WO2023140411A1

WO2023140411A1 - Method for frequency therapy using ai

Info

Publication number: WO2023140411A1
Application number: PCT/KR2022/001229
Authority: WO
Inventors: 유은숙
Original assignee: 퀀텀바이오 주식회사
Priority date: 2022-01-24
Filing date: 2022-01-24
Publication date: 2023-07-27

Abstract

Provided is a method for frequency therapy using AI, the method enabling disease determination and selection of a combined therapy frequency according to the disease determination, which had been performed manually, to be automatically performed using an AI program. The method for frequency therapy using AI comprises steps in which: a remote management server extracts analysis-required wave patterns, which are wave patterns having a deviation greater than or equal to a reference value, from wave deviations, which are deviations between a wave pattern of a standard code and wave patterns fed back from a human body; the remote management server extracts, from the analysis-required wave patterns, wave patterns related to autonomic nerves; the remote management server compares the number of items related to acceleration and deceleration of autonomic nerves in analysis-required wave patterns related to acceleration of autonomic nerves; as the determination result of the step, the presence of an autoimmune disease is determined if the number of items of the analysis-required wave patterns related to acceleration of autonomic nerves is greater than the number of items of analysis-required wave patterns related to deceleration of autonomic nerves, so that an occupation rate of how much an analysis-required wave pattern matches wave patterns of various kinds of autoimmune diseases is calculated; and the remote management server determines the priorities of diseases on the basis of the magnitudes of the calculated occupation rates, and performs frequency therapy by transferring wave patterns to the human body in the order of the determined priorities.

Description

Frequency therapy method using AI

The present invention relates to a frequency treatment method using artificial intelligence (AI), and more particularly, to a frequency treatment method using AI that enables an AI program to automatically determine a disease and select a complex treatment frequency accordingly, which was previously manually performed by an expert.

In recent years, artificial intelligence (AI) systems that implement human-level intelligence have been used in various fields. Unlike existing rule-based smart systems, artificial intelligence systems are systems in which machines learn, judge, and become smarter on their own. Artificial intelligence systems are being replaced by systems that improve recognition rates as they are used and understand user tastes more accurately.

Artificial intelligence technology consists of machine learning, for example, deep learning algorithms and component technologies utilizing machine learning algorithms. Machine learning algorithm is an algorithm technology that classifies and learns the characteristics of input data by itself, and element technology is a technology that uses machine learning algorithms such as deep learning to mimic the functions of the human brain, such as recognition and judgment.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/text, and includes natural language processing, machine translation, dialogue system, question and answering, voice recognition/synthesis, and the like. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, space understanding, image improvement, and the like. Inference prediction is a technique of reasoning and predicting logically by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendation.

Knowledge representation is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data creation/classification) and knowledge management (data utilization). Motion control is a technology for controlling the autonomous driving of a vehicle and the movement of a robot, and includes motion control (navigation, collision, driving), manipulation control (action control), and the like.

On the other hand, frequency therapy has been used as one of alternative medicine at home and abroad for a long time. This frequency therapy is a technique for alleviating symptoms or treating diseases by combining complex related natural waves possessed by each component on the premise that all components of the human body have a unique wave (frequency), and using the combined frequency. Currently, various types of frequency therapy are proposed and used in real life.

However, up to now, frequency therapy has been used to determine and determine the type or degree of disease and the complex frequency (wave) for treatment by transferring the unique wave energy of each component of the human body in a healthy state, that is, the wave pattern of the standard code, to the human body.

The present invention has been devised to solve the above-mentioned problems, and the purpose is to provide a frequency treatment method using AI that allows conventional experts to automatically perform disease determination and selection of complex treatment frequencies through an AI program, which was manually performed.

