KR102554211B1 - System and method for creating an abstract painting using dna fingerprinting - Google Patents

Abstract

The present invention relates to a system and method for generating an abstract image based on user's genetic fingerprint information.
The genetic fingerprint abstract generation system according to the present invention includes an abstraction user information acquisition unit that acquires information about abstraction service users, and genetic fingerprint information that obtains genetic fingerprint information including genotype detection values for each hypermutable genetic locus of abstraction service users. Acquisition unit, outline shape and size of genetic fingerprint abstraction, arrangement rules for hypermutable genetic loci, abstraction item determination unit that determines background and unit genotype images, so that the genotype detection value becomes a normalized value within the length of the guidelines for each locus Based on the contour shape and size of the genetic fingerprint abstraction determined in the conversion genotype detection value normalization unit and the abstraction item determination unit and the arrangement rule of the hypermutational locus, the position of the guideline corresponding to the normalized value of the genotype detection value for each locus and an abstraction generation unit for generating a genetic fingerprint abstraction by disposing a unit genotype image in and performing color processing on the background and the arranged unit genotype image.

Description

Abstraction generation system and method using genetic fingerprint information {SYSTEM AND METHOD FOR CREATING AN ABSTRACT PAINTING USING DNA FINGERPRINTING}

The present invention relates to a system and method for generating an abstraction using genetic fingerprint information, and more particularly, to a system and method for generating an abstract image based on genetic fingerprint information of a user.

Due to the rapid development of wired and wireless communication, various platforms such as game platforms or metaverse platforms are used to conduct economic, social, cultural, and political activities using avatars, virtual egos, in a 4-dimensional virtual space-time like the real world. The platform is getting attention. In line with the personalization/differentiation trend, platform users have a desire to create their own special image by reflecting their unique genetic identity on various platforms.

In addition, as interest in NFT technology, which tokenizes various digital assets into non-fungible tokens (NFTs) and allows them to be traded with values of scarcity and uniqueness, is rapidly increasing, interest in creating various digital artworks has also increased. is also rising.

However, no technology has been developed to create an abstract painting, which is a digital artwork, based on human genetic fingerprints.

An object of the present invention is to solve the above problems, and to provide a system and method for generating an abstract image based on genetic fingerprint information of a service user.

The present invention may be implemented in a variety of ways, including an apparatus (system), a method, a computer program stored in a computer readable medium, or a computer readable medium in which a computer program is stored.

An abstraction generation system using genetic fingerprint information according to an embodiment of the present invention includes an abstraction user information acquisition unit that acquires information about an abstraction service user, and a genetic fingerprint including a genotype detection value for each hypermutable genetic locus of an abstraction service user. A genetic fingerprint information acquisition unit that acquires information, an abstraction item determination unit that determines the outline shape and size of genetic fingerprint abstraction, arrangement rules for supermutant genetic loci, background and unit genotype images, and genotype detection values according to the guidelines for each genetic locus. The normalized value of the genotype detection value for each locus, based on the shape and size of the contour shape and size of the genetic fingerprint abstraction determined in the normalization unit and the abstraction item determination unit, and the arrangement rules of the hypermutable locus, which are converted to normalized values within the length. and an abstraction generation unit for generating a genetic fingerprint abstraction by arranging a unit genotype image at a position of a guideline corresponding to and color-processing the background and unit genotype image.

More preferably, it further includes a genetic locus selection unit for selecting and extracting some hypermutational genetic loci and genotype detection value information necessary for generating a genetic fingerprint abstract from the genetic fingerprint information acquired by the genetic fingerprint information obtaining unit.

More preferably, the hypermutant locus is D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, vWA, D8S1179, TPOX, FGA, AMELOGENIN, D19S433 and D2S out of 1338 contains at least one

More preferably, the outline shape of the genetic fingerprint abstraction includes at least one of a square, a rectangle, a circle, an ellipse, a fan shape, a star, a hexagon, an octagon, a decagon, and a graph shape.

More preferably, the arrangement rule of the hypervariable locus is the arrangement order of the hypervariable locus, the arrangement direction for each locus, the length of the guideline for each locus, and the position of the minimum genotype and the maximum position of the genotype among the guidelines for each locus. contains at least one

More preferably, the abstraction item determination unit determines a background color, a background image, a background picture, a location of the genetic fingerprint abstraction on the background, and text to be added to the background screen.

