anxiety, excitement, motivation, comprehension, creativity, and calmness of the subject of analysis. It's crazy.

In an embodiment of the present disclosure, hormones such as serotonin, dopamine, acetylcholine, GABA, and adrenaline can be applied as the body substance, and any body substance that affects mental state items can be applied.

In addition, components involved in the production of each body substance are applied as raw materials, and these raw materials are applied as main materials, secondary materials, and inhibitors, and different weights can be set depending on their respective roles and contributions.

For example, main ingredients or secondary ingredients can be applied as a positive factor in the production of substances in the body, and inhibitors can be applied as a negative element that interferes with the production of substances in the body.

To help explain, as an additional example, assuming that the hormone is ceramics, the main material can be used as the main ingredient for making ceramics, the secondary materials can be fillers and additives, and the inhibitor can be cold water that interferes with making ceramics.

The compositional science-based mental state analysis method according to an embodiment of the present disclosure, which will be described below, includes weights set for various raw ingredients involved in producing such body substances, and body substances being analyzed in various mental state items of the analysis target. The mind of the subject of analysis is analyzed based on the degree of influence.

The mental state analysis device 100 receives a test value of the body level of the analysis target for at least one raw material component through the communication unit 130. (S100)

More specifically, the mental state analysis device 100 tests the body level of the analysis target for at least one raw material component involved in the production of each body substance affecting at least one mental state item through the communication unit 130. Receive a value.

At this time, the mental state item may include the psychological state or cognitive ability of the analysis target.

The psychological state may include states related to the psychology of the subject of analysis, such as happiness, satisfaction, anxiety, excitement, and calmness.

Cognitive abilities can include abilities related to the cognition of the object of analysis, such as concentration, memory, motivation, comprehension, and creativity.

However, a mental state item does not necessarily include either a psychological state or cognitive ability, and depending on the type of mental state item, it may be an item that includes both a psychological state and cognitive ability. Accordingly, the mental state item may include at least one of a psychological state and cognitive ability.

Referring to Figure 5, it is illustrated in the body level test value received for the analysis subject, and more specifically, the body level test of the analysis subject for each raw material component included in each of serotonin, dopamine, acetylcholine, GABA, and adrenaline. The values are illustrated.

In one embodiment, the mental state analysis device 100 may use the internal body numerical test values for the analysis target received in S100 as is, or may use them after correcting them.

*At this time, the processor 110 can use one of the two methods below to correct the body numerical test value.

① Method of correction based on the percentile position of the body numerical test values

The memory 150 stores statistical data including body level test values of a plurality of analysis subjects for the at least one raw material component.

S100 is a step of the processor 110 comparing the body numerical test value received in S100 with statistical data to calculate a percentile for the test value of the analysis object, and calculating a percentile for each of the at least one raw material component based on the calculated percentile. A step of correcting the body numerical test value of the analysis target may be further included.

This correction method can be corrected by considering the relative position of the test value for the analysis object compared to the test values of other analysis objects.

② Method of correction based on the numerical range within which the body’s numerical test values fall

The memory 150 stores a plurality of numerical ranges for each raw material component and weight settings for each numerical range.

S100 may further include the step of the processor 110 correcting the received body value test value of the analysis target based on a weight set for the numerical range corresponding to the body value test value received in S100.

For example, a numeric range from 0 to 20 has a weight of 1, a numeric range from 20 to 40 has a weight of 1.5, a numeric range from 40 to 60 has a weight of 2, a numeric range from 60 to 80 has a weight of 2.5, and 80 to 80 has a weight of 2.5. Different weights can be set for each numerical range, such as a numerical range of 100 having a weight of 3.

The processor 110 calculates the raw material condition index of the analysis target for each body substance. (S200)

In an embodiment of the present disclosure, the raw material condition index is quantified by calculating the degree to which the analysis subject possesses raw material components in the body for producing each body substance in the body.

In the raw material condition index calculation step (S200), the processor 110 determines the raw material status of the analysis target for each body substance based on the body numerical test value received in S100 and the first weight preset for each of at least one raw material component. The index can be calculated.

Referring to Figure 6, the raw material condition index of the analysis target for each body substance of serotonin, dopamine, acetylcholine, GABA, and adrenaline is calculated.

Let us explain with an example by referring to serotonin.