Another object of the present invention is to judge that the patient has an autoimmune disease or immunocompromised disease according to the comparison result of the number of items related to autonomic nerve enhancement and decline among the wave patterns required for analysis, which are wave patterns having deviations greater than the standard value in the wave patterns fed back from the human body. A frequency treatment method using AI capable of performing frequency treatment by transcribing the wave patterns into the human body by determining the priority of the disease according to the size of the occupancy rate of how much the wave pattern required for analysis matches the wave pattern for each autoimmune disease or immunocompromised disease. Its purpose is to provide

The frequency treatment method using AI according to the first embodiment of the present invention is performed between a frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is loaded, and a remote management server connected to the computer through a wired/wireless communication network. extracting a wave pattern required for phosphorus analysis; ii) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among wave patterns that need analysis; iii) comparing, by the remote management server, the number of items related to autonomic hyperactivity and depression among wave patterns that need to be analyzed related to autonomic hyperactivity; iv) As a result of the determination in step iii), if the number of items of wave patterns required for analysis related to hyperactivity of autonomic nerves is greater than the number of items of wave patterns required for analysis related to degradation of autonomic nerves, it is determined that there is an autoimmune disease, and a step of calculating the share of how much the wave patterns required for analysis match the wave patterns for each autoimmune disease; and v) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step iv), and transcribing the wave patterns to the human body in the order of the determined priority to perform frequency therapy.

The frequency treatment method using AI according to the second embodiment of the present invention is performed between a frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is loaded, and a remote management server connected to the computer through a wired/wireless communication network, wherein a) the remote management server has a wave pattern having a deviation greater than a standard value in the wave deviation, which is a deviation between the wave pattern of the standard code and the wave pattern fed back from the human body. extracting a wave pattern required for phosphorus analysis; b) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among the wave patterns to be analyzed; c) comparing, by the remote management server, the number of items related to autonomic hyperactivity and deterioration among wave patterns to be analyzed related to autonomic hyperactivity; d) as a result of the determination in step c), if the number of items of the wave pattern to be analyzed related to hyperactivity of the autonomic nerve is greater than the number of items of the wave pattern to be analyzed related to the decline of the autonomic nerve, it is determined that the patient has an autoimmune disease, and a step of calculating the share of how much the wave pattern to be analyzed matches the wave pattern for each type of autoimmune disease; e) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step d), and transcribing wave patterns to the human body in the order of the determined priority to perform frequency therapy; and f) providing a check list of major pains according to related diseases, and then, when the checked pain items exist, transcribing them to the human body in a manner that reinforces a wave pattern related to the corresponding pain response tube.

The frequency treatment method using AI according to the third embodiment of the present invention is performed between a frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is loaded, and a remote management server connected to the computer and a wired/wireless communication network, A) the remote management server generates a wave with a deviation greater than a standard value in the wave deviation, which is the deviation between the wave pattern of the standard code and the wave pattern fed back from the human body extracting a wave pattern to be analyzed, which is a pattern; b) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among the wave patterns to be analyzed; c) comparing, by the remote management server, the number of items related to autonomic hyperactivity and deterioration among wave patterns to be analyzed related to autonomic hyperactivity; D) As a result of the determination in step C), if the number of items of wave patterns required for analysis related to hyperactivity of autonomic nerves is greater than the number of items of wave patterns required for analysis related to degradation of autonomic nerves, it is determined that an autoimmune disease is present and the analysis required A step of calculating the share of how much the wave pattern matches the wave patterns for each autoimmune disease; e) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step d), and transcribing the wave patterns to the human body in the order of the determined priority to perform frequency treatment; and f) when the frequency treatment period passes the first cycle, comparing the wave pattern for the main pain list of the related disease with the wave pattern analyzed immediately before extracting the deteriorated wave pattern, and then intensifying the deteriorated wave pattern.

According to the frequency treatment method using AI of the present invention, since the judgment or determination of the type or degree of disease and the complex frequency (wave) for treatment, which has been subjectively performed by conventionally trained experts, is automatically performed by the AI program, not only the time required to determine or determine the type or degree of disease and the complex frequency (wave) for treatment can be reduced, and the accuracy and objectivity of the judgment or decision can be improved.

In addition, the present invention determines that the patient has an autoimmune disease or immunosuppressive disease such as systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, autoimmune anemia, and Graves' disease according to the comparison result of the number of items related to autonomic nerve hyperactivity and depression among the wave patterns required for analysis having deviations greater than the standard value in the wave pattern fed back from the human body. It is possible to perform frequency therapy by transferring the wave pattern to the human body by determining the priority of the frequency.

1 is a system configuration diagram in which a frequency treatment method using AI of the present invention is implemented.

2 is a flowchart for explaining a frequency treatment method using AI according to a first embodiment of the present invention.

3 is a flowchart for explaining a frequency treatment method using AI according to a second embodiment of the present invention.