More preferably, the unit genotype image includes at least one of shape, size, length, color and brightness of an image representing one genotype.

More preferably, the genotype detection value normalization unit normalizes the genotype detection value to be within the length of the guideline based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each hypermutable genetic locus.

In a computer system, a method for generating an abstraction using a genetic fingerprint according to the present invention implemented by at least one processor includes obtaining information about an abstraction service user, and a genotype detection value for each hypervariable locus of the abstraction service user. The step of obtaining genetic fingerprint information to determine the shape and size of the outline of the genetic fingerprint abstraction, the arrangement rule of the hypermutable locus, the step of determining the background and unit genotype image, and the normalization of the genotype detection value within the length of the guideline for each locus Based on the step of converting to a value and the shape and size of the outline of the determined genetic fingerprint abstraction and the arrangement rule of the hypermutable genetic locus, the unit genotype image is placed at the position of the guideline corresponding to the normalized value of the genotype detection value for each locus. and generating a genetic fingerprint abstraction by color processing the background and unit genotype image.

More preferably, the method further comprises selecting and extracting some hypermutation locus and genotype detection value information necessary for generating a genetic fingerprint abstract from the obtained genetic fingerprint information.

More preferably, the hypermutant locus is D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, vWA, D8S1179, TPOX, FGA, AMELOGENIN, D19S433 and D2S out of 1338 contains at least one

More preferably, the outline shape of the genetic fingerprint abstraction includes at least one of a square, a rectangle, a circle, an ellipse, a fan shape, a star, a hexagon, an octagon, a decagon, and a graph shape.

More preferably, the arrangement rule of the hypervariable locus is the arrangement order of the hypervariable locus, the arrangement direction for each locus, the length of the guideline for each locus, and the position of the minimum genotype and the maximum position of the genotype among the guidelines for each locus. contains at least one

More preferably, it includes determining the background color, background image, background picture, location of the genetic fingerprint abstraction on the background and text to be added to the background screen.

More preferably, the unit genotype image includes at least one of shape, size, length, color and brightness of an image representing one genotype.

More preferably, the step of converting the genotype detection value so that it becomes a normalized value within the length of the guideline for each genetic locus includes the genotype detection value based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each hypermutable locus. A step of normalizing to be a value within the length of the guideline is included.

According to the present invention, there are the following effects.

The present invention generates an abstract image using genetic fingerprint information, so that a user can have a personalized and differentiated picture based on his or her genetic identity.

The present invention allows abstraction using genetic fingerprint information to be used in a platform, so that positive emotions such as interest, affection, and preference for the platform can be increased.

The present invention allows abstraction using genetic fingerprint information to be combined with one's own avatar, so that an avatar reflecting genetic identity can be created.

The present invention can register and sell non-fungible tokens (NFTs) to abstract image files using genetic fingerprint information.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned are clear to those skilled in the art (referred to as "ordinary technicians") from the description of the claims. will be understandable.

BRIEF DESCRIPTION OF THE DRAWINGS Embodiments of the present invention will be described with reference to the accompanying drawings described below, wherein like reference numbers indicate like elements, but are not limited thereto.
1 shows an exemplary environment in which the system for generating abstractions using genetic fingerprint information of the present invention can operate.
2 is a block diagram illustrating an example computer device configured to participate in a genetic fingerprinting abstraction generating system.
3 is an exemplary diagram of a genetic fingerprint abstraction in which the contour shape is a rectangle, generated according to the present invention.
4 is an exemplary diagram of a gene fingerprint abstraction in which the contour shape is a fan shape created according to the present invention.
5 is a diagram summarizing the number of genotype types, the minimum genotype value, and the maximum genotype value for each hypermutable genetic locus.
6 is an example of a genetic fingerprint of an abstract service user, and is a diagram showing a genotype detection value and a normalization value when the guideline length is 20.
7 is a configuration diagram illustrating an abstraction generation system using genetic fingerprints according to an embodiment of the present invention.
8 is a process flow diagram illustrating a method for generating a genetic fingerprint abstraction according to an embodiment of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS Embodiments of the present invention will be described with reference to the accompanying drawings described below, wherein like reference numbers indicate like elements, but are not limited thereto.

Hereinafter, specific details for the implementation of the present invention will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present invention, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding elements are given the same reference numerals. In addition, in the description of the following embodiments, overlapping descriptions of the same or corresponding components may be omitted. However, omission of a description of a component does not intend that such a component is not included in an embodiment.