The body level test value for the first raw material ingredient (main ingredient) of serotonin is 80, the body level test value for the second raw ingredient (auxiliary ingredient 1) is 70, and the body level test value for the third raw ingredient (auxiliary ingredient 2) The value is 70, and the body level test value for the fourth raw material ingredient (inhibitor) is 10.

When each body numerical test value is multiplied by the preset first weight, the first raw material component is 80 x 10 = 800, the second raw material component is 70 x 2 = 140, and the third raw material component is 70 x 1 = 70. 4 The raw material components are calculated as 10 x -2 = -20, and when added together, 990 is calculated as shown in FIG.

In an embodiment of the present disclosure, the first weight is characterized in that each of the at least one raw material component is set according to at least one of the role and proportion related to the production of substances in the body.

For example, if the role of the raw material is a main ingredient and has a large contribution to the production of substances in the body, a very high weight may be applied, and if the role of the raw material is a minor ingredient and also has a small contribution to the production of substances in the body, a very high weight may be applied. Normal or small weights may be applied to .

If dopamine, acetylcholine, GABA, and adrenaline are each calculated based on the body test values and weights shown in FIG. 5, the results shown in FIG. 6 can be obtained.

Processor 110 calculates a score for the mental state item of the substance in the body. (S300)

The processor 110 calculates a composite score for each mental state item subject to analysis. (S400)

In an embodiment of the present disclosure, various mental state items are applicable, and with reference to FIG. 6, happiness, satisfaction, concentration, memory, anxiety, excitement, motivation, understanding, creativity, and calmness are exemplified.

In addition, it can be applied to any item that is related to substances in the body and the mind of the subject of analysis.

The psychoanalysis apparatus 100 according to an embodiment of the present disclosure may perform a process aimed at analyzing at least one specific mental state item designated for the analysis object, or a process aimed at analyzing all mental state items. You can also perform . The performance of this process may be set differently depending on options input during the implementation or implementation of the invention.

For example, the subject of analysis may request analysis of two mental state items, happiness and satisfaction, or may request analysis of all mental state items to determine which mental state items need improvement.

In addition, in the embodiment of the present disclosure, the mental state item is illustrated as including the psychological state or cognitive ability of the analysis target, but in addition, any state or ability related to the mind of the analysis target can be applied. Additionally, depending on the embodiment, the mental state item may have various names such as heart item, emotion item, mental item, etc.

At S300, the processor 110 calculates a score for each of the at least one mental state item of each body substance based on the raw material state index calculated at S200 and the second weight set for the at least one mental state item of each body substance. can be calculated.

In an embodiment of the present disclosure, the second weight is set according to the influence of each substance in the body on each of at least one mental state item.

Taking serotonin in FIG. 6 as an example, in S300, the processor 110 calculates the raw state index 990 for serotonin for each mental state item based on a preset second weight for each of at least one mental state item. A score can be calculated.

For example, the processor 110 may determine the serotonin score for happiness as 990 x 10 = 9,900, 990 x -10 = -9,900 for anxiety, 990 x -10 = -9,900 for excitement, 990 x 20 = 19,800 for motivation, 990 = 9,900, the score for creativity can be calculated as 990 x 10 = 9,900, and the score for calmness can be calculated as 990 x 30 = 29,700.

The processor 110 can calculate a score for at least one mental state item by equally applying this operation to other body substance items.

In addition, when the calculation operation for all body substance items is completed, the processor 110 obtains a score for each body substance for each mental state item for the analysis object, and adds them all to calculate a comprehensive score for each mental state item. It becomes possible.

Referring to Figure 6, the overall score results for the analysis subjects are as follows: happiness is 59,500, satisfaction is 49,750, concentration is 57,700, memory is 79,500, anxiety is 33,300, excitement is 29,200, Motivation is calculated with a composite score of 65,300, comprehension is calculated with a composite score of 73,600, creativity is calculated with a composite score of 72,400, and calmness is calculated with a composite score of 42,700.

The processor 110 derives intake information or customized content for the analysis target. (S500)

The processor 110 obtains a comprehensive score calculated for each of the one or more mental state items of the analysis target through S100 to S400, which represents the result of the psychoanalysis of the analysis target.

The processor 110 may provide the psychological analysis results by outputting them through an output unit, or may provide information by transmitting them to the user's terminal 200, the medical staff's terminal 200, or the server of the medical institution.