4 is a flowchart for explaining a frequency treatment method using AI according to a third embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, a preferred embodiment of the frequency treatment method using AI of the present invention will be described in more detail.

The principle of frequency therapy of the present invention is to transfer the unique wave energy according to each component or condition of the human body in a healthy state, that is, the wave pattern of the standard code to the patient in a state where it is stored as data and receive feedback. to alleviate symptoms or treat disease.

1 is a system configuration diagram in which a frequency treatment method using AI of the present invention is implemented. As shown in FIG. 1, the system in which the frequency treatment method using AI of the present invention is implemented is a frequency processing device 100 installed in a place where frequency treatment is performed, for example, a home, a hospital, an accommodation facility, or a nursing home or recreation facility in an area where a person who wants frequency treatment is located, a computer 200 connected to the frequency processing device 100 through a wired/wireless network, for example, a local area network such as Wi-Fi or Bluetooth, or a local area wired communication network such as a LAN, for example, a desktop , A laptop or tablet PC and the computer 200 and a wired / wireless communication network, for example, may include a remote management server 300 connected remotely through the Internet.

In the configuration described above, the frequency processing device 300 includes the human body contact band 150 filled in the right places of the human body, for example, both wrists and both ankles, a frequency generator 110 that randomly generates and transfers wave patterns of various frequencies used for analyzing the user's health condition and alleviating abnormal symptoms to the human body through the human body contact band 150, and a frequency collection unit 1 that collects wave patterns fed back from the human body through the human body contact band 150 to analyze the user's health state. 20), a controller 130 that controls the operation of the frequency generator 110 and the frequency collector 120, and a communication unit 140 that communicates with the computer 200 to receive an operation command of the frequency processing device 100 from the computer 200 and transmits wave pattern data collected by the frequency processing device 100 receiving feedback from the human body to the computer 200.

A frequency treatment program is installed in the computer 200. This frequency treatment program stores standard code wave pattern data or receives standard code wave pattern data from the remote management server 300 and transmits the standard code wave pattern data to the controller 110 of the frequency processing device 100, compares the collected wave pattern data received from the human body with the standard code wave pattern data, and transmits the result to the remote management server 300.

Finally, the remote management server 300 divides and stores the wave pattern data of the standard code into major categories, intermediate categories, and small categories, as described below, and stores a plurality of wave pattern data used for treatment of various diseases, and processes these data through an AI program as will be described later to generate treatment wave pattern data most suitable for the patient and then transmit it to the computer 200.

2 is a flowchart for explaining a frequency treatment method using AI according to a first embodiment of the present invention. Referring to FIG. 2 , when a user first logs in to the remote management server 300 with the computer 200 and the frequency treatment program running after turning on the computer 200 and the frequency treatment program, the wave pattern data of the standard code is transmitted to the frequency processing device 100 through the frequency treatment program of the computer 200 and transferred to the human body through the human body contact band 150 (step S100). It is collected through and collected in the computer 200 (step S110).

Table 1 is a table in which the wave patterns of the standard code according to the present invention are divided into major, intermediate, and small categories according to human body components and states or symptoms.

As shown in Table 1, according to one embodiment of the present invention, the components of the human body are broadly classified into a total of 23 systems, further subclassed into a plurality of components or conditions or symptoms for each system, and each subclass is again subclassed into a plurality of conditions or symptoms, and one wave pattern is corresponded to each subclass. One wave pattern may be applied to a plurality of subclass items at the same time.

Next, the computer 200 calculates the deviation of the wave pattern of the standard code and the wave pattern fed back from the human body (hereinafter referred to as 'wave deviation') (step S120), and transmits the result to the remote management server 300. Conventionally, a trained expert analyzes or reads the printed matter calculated for each computer 200, that is, each patient, in the remote management server 300 to determine and determine the type or degree of disease and the complex frequency (wave) for treatment. have been On the other hand, according to the frequency treatment method of the present invention, the remote management server 300 extracts a wave pattern having a deviation greater than or equal to a standard value from the calculated wave deviation (hereinafter, referred to as a 'wave pattern requiring analysis') (step S130). Tables 2 and 3 are diagrams showing wave patterns required for analysis for the skeletal and musculoskeletal systems, respectively.