Advantages and features of the embodiments disclosed herein, and methods for achieving them, will become clear with reference to the embodiments described below in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms, and only these embodiments are provided to fully inform those skilled in the art related to the present invention the scope of the invention. it just becomes

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used in a meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

For example, the term "technology" may refer to systems, methods, computer readable instructions, modules, algorithms, hardware logic, and/or operations throughout the document and/or as permitted by the context described above.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present invention, but these may vary according to the intention of a person skilled in the related field, a precedent, or the emergence of new technology. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the invention. Therefore, the term used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, not simply the name of the term.

Expressions in the singular number in this specification include plural expressions unless the context clearly dictates that they are singular. Also, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a certain part includes a certain component in the entire specification, this means that it may further include other components without excluding other components unless otherwise stated.

In the present invention, the terms 'comprise', 'comprising' and the like may indicate that features, steps, operations, elements and/or components are present, but may indicate that such terms include one or more other functions, It is not excluded that steps, actions, elements, components, and/or combinations thereof may be added.

In the present invention, when a specific element is referred to as 'binding', 'combining', 'connecting', 'associating', or 'reacting' to any other element, the specific element is directly coupled to the other element. , can be combined, linked and/or associated, reacted, but not limited thereto. For example, one or more intermediate components may exist between certain components and other components. Also, in the present invention, “and/or” may include each of one or more items listed or a combination of at least a part of one or more items.

In the present invention, terms such as 'first' and 'second' are used to distinguish a specific component from other components, and the above-described components are not limited by these terms. For example, a 'first' element may be used to refer to an element having the same or similar shape as a 'second' element.

In the present invention, 'deoxyribonucleic acid (DNA)' is a basic substance constituting life containing genetic information, and may be a high molecular organic substance in which numerous unit substances called nucleotides are linked. Nucleotides are composed of a substance usually called a base and one molecule each of pentose and phosphoric acid, which are a type of carbohydrate. Base substances include adenine (A), guanine (G), and cytosine (C ), and thymine (T). There are four types of nucleotides constituting deoxyribonucleic acid, those with A, those with G, those with C, and those with T as basic substances, and these four nucleotides are linked in countless numbers. Deoxyribonucleic acid can be.

In the case of humans, DNA in a total of 46 chromosomes can contain about 3 billion base pairs, and each has a different DNA sequence except for identical twins. By analyzing DNA sequencing using these characteristics, it is possible to identify genetic differences between individuals. Since it is not easy to compare 3 billion pairs of DNA sequences from beginning to end, it is possible to use a technique that focuses on analyzing only the regions (STR, VNTR) that have different hypermutable base sequences for each individual due to severe polymorphism. At present, more than 18 repetitive regions of the hypermutable DNA sequence are known, and may be added or changed according to the development of human genome mapping technology.

In the present invention, a 'hypervariant genetic locus' may refer to a region where a hypermutable DNA sequence that can identify an individual is repeated. Currently, a plurality of hypermutational genetic loci have been discovered, and individual genetic fingerprint information can be derived by examining all of these hypermutable genetic loci.

An individual's genetic fingerprint can be detected by genetic identification methods such as short tandem repeat (STR) and variable number of tandem repeat (VNTR). STR is a technique for detecting DNA sites in which 2 to 9 short nucleotide sequences repeatedly appear, and VNTR may be a technique for identifying DNA sites in which 10 to 100 relatively long nucleotide sequences appear repeatedly. For example, when the nucleotide sequence of an arbitrary STR region is 'GTACGTACGTACGTAC', GTAC is duplicated 4 times, and the number of such repeat patterns may vary from person to person. That is, one person may have a nucleotide sequence in which GTAC overlaps 6 consecutive times in the STR region, and another person may have a nucleotide sequence in which GTAC overlaps 12 consecutive times in the STR region. In this way, the overlapping number (number) of the repetitive pattern can be detected as a genotype at the corresponding genetic locus. In addition, since humans each have two alleles inherited from their parents, if they inherit the same genotype from their parents, only one genotype exists at the locus, and if they inherit different genotypes from their parents, the genotype at the locus Two may exist.