Additionally, the processor 110 may generate a psychoanalysis result of the analysis target by inputting the overall score into the artificial intelligence model.

In an embodiment of the present disclosure, the artificial intelligence model is taught how to calculate the raw material condition index of the analysis target for each body substance based on the body numerical value of the analysis target using the raw material condition index calculation algorithm.

In addition, in an embodiment of the present disclosure, the artificial intelligence model is a method of calculating a comprehensive score for each of one or more mental state items of the analysis object based on the degree of influence between each body substance and one or more mental state items related to the mind of the analysis object. It has been learned.

In addition, in the embodiment of the present disclosure, the artificial intelligence model is taught how to generate psychoanalysis results of the analysis object based on the comprehensive score calculated for the analysis object, and more specifically, the artificial intelligence model is trained to generate psychoanalysis results for the analysis object. The average value of each mental state item is learned.

Therefore, when the overall score of the analysis target is input, the artificial intelligence model can generate an evaluation result for the overall score for each mental state item.

For example, with reference to Figure 8, assuming that the average happiness level of a general analysis subject is 65,000, the artificial intelligence model may generate an evaluation result showing that the analysis subject's happiness is somewhat low.

Additionally, the artificial intelligence model can derive and provide recommended information that can improve the mental state items that need improvement to the analysis subject based on the psychoanalysis results of the analysis subject. In this case, the recommended information may include rest, sunlight, This may include meditation, exercise, etc.

In one embodiment, the processor 110 may input the overall score obtained for the analysis object into an artificial intelligence model to derive intake information necessary for improving a specific mental state item of the analysis object.

The processor 110 generates intake information for each of the at least one raw material component according to the body level test value for the analysis target received in S100 and the role or proportion of each of the at least one raw material component in producing substances in the body. can do.

Referring to FIG. 7, the processor 110 determines whether to administer or administer A, B, and C in consideration of the high and low values of A, B, and C, and determines the amount needed to improve the specific mental state item of the analysis target. Intake information can be derived.

At this time, a specific mental state item means that the comprehensive score calculated for the analysis object is outside the preset numerical range compared to the average value, and because multiple body substances may be involved in a specific mental state item, the artificial intelligence model Intake information can be derived by considering multiple body substances together.

In detail, the artificial intelligence model generates virtual intake information by determining the values of raw ingredients contained in a plurality of body substances to calculate intake information of the analysis object based on the body numerical test values received for the analysis object. .

Then, the processor 110 calculates the expected body value test value of the analysis target to be changed based on the virtual intake information, and proceeds with the processes of S100 to S400 using the calculated expected body value test value, thereby reducing the mental health of the analysis subject. By calculating the expected overall score for each status item, it is possible to determine the change in the overall score when the analysis subject ingests the virtual intake information.

Afterwards, the processor 110 may use the derived intake information as is if the identified change in overall score is as desired, and if there is a difference from the goal, the processor 110 may control the artificial intelligence model to reflect this and correct the intake information. there is.

The processor 110 may generate and provide various types of visualization data based on a comprehensive score calculated for one or more mental state items of the analysis target.

Referring to FIG. 8, the processor 110 may generate and display/output/provide a mental chart using psychoanalysis results, based on the overall score of the subject of analysis for at least one mental state item.

At this time, the processor 110 may display the average value of the general public for each mental state item and also provide grade information on the overall score of the analysis target.

The processor 110 may display the overall score of the analysis target in numbers, but may also provide rating information such as very low, low, average, high, very high, needs improvement, etc. to make it easier for the general public to understand.

In an embodiment of the present disclosure, the processor 110 may generate a radar chart as shown in FIG. 9 to provide visualization data on the results of the mental state analysis of the subject of analysis. In one embodiment, the processor 110 ) can identify the mental state pattern of the analysis object according to the mental state analysis results obtained for the analysis object, determine the chart type optimized for the identified mental state pattern, and apply a comprehensive score to the determined chart type to create the chart. can be created.

Figure 10 is a diagram illustrating extracting and prescribing customized content that matches the analysis results of the analysis target from the memory 150 where various mental state improvement content is stored.

In an embodiment of the present disclosure, the memory 150 stores a plurality of contents and URLs of the contents that are helpful in improving at least one mental state item.

Each of a plurality of contents in which data or URLs are stored in the memory 150 has at least one mental state item that can be improved.