Next, the remote management server 300 extracts a wave pattern related to the autonomic nerve from among the wave patterns that need analysis (step S140). Next, the remote management server 300 compares the number of items related to autonomic hyperactivity and depression among the wave patterns to be analyzed related to autonomic nerve hyperactivity and autonomic nerve degradation (step S150), and determines whether the patient has a disease related to autonomic nerve hyperactivity or autonomic nerve hypotrophy.

As a result of the determination in step S150, if the number of wave pattern items requiring analysis related to hyperactivity of the autonomic nerve is greater than the number of wave pattern items requiring analysis related to autoimmune deterioration, the remote management server 300 determines that the patient has an autoimmune disease, and calculates whether or not the wave pattern requiring analysis matches the wave pattern for each autoimmune disease, that is, its share (step S160). Examples of autoimmune diseases include systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis, autoimmune anemia, and Graves' disease.

Next, the remote management server 300 prioritizes the disease of the patient according to the size of the occupancy rate calculated in step S160. For example, if the occupancy rate of the wave pattern item requiring analysis is the highest among the wave patterns according to various symptoms of rheumatoid arthritis (which may appear in multiple major and intermediate categories), it is determined that the possibility of rheumatoid arthritis is the highest (step S170).

Finally, the remote management server 300 downloads the wave patterns according to the priorities determined in step S170 to the computer 200, and the computer 200 commands the frequency processing device 100 based on this to transfer the wave patterns for treatment to the human body for a given time (step S180).

As a result of the determination in step S150, if the number of wave pattern items requiring analysis related to autonomic nerve deterioration is greater than the number of wave pattern items requiring analysis related to autonomic nerve enhancement, the remote management server 300 determines that the patient has an immunocompromised disease, calculates how much the wave pattern requires analysis matches the wave pattern for each immunocompromised disease, that is, calculates its share (step S190), and determines the priority of the patient's disease based on the size of the calculated share (step S170). ).

Finally, the remote management server 300 downloads the wave patterns according to the priority determined in step S170 to the computer 200, and based on this, the computer 200 commands the frequency processing device 100 to transfer the wave patterns for treatment to the human body for a given time (step S180).

3 is a flowchart for explaining a frequency treatment method using AI according to a second embodiment of the present invention. When a user first logs in to the remote management server 300 with the computer 200 and the frequency processing device 100 turned on and the frequency treatment program running, the wave pattern data of the standard code is transferred to the frequency processing device 100 through the frequency treatment program of the computer 200 and transferred to the human body through the human body contact band 150 (step S300), and the wave pattern data fed back from the human body is collected through the human body contact band 150 and returned to the computer 200. 0) is collected (step S310).

Next, the computer 200 calculates the deviation (hereinafter referred to as 'wave deviation') of the wave pattern of the standard code and the wave pattern fed back from the human body (step S320), and transmits the result to the remote management server 300. Conventionally, a trained expert analyzes or reads the printout of the wave deviation calculated for each computer 200, that is, each patient, in the remote management server 300 to determine and determine the type or degree of disease and the complex frequency (wave) for treatment. have been

On the other hand, according to the frequency treatment method of the present invention, the remote management server 300 extracts (step S330) a wave pattern having a deviation greater than or equal to a reference value from the calculated wave deviation (hereinafter referred to as a 'wave pattern requiring analysis').

Next, the remote management server 300 extracts a wave pattern related to the autonomic nerve from among the wave patterns that need analysis (step S340). Next, the remote management server 300 compares the number of items related to autonomic hyperactivity and depression among the wave patterns that need to be analyzed related to autonomic nerve hyperactivity (step S350), and determines whether the patient has a disease related to autonomic nerve hyperactivity or autonomic hypotrophy.

As a result of the determination in step S350, if the number of wave pattern items requiring analysis related to hyperactivity of the autonomic nerve is greater than the number of wave pattern items requiring analysis related to autoimmune deterioration, the remote management server 300 determines that the patient has an autoimmune disease and calculates how much the wave pattern required analysis matches the wave pattern for each autoimmune disease, that is, its share (step S360). Examples of autoimmune diseases include systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis, autoimmune anemia, and Graves' disease.

Next, the remote management server 300 determines the priority of the patient's disease according to the size of the occupancy rate calculated in step S360. For example, if the occupancy rate of the wave pattern item requiring analysis is the highest among the wave patterns according to various symptoms of rheumatoid arthritis (which may appear in multiple major and intermediate categories), it is determined that the possibility of rheumatoid arthritis is the highest (step S370).