In the present invention, 'genetic fingerprint' may refer to a set of genotypes at a plurality of hypermutable genetic loci. More specifically, it may be the result of genotypes detected in 18 hypermutable genetic loci in the STR genetic identification method. Each hypermutational locus may have one or two genotypes, and the combination of genotype detection values at the hypermutational locus may be unique information for identifying each individual since each individual is different. In the present invention, 'genetic profiling' may refer to an act of deriving a genetic fingerprint of an individual using a separated biological sample. Genetic profiling equipment may refer to equipment for deriving a genetic fingerprint of an individual by analyzing a separated biological sample.

In summary, more than 18 hypermutable genetic loci for identifying human individuals were detected by the STR genetic identification method, and one or two genotypes can be detected in each hypermutable locus. Since the combination of genotypes at hypermutational loci is different for each individual, a set of detection values of genotypes at a plurality of hypermutational loci can be used as a genetic fingerprint.

In the STR gene identification method, the hypermutational loci are D3S1358 (on chromosome 3), TH01 (on chromosome 11), D21S11 (on chromosome 21), D18S51 (on chromosome 18), and PENTA-E (on chromosome 18). present on chromosome 15), D5S818 (present on chromosome 5), D13S317 (present on chromosome 13), D7S820 (present on chromosome 7), D16S539 (present on chromosome 16), CSF1PO (present on chromosome 5) ), PENTA-D (on chromosome 21), Vwa (on chromosome 12), D8S1179 (on chromosome 8), TPOX (on chromosome 2), FGA (on chromosome 4), AMELOGENIN (existing on the X,Y chromosomes), D19S433 (existing on chromosome 19), D2S1338 (existing on chromosome 2), and the like.

Here, the D3S1358 locus may include 10 different genotypes. The number of genotype types of the D3S1358 locus is 10. For example, the genotype types of the D3S1358 locus may be 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20. The genotype minimum at the D3S1358 locus is 11 and the genotype maximum is 20. Through genetic profiling, humans can detect one or two genotypes out of 10 different genotype types at the D3S1358 locus.

The TH01 locus can contain 18 different genotypes. The number of genotype types in the TH01 locus is 18, for example, the genotype types in the TH01 locus are 3, 4, 5, 5.3, 6, 6.3, 7, 7.3, 8, 8.3, 9, 9.3, 10, 10.3 , 11, 12, 13, 13.3. The genotype minimum at the TH01 locus is 3 and the genotype maximum is 13.3. Through genetic profiling, humans can detect one or two genotypes out of 18 different genotype types at the TH01 locus.

The D21S11 locus can contain 32 different genotypes. The number of genotype types of the D21S11 locus is 32, for example, the genotype types of the D21S11 locus are 23, 24, 24.2, 25, 25.2, 26, 26.2, 27, 27.2, 28, 28.2, 29, 29.2, 30 , 30.2, 31, 31.2, 32, 32.2, 33, 33.2, 34, 34.2, 35, 35.2, 36, 36.2, 37, 37.2, 38, 38.2, 39. The genotype minimum at the D21S11 locus is 23 and the genotype maximum is 39. Through genetic profiling, humans can detect one or two of the 32 different genotype types at the D21S11 locus.

The D18S51 locus can contain 36 different genotypes. The number of genotype types of the D18S51 locus is 36, for example, the genotype types of the D18S51 locus are 7, 8, 9, 9.2, 10, 10.2, 11, 11.2, 12, 12.2, 13, 13.2, 14, 14.2 . The genotype minimum at the D18S51 locus is 7 and the genotype maximum is 27. Through genetic profiling, humans can detect one or two of the 36 different genotype types at the D18S51 locus.

The PENTA-E locus can contain 20 different genotypes. The number of genotype types in the PENTA-E locus is 20, for example, the genotype types in the PENTA-E locus are 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 , 17, 18, 19, 20, 21, 22, 23, 24. At the PENTA-E locus, the genotype minimum is 5 and the genotype maximum is 24. Through genetic profiling, humans can detect one or two of the 20 different genotype types at the PENTA-E locus.

The D5S818 locus can contain 12 different genotypes. The number of genotype types of the D5S818 locus is 12, and for example, the genotype types of the D5S818 locus may be 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17. The genotype minimum at the D5S818 locus is 6 and the genotype maximum is 17. Through genetic profiling, humans can detect one or two of the 12 different genotype types at the D5S818 locus.

The D13S317 locus can contain 10 different genotypes. The number of genotype types of the D13S317 locus is 10. For example, the genotype types of the D13S317 locus may be 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. At the D13S317 locus, the genotype minimum is 7 and the genotype maximum is 16. Through genetic profiling, humans can detect one or two genotypes out of 10 different genotype types at the D13S317 locus.