In one embodiment, each of a plurality of contents in which data or URLs are stored in the memory 150 has at least one mental state item that can be improved and an expected improvement value set.

The processor 110 inputs the comprehensive score calculated for the analysis object into the artificial intelligence model, and the artificial intelligence model selects at least one mental state item requiring improvement for the analysis object based on the comprehensive score of the analysis object. At least one content that can be derived and improved can be derived and provided as customized content to the analysis target.

The method according to an embodiment of the present disclosure described above may be implemented as a program (or application) and stored in a medium in order to be executed in combination with a server, which is hardware.

The above-mentioned program is C, C++, JAVA, machine language, etc. that can be read by the processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented in the program. It may include code coded in a computer language. These codes may include functional codes related to functions that define the necessary functions for executing the methods, and include control codes related to execution procedures necessary for the computer's processor to execute the functions according to predetermined procedures. can do. In addition, these codes may further include memory reference-related codes that indicate at which location (address address) in the computer's internal or external memory additional information or media required for the computer's processor to execute the above functions should be referenced. there is. In addition, if the computer's processor needs to communicate with any other remote computer or server in order to execute the above functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes regarding whether communication should be performed and what information or media should be transmitted and received during communication.

The storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers that the computer can access or on various recording media on the user's computer. Additionally, the medium may be distributed to computer systems connected to a network, and computer-readable code may be stored in a distributed manner.

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

Above, embodiments of the present disclosure have been described with reference to the attached drawings, but those skilled in the art will understand that the present disclosure can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

Claims

performed by a mental state analysis device,

Receiving a test value of the subject's body level for at least one raw ingredient involved in the production of each substance in the body that affects at least one mental state item including the subject's psychological state or cognitive ability;

Inputting the received body numerical test values into an artificial intelligence model to calculate a raw material condition index of the analysis target for each body substance; and

Comprising a step of calculating a composite score for each of the one or more mental state items of the subject of analysis,

The step of receiving the test value is,

Comparing the received body count test value with statistical data including body count test values for other analysis subjects other than the analysis subject, and calculating a percentile for the test value of the analysis subject; and

Comprising the step of correcting the body level test value of the analysis subject for each of the at least one raw material component according to the calculated percentile,

A mental state analysis method based on composition.
According to paragraph 1,

The raw material condition index is a quantification of the degree to which the subject of analysis possesses in the body the raw material ingredients for producing each body substance in the body.

A mental state analysis method based on composition.
According to paragraph 2,

The raw material condition index calculation step is,

Characterized in calculating the raw material condition index of the analysis target for each body substance based on the received body value test value and a first weight set for each of the at least one raw material component.

A mental state analysis method based on composition.
According to paragraph 3,

The first weight is set according to the role or specific gravity of each of the at least one raw material component in producing the substance in the body,

A mental state analysis method based on composition.
According to paragraph 1,

The overall score calculation step is,

calculating a score for the at least one mental state item of each body substance based on the calculated raw state index and a second weight set for the at least one mental state item of each body substance; and

Comprising the step of calculating the overall score for each of the one or more mental state items of the analysis subject, based on the score calculated for each body substance,

A mental state analysis method based on composition.
According to paragraph 1,

Further comprising deriving intake information for improving mental state items in which the calculated overall score is outside a preset numerical range among the one or more mental state items,

A mental state analysis method based on composition.
According to clause 6,

Characterized in that generating intake information for each of the at least one raw material component according to the received body level test value and the role or specific gravity of each of the at least one raw material component in producing the substance in the body.

A mental state analysis method based on composition.
A receiving unit that receives a test value of the body level of the subject of analysis for at least one raw ingredient involved in the production of each substance in the body that affects at least one mental state item including the psychological state or cognitive ability of the subject of analysis; and

Inputting the received body numerical test values into an artificial intelligence model to calculate the raw material condition index of the analysis target for each body substance,

A processor that calculates a composite score for each of the one or more mental state items of the subject of analysis,

The processor,

Comparing the received body count test value with statistical data including body count test values for other analysis subjects other than the analysis subject, calculating a percentile for the test value of the analysis subject,

Characterized in correcting the body level test value of the analysis subject for each of the at least one raw material component according to the calculated percentile,

A component-based mental state analysis device.
A computer-readable recording medium coupled to a hardware computer and storing a program for executing the method of any one of claims 1 to 7.