Finally, the remote management server 300 downloads the wave patterns according to the priority determined in step S370 to the computer 200, and the computer 200 commands the frequency processing device 100 based on this to transfer the wave patterns for treatment to the human body for a given time (step S380).

As a result of the determination in step S350, if the number of wave pattern items requiring analysis related to autonomic nerve degradation is greater than the number of wave pattern items requiring analysis related to autonomic nerve enhancement, the remote management server 300 determines that the patient has an immunocompromised disease, calculates how much the wave pattern requires analysis matches the wave pattern for each immunocompromised disease, that is, calculates its share (step S390), and determines the priority of the patient's disease based on the size of the calculated share (step S370). ).

Finally, the remote management server 300 downloads the wave patterns according to the priority determined in step S200 to the computer 200, and the computer 200 commands the frequency processing device 100 based on this to transfer the wave patterns for treatment to the human body for a given period of time (step S380).

On the other hand, as this frequency treatment progresses, the body's adjustment reaction occurs to return the diseased cells inside and outside the body, that is, the bad condition in the inflamed cells, tissues, or organs to a healthy original state, which is called 'improvement reaction'. These improvement reactions are, for example, microbes such as viruses, bacteria, harmful gases, accumulated poisoning, inflammation formed by waste products such as drugs and fat, fibrosis, calcification, tumors, etc. are decomposed and various reactions caused by excretory organs such as skin, pores, urine, feces, eyes, nose, mouth, ears, etc.

Eventually, the disordered movements of small particles within cells cause symptoms such as pain, swelling, headache, numbness in hands and feet, vomiting, diarrhea, constipation, drowsiness, dizziness, abdominal pain, menstrual cramps, clearing cough, sores, runny nose, bleeding, rash, itchy skin, hypersomnia, and fatigue. referred to as the 'pain response'). Therefore, the stronger the person, the lower the intensity of this pain response and the shorter the duration.

In step S410, a major pain checklist according to the related disease of the user is provided in order to adjust the transcriptional wave pattern according to the pain response that is different for each disease or individual.

When one or more pain items are input (checked) in step S420, the input (checked) pain response tube-related wave pattern is strengthened, for example, the transcription time of the deteriorated wave pattern is increased or the transcription period is shortened. By strengthening the aggravated wave pattern, intensive treatment is performed for the corresponding pain response.

Next, in step S440, it is determined whether a predetermined period of time, for example, 6 months, has elapsed. If not, step S410 is repeatedly performed, but if it elapses, the return to step S100 is performed to transfer the wave pattern of the standard code to the human body. The wave pattern to be transferred to the patient is completely supplemented, reconstructed, or terminated.

4 is a flowchart for explaining a frequency treatment method using AI according to a third embodiment of the present invention. First, when a user turns on the power of the computer 200 and the frequency processing device 100 and logs in to the remote management server 300 for the first time while running a frequency treatment program, the wave pattern data of the standard code is transmitted to the frequency processing device 100 through the frequency treatment program of the computer 200 and transferred to the human body through the human body contact band 150 (step S500), and the wave pattern data fed back from the human body is collected through the human body contact band 150 and the computer ( 200) is collected (step S510).

Next, the computer 200 calculates the deviation of the wave pattern of the standard code and the wave pattern fed back from the human body (hereinafter referred to as 'wave deviation') (step S520), and transmits the result to the remote management server 300. In the related art, a trained expert analyzes or reads the printout of the wave deviation calculated for each computer 200, that is, each patient, in the remote management server 300 to determine and determine the type or degree of disease and the complex frequency (wave) for treatment. have been

On the other hand, according to the frequency treatment method of the present invention, the remote management server 300 extracts a wave pattern (hereinafter referred to as a 'wave pattern required for analysis') having a deviation greater than the standard value from the calculated wave deviation (step S530).

Next, the remote management server 300 extracts a wave pattern related to the autonomic nerve from among these wave patterns that need analysis (step S540). Next, the remote management server 300 compares the number of items related to autonomic hyperactivity and depression among the wave patterns that require analysis related to autonomic nerve hyperactivity (step S550), and determines whether the patient has a disease related to autonomic nerve hyperactivity or autonomic hypotrophy.