The D7S820 locus can contain 12 different genotypes. The number of genotype types of the D7S820 locus is 12. For example, the genotype types of the D7S820 locus may be 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. The genotype minimum at the D7S820 locus is 5 and the genotype maximum is 16. Through genetic profiling, humans can detect one or two of the 12 different genotype types at the D7S820 locus.

The D16S539 locus can contain 13 different genotypes. The number of genotype types of the D16S539 locus is 13, for example, the genotype types of the D16S539 locus can be 5, 6, 7, 8, 9, 10, 11, 12, 12.2, 13, 14, 15, 16. there is. At the D16S539 locus, the genotype minimum is 5 and the genotype maximum is 16. Through genetic profiling, humans can detect one or two genotypes out of 13 different genotype types at the D16S539 locus.

The CSF1PO locus can contain 12 different genotypes. The number of genotype types of the CSF1PO locus is 12, for example, the genotype types of the CSF1PO locus may be 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. At the CSF1PO locus, the minimum genotype value is 5 and the maximum genotype value is 16. Through genetic profiling, humans can detect one or two genotypes out of 12 different genotype types at the CSF1PO locus.

The PENTA-D locus can contain 9 different genotypes. The number of genotype types of the PENTA-D locus is 9. For example, the genotype types of the PENTA-D locus may be 7, 8, 9, 10, 11, 12, 13, 14, or 15. At the PENTA-D locus, the genotype minimum is 7 and the genotype maximum is 15. Through genetic profiling, humans can detect one or two genotypes out of nine different genotype types at the PENTA-D locus.

The Vwa locus can contain 18 different genotypes. The number of genotype types in the Vwa locus is 18, for example, the genotype types in the Vwa locus are 10, 11, 12, 13, 14, 15, 15.2, 16, 17, 18, 18.2, 19, 20, 21 , 22, 23, 24, or 25. The genotype minimum at the Vwa locus is 10 and the genotype maximum is 25. Through genetic profiling, humans can detect one or two genotypes of 18 different genotype types at the Vwa locus.

The D8S1179 locus can contain 14 different genotypes. The number of genotype types in the D8S1179 locus is 14. can be The genotype minimum at the D8S1179 locus is 7 and the genotype maximum is 20. Through genetic profiling, humans can detect one or two of 14 different genotype types at the D8S1179 locus.

The TPOX locus can contain 10 different genotypes. The number of genotype types of the TPOX locus is 10, for example, the genotype types of the TPOX locus may be 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14. The genotype minimum at the TPOX locus is 5 and the genotype maximum is 14. Through genetic profiling, humans can detect one or two of 10 different genotype types at the TPOX locus.

The FGA locus can contain 46 different genotypes. The number of genotype types in the FGA locus is 46, for example, the genotype types in the FGA locus are 16, 16.2, 17, 17.2, 18, 18.2, 19, 19.2, 20, 20.2, 21, 21.2, 22, 22.2 , 23, 23.2, 24, 24.2, 25, 25.2, 26, 26.2, 27, 27.2, 28, 28.2, 29, 29.2, 30, 30.2, 31, 31.2, 32, 32.2, 33.2, 34.2, 42.2, 4 3.2, 44.2 , 45.2, 46.2, 47.2, 48.2, 49.2, 50.2, 51.2. The genotype minimum at the FGA locus is 16 and the genotype maximum is 51.2. Through genetic profiling, humans can detect one or two of the 46 different genotype types at the FGA locus.

The AMELOGENIN locus can contain two different genotypes. The number of genotype types of the AMELOGENIN locus is two, for example, the genotype types of the AMELOGENIN locus may be X or Y. Through gene profiling, humans can detect one or both genotypes of two different genotype types at the AMELOGENIN locus. If only X is detected, the gender can be recognized as female. When XY is detected, gender can be recognized as male.

The D19S433 locus can contain 20 different genotypes. The number of genotype types of the D19S433 locus is 20. For example, the genotype types of the D19S433 locus are 9, 9.2, 10, 10.2, 11, 11.2, 12, 12.2, 13, 13.2, 14, 14.2, 15, 15.2 , 16, 16.2, 17, 17.2, 18, 18.2. The genotype minimum at the D19S433 locus is 9 and the genotype maximum is 18.2. Through genetic profiling, humans can detect one or two genotypes out of 20 different genotype types at the D19S433 locus.

The D2S1338 locus can contain 17 different genotypes. The number of genotype types at the D2S1338 locus is 17, for example, the genotype types at the D2S1338 locus are 14, 15, 16, 16.3, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 , 27, 28, 29. The genotype minimum at the D2S1338 locus is 14 and the genotype maximum is 29. Through genetic profiling, humans can detect one or two of 17 different genotype types at the D2S1338 locus.

The number and types of genotypes at each locus may vary depending on the genetic profiling equipment. In the present invention, the type of genotype detected at each genetic locus through gene profiling may be referred to as a genotype detection value.

The genetic fingerprint abstraction of the present invention may be a picture generated based on a genotype detection value of a user's hypermutable genetic locus. It determines the outline shape of the genetic fingerprint abstraction, determines the placement rules of the hypervariable genetic loci, and determines the size, length, shape, color and brightness of the unit genotype image.

In addition, it is possible to determine the background color of the abstraction, determine the background picture or background picture, change the location of the genetic fingerprint abstraction, add text to the background screen, and input the name, production date, message, etc. of the abstraction service user. there is.

In addition, genotype detection values for each supermutational locus of the user may be obtained, and unit genotype images may be arranged at locations according to the genotype detection values based on the arrangement rule of the hypermutational locus, thereby creating a genetic fingerprint abstraction.

The operating system described below constitutes one embodiment and is not intended to limit the scope of the claims to any one particular operating environment. It may be used in other environments without departing from the spirit and scope of the claimed subject matter.

1 illustrates an exemplary environment 100 in which the present invention's system for generating abstractions using genetic fingerprint information may operate. In some examples, various devices and/or components of environment 100 may include distributed computer resources 102 that can communicate with each other and with external devices via one or more networks 104 .

For example, networks 104 may include public networks such as the Internet, private networks such as institutional and/or private intranets, or some combination of private and public networks. Network 104 may include wired and/or wireless networks of any type, including but not limited to local area networks (LANs), wide area networks (WANs), satellite networks, cable networks, Wi-Fi networks, and WiMax networks. and may include a mobile communication network (eg, 3G, 4G, 5G, etc.) or any combination thereof. Network 104 may utilize communication protocols including packet-based and/or datagram-based protocols such as Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), or other types of protocols. there is. Moreover, network 104 may include a number of devices that facilitate network communications or form the hardware basis for the network, such as switches, routers, gateways, access points, firewalls, base stations, repeaters, and backbone devices.

In some embodiments, network 104 may further include devices that enable connection to a wireless network, such as a wireless access point (WAP). Embodiments according to the present invention are implemented through various electromagnetic frequencies (eg, radio frequencies), including WAPs that support the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard (eg, 802.11g, 802.11n, etc.). It can support connection through WAP that transmits and receives data.

In various embodiments, distributed computer resource 102 may include devices 106(1)-106(N). Embodiments of the present invention address scenarios in which devices 106 may include one or more computing devices to operate in a cluster or other grouped configuration to share resources, distribute load, increase performance, or for other purposes. support Device 106 may fall into various categories such as conventional server-type devices, desktop computer-type devices, mobile devices, special purpose devices, embedded devices, and/or wearable devices. Thus, although described as desktop and laptop computers, device 106 may include a variety of device types and is not limited to a particular type of device. Device 106 may include a desktop computer, server computer, web server computer, personal computer, mobile computer, laptop computer, tablet computer, wearable computer, implantable computer, communication device, automobile computer, network enabled television, thin client, terminal, PDA , game consoles, gaming devices, workstations, media players, personal video recorders (PVRs), set-top boxes, cameras, and integrated components for inclusion in computer devices. At least one of the devices 106(1) to 106(N) included in the distributed computer resource 102 may be connected to a gene profiling device, not shown, that detects a genetic fingerprint from a separated biological sample.

Apparatus 106 may include any type of computer device having one or more processing units 108 operably coupled to a computer readable media (CRM) 110 via a bus 112; , and in some cases may include one or more of these. These buses 112 may include system buses, data buses, address buses, PCI buses, mini PCI buses, and various local, peripheral and/or standalone buses.