As a result of the determination in step S550, if the number of wave pattern items requiring analysis related to hyperactivity of the autonomic nerve is greater than the number of wave pattern items requiring analysis related to autoimmune deterioration, the remote management server 300 determines that the patient has an autoimmune disease, and calculates how much the wave pattern requires analysis matches the wave pattern for each autoimmune disease, that is, its share (step S560). Autoimmune diseases include, for example, systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis, autoimmune anemia, and Graves' disease.

Next, the remote management server 300 determines the priority of the patient's disease according to the size of the occupancy rate calculated in step S560. For example, if the occupancy rate of the wave pattern item requiring analysis is the highest among the wave patterns according to various symptoms of rheumatoid arthritis (which may appear in multiple major and intermediate categories), it is determined that the possibility of rheumatoid arthritis is the highest (step S570).

Finally, the remote management server 300 downloads the wave patterns according to the priority determined in step S570 to the computer 200, and the computer 200 commands the frequency processing device 100 based on this to transfer the wave patterns for treatment to the human body for a given time (step S580).

As a result of the determination in step S550, if the number of wave pattern items requiring analysis related to autonomic nerve deterioration is greater than the number of wave pattern items requiring analysis related to autonomic nervous hyperactivity, the remote management server 300 determines that the patient has an immunocompromised disease, calculates how much the wave pattern requires analysis matches the wave pattern for each immunocompromised disease, that is, calculates its share (step S590), and determines the priority of the patient's disease based on the size of the calculated share (step S570). ).

Finally, the remote management server 300 downloads the wave patterns according to the priorities determined in step S570 to the computer 200, and the computer 200 commands the frequency processing device 100 based on them to transfer the wave patterns for treatment to the human body for a given time (step S580), whereby frequency treatment can be performed in the order of diseases with high possibility.

Then, in order to adjust the transcription wave pattern according to the pain response that is different for each disease or individual, it is determined whether the first frequency treatment period, for example, one week has elapsed (step S610). The first cycle can be automatically or manually adjusted according to the type of disease to be treated. In step S610, if the first cycle does not elapse, steps S580 and below are repeated. On the other hand, if the first cycle elapses, the wave pattern of the main pain list of the related disease is compared with the initially analyzed wave pattern to extract the aggravated wave pattern. To this end, the main pain list for each disease is stored in the form of a database in the remote management server 200.

Next, in step S630, the aggravated wave pattern is strengthened by a method of reinforcing the aggravated wave pattern, for example, by increasing the transcription time of the aggravated wave pattern or shortening the transcription period, so that intensive treatment is performed for the corresponding pain response.

Next, in step S640, it is determined whether the first cycle after the second cycle has elapsed. If not, step S640 is repeated, whereas if elapsed, the process proceeds to step S650, and compares the wave pattern for the major pain list of the related disease with the previously analyzed wave pattern to extract an aggravated wave pattern, and again performs step S660 to intensify the aggravated wave pattern, so that the aggravated wave pattern is transferred to the human body.

Finally, in step S670, it is determined whether the second cycle, for example, 6 months has elapsed. If not, step S640 is repeatedly performed, but if it elapses, it returns to step S500 and the wave pattern of the standard code is transferred to the human body. The wave pattern to be transferred to the patient is completely supplemented, reconstructed, or terminated.

The frequency treatment method using the AI of the present invention is not limited to the above-described embodiment and can be implemented with various modifications within a range that does not deviate from the spirit of the present invention, and such modifications are described in the claims of the present invention. It is revealed that it is within the scope. For example, the aforementioned predetermined period, first cycle, and second cycle may be appropriately changed according to the type of disease or the characteristics of each patient, for example, the severity of the disease.