Executable instructions stored in CRM 110 may be, for example, operating system 114, fingerprint abstract generation system 116 according to the present invention, and other modules, programs or applications loadable and executable by processing unit 108. can include Additionally, functionally described herein may be performed at least in part by one or more hardware logic components, such as an accelerator. For example, example types of hardware logic components that may be used include Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application-Specific Standard Products (APS), System-on-a-Chip System (SOC), Complex Programmable Logic Device (CPLD), and the like.

Device 106 includes one or more input/output (I/O) interfaces 118 to enable device 106 to peripheral input devices (e.g., keyboard, mouse, pen, game controller, voice input device, touch input devices, gesture input devices, etc.) and/or peripheral output devices (eg, displays, printers, etc.).

Device 106 may be a fingerprint abstraction service platform, and may have one or more input/outputs (I /O) network interface 120 . Such an input/output (I/O) network interface 120 may include one or more network interface controllers (NICs) or other types of transceiver devices for sending and receiving communications over the network 104 . Here, a consumer is a person who uses the genetic fingerprint abstraction generation service according to the present invention, provides his/her separated biological sample to genetic profiling equipment, and creates abstraction based on his/her genetic fingerprint in the genetic fingerprint abstraction service platform. It may include users of abstract services that are created. Alternatively, consumers include a genetic profiling service provider that operates genetic profiling equipment, receives biological samples separated from abstraction service users, analyzes them through genetic profiling equipment, extracts genetic fingerprint information, and provides it to the device 106. may be

Genetic profiling equipment, not shown, may be connected to the device 106, and the genetic profiling equipment obtains genetic fingerprint information of the abstraction service user by analyzing a separated biological sample of the abstraction service user, and displays the obtained genetic fingerprint information. device 106. The apparatus 106 may provide an abstraction generation service based on genetic fingerprints, that is, genotype information for each hypermutable genetic locus, to an abstraction service user.

Alternatively, a genetic profiling device, not shown, may be connected to the consumer computer device 126, and the gene profiling device analyzes the separated biological sample of the abstraction service user, and the gene profiling service provider analyzes the gene of the abstraction service user. Fingerprint information may also be provided to device 106 . Alternatively, the abstract service user may directly provide his or her genetic fingerprint information previously secured to the device 106 using a consumer computer device.

In the present invention, other devices related to the abstract generation service using genetic fingerprints may include consumer computer devices 126(1) to 126(7). Consumer computing devices 126 may fall into various categories, such as consumer-type devices, desktop computer-type devices, mobile devices, special purpose devices, embedded devices, and/or wearables. Although depicted as a mobile computing device that may have fewer computer resources than device 106, consumer computing device 126 may include a variety of device types and is not limited to any particular type of device.

The consumer computer device 126 is a device used by an abstraction service user, and includes a computer device, a desktop computer, a web server computer or a network attached storage device 126(1), a laptop computer, and a thin client necessary to perform an abstraction creation service. , terminals, or other mobile computers, personal data assistants (PDAs), wearable computers such as smart watches, computer navigation consumer computer devices, and global positioning system (GPS) devices (e.g., graphically represented by PDAs). Satellite-based navigation system device (126(2)), tablet computer or tablet hybrid computer (126(3)), smartphone, cell phone, cell phone-tablet hybrid device or other communication device (126(4)), handheld or console based gaming devices or other entertainment devices such as network-capable televisions, set-top boxes, media players, cameras graphically displayed as cameras or personal video recorders (PVRs) (126(5)), automotive computers or vehicle security such as vehicle control systems. system 126(6) and mechanical robotic device 126(7); As another example, the consumer computer device 126 may be a computer device used by a genetic profiling service provider or the like.

Consumer computer device 126 connects computer readable media (CRM) via buses that may include many more system buses, data buses, address buses, PCI buses, mini-PCI buses, and various local, peripheral and/or independent buses. ) (130).

The CRMs 110 and 130 described herein may include computer storage media and/or communication media. Computer storage media is a tangible term such as volatile memory, non-volatile memory and/or other permanent or auxiliary computer storage media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. of storage units, removable and non-removable computer storage media.

Computer storage media includes RAM, static RAM (SRAM), dynamic RAM (DRAM), phase change memory (PRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory memory (EEPROM), flash memory, DVD, optical storage medium, magnetic cassette, magnetic tape, solid state memory (SSD), and the like.

Unlike computer storage media, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism. As used herein, computer storage media does not include communication media.