Claims

A frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is mounted, and a computer and a wired / wireless communication network. It is performed between the connected remote management server,

i) extracting, by the remote management server, a wave pattern to be analyzed, which is a wave pattern having a deviation greater than or equal to a reference value in a wave deviation, which is a deviation between a wave pattern of a standard code and a wave pattern fed back from a human body;

ii) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among wave patterns that need analysis;

iii) comparing, by the remote management server, the number of items related to autonomic hyperactivity and depression among wave patterns that need to be analyzed related to autonomic hyperactivity;

iv) As a result of the determination in step iii), if the number of items of wave patterns required for analysis related to hyperactivity of autonomic nerves is greater than the number of items of wave patterns required for analysis related to degradation of autonomic nerves, it is determined that there is an autoimmune disease, and a step of calculating the share of how much the wave patterns required for analysis match the wave patterns for each autoimmune disease; and

v) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step iv), and transcribing wave patterns to the human body in the order of the determined priority to perform frequency treatment.
The method of claim 1,

vi) as a result of the determination in step iii), if the number of items of the wave pattern required for analysis related to the degradation of the autonomic nerve is greater than the number of items of the wave pattern required for analysis related to the enhancement of the autonomic nerve, it is judged to have an immunocompromised disease; and

vii) determining, by the remote management server, the priority of the disease by the size of the occupancy rate calculated in step vi), and transcribing the wave pattern to the human body in the order of the determined priority, thereby performing frequency treatment. Frequency treatment method using AI, characterized in that it comprises a step.
A frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is mounted, and a computer and a wired / wireless communication network. It is performed between the connected remote management server,

a) extracting, by the remote management server, a wave pattern to be analyzed, which is a wave pattern having a deviation greater than or equal to a reference value in a wave deviation, which is a deviation between a wave pattern of a standard code and a wave pattern fed back from a human body;

b) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among the wave patterns to be analyzed;

c) comparing, by the remote management server, the number of items related to autonomic hyperactivity and deterioration among wave patterns to be analyzed related to autonomic hyperactivity;

d) as a result of the determination in step c), if the number of items of the wave pattern to be analyzed related to hyperactivity of the autonomic nerve is greater than the number of items of the wave pattern to be analyzed related to the decline of the autonomic nerve, it is determined that the patient has an autoimmune disease, and a step of calculating the share of how much the wave pattern to be analyzed matches the wave pattern for each type of autoimmune disease;

e) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step d), and transcribing wave patterns to the human body in the order of the determined priority to perform frequency therapy; and

f) After providing a checklist of major pains according to related diseases, if the checked pain items exist, transferring them to the human body in a manner that reinforces the wave patterns related to the corresponding pain response tubes. Frequency treatment method using AI.
The method of claim 3,

A frequency treatment method using AI, characterized in that when the frequency treatment period elapses a predetermined period, steps a) and below are repeatedly performed.
The method of claim 4,

The frequency treatment method using AI, characterized in that the predetermined period is increased or decreased according to the type or severity of the disease.
A frequency processing device including a frequency generator that generates a desired wave pattern and transcribes it to the human body and a frequency collection unit that collects the wave pattern fed back from the human body, a computer that controls the frequency processing device while a frequency treatment program is mounted, and a computer and a wired / wireless communication network. It is performed between the connected remote management server,

a) extracting, by the remote management server, a wave pattern to be analyzed, which is a wave pattern having a deviation greater than or equal to a standard value in a wave deviation, which is a deviation between a wave pattern of a standard code and a wave pattern fed back from a human body;

b) extracting, by the remote management server, a wave pattern related to the autonomic nerve from among the wave patterns to be analyzed;

c) comparing, by the remote management server, the number of items related to autonomic hyperactivity and deterioration among wave patterns to be analyzed related to autonomic hyperactivity;

D) As a result of the determination in step C), if the number of items of wave patterns required for analysis related to hyperactivity of autonomic nerves is greater than the number of items of wave patterns required for analysis related to degradation of autonomic nerves, it is determined that an autoimmune disease is present and the analysis required A step of calculating the share of how much the wave pattern matches the wave patterns for each autoimmune disease;

e) determining, by the remote management server, the priority of diseases based on the size of the occupancy rate calculated in step d), and transcribing the wave patterns to the human body in the order of the determined priority to perform frequency treatment; and

F) When the frequency treatment period passes the first cycle, the wave pattern for the main pain list of the related disease is compared with the wave pattern analyzed immediately before extracting the worsened wave pattern, and then the worsened wave pattern. Frequency treatment method using AI, characterized in that it includes the step of transferring to the human body in a manner that strengthens.
The method of claim 6,

When the frequency treatment period passes the second cycle (> the first cycle), step a) is performed repeatedly.
The method of claim 7,

The frequency treatment method using AI, characterized in that the first cycle and the second cycle increase or decrease according to the type or severity of the disease.