Executable instructions stored in CRM 130 may include other modules, programs or applications that are loadable and executable by operating system 134 and processing unit 128 . Additionally or alternatively, what is functionally described herein may be performed at least in part by one or more hardware logic components, such as an accelerator. For example, example types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a chip systems (SOC), complex programmable logic devices (CPLD), and the like. For example, the accelerator could be a hybrid device from XILINX or ALTERA that includes a CPU embedded in an FPGA fabric.

Consumer computer devices 126 may also include one or more input/output (I/O) interfaces, including one or more network interfaces 136 and user interfaces 138, to communicate over network 104 to other consumer computer devices 126. ) or between other network devices, such as device 106. Such network interface 136 may include one or more network interface controllers (NICs) or other types of transceiver devices for sending and receiving communications over the network.

Consumer computer device 126 also includes user interface 138 so that consumer computer device 126 can receive user input, such as information about abstraction service users, information about abstraction creation, and the like. That is, the abstraction service user may use the consumer computer device 126 to access the abstraction service platform provided by the device 106, input information about the abstraction service user, register as a member, and log in, and in the abstraction service platform You can request an abstract generation service using your own genetic fingerprint provided. In addition, the genetic fingerprint abstract image created in this way can be created as a non-fungible token (NFT) and can be traded on the NFT exchange.

2 is a block diagram illustrating an exemplary computer device 200 configured to participate in a genetic fingerprinting abstraction generation system. In some embodiments, computer device 200 may be a single computer device that is a distributed computer resource, such as device 106 of FIG. 1 . In device 200, processing unit 202 may be, for example, a CPU type processing unit, a GPU type processing unit, a field programmable gate array (FPGA), a digital signal processor (DSP), or other hardware that may be driven by a CPU. It may include a processing unit 108 representing a logic component. For example, these hardware logic components include application-specific integrated circuits (ASICs), application-specific standard products (ASPs), system-on-a-chip (SOC) systems, and complex programmable logic devices (CPLDs). can include

In some embodiments, CRM 204 may represent CRM 110 and may store instructions executable by processing unit 202 integrated in device 200 as described above. The CRM 204 may also include an external processing unit such as an external CPU type processing unit 206, an external GPU type processing unit 208 and/or an FPGA type accelerator 210(1), a DSP type accelerator 210(2) Alternatively, it may store instructions executable by the external accelerator 210, such as the other accelerator 210(N).

In the illustrated embodiment, CRM 204 may also include data store 212 . In some embodiments, data store 212 may include a data store such as a database, data warehouse, or other type of structured or unstructured data store. In some embodiments, data store 212 may include one or more Hypertext Markup Language (HTML) tables, Resource Description Framework (RDF) tables, Web Ontology Language (OWL) tables, and/or Extensible Markup Language (XML) tables. It may include corpus and/or relational databases with one or more tables, indexes, stored procedures, etc. that enable access to data, such as web tables that contain Language) tables. For example, data store 212 may store data and/or instructions for operations of processes, applications, components, and/or modules stored in CRM 204 and executed by processing unit 202 .

The device 200 may further include one or more input/output (I/O) interfaces 216, which allow the device 200 to use a peripheral input device (eg, For example, user input devices including keyboards, mice, pens, game controllers, voice input devices, touch input devices, gesture input devices, cameras, etc., and peripheral output devices (eg displays, printers, etc.) can communicate. It may be an I/O interface 216 that allows communication with input/output devices located therein. Additionally, in device 200, network interface 218, which may be network interface 122, may represent a network interface controller (NIC) or other type of transceiver device for sending and receiving communications over a network.

In the illustrated embodiment, CRM 204 also includes operating system 220 , which may be operating system 114 . CRM 204 also includes a fingerprint abstraction generation system 222, which may be a fingerprint abstraction generation system 116. The fingerprint abstraction generation system 222 may include one or more modules and/or APIs, shown as blocks 224, 226, 228, 230, and 232, but this is only an example, and more or fewer of them. can

The functions described with respect to blocks 224, 226, 228, 230 and 232 may be combined to be performed by fewer modules and/or APIs or by a greater number of modules and/or APIs. can be divided and performed.

For example, block 224 may be an abstraction service user information acquisition unit 224 that obtains information about an abstraction service user received from the consumer computer device 126 . In various embodiments, the abstract service user information acquisition unit 224 may obtain information about the abstract service user including an identification ID, name, date of birth, and a separated biological sample delivery route of the abstract service user. It is preferable for the abstract service user to provide his/her separated biological sample to a genetic profiling device (not shown) or to provide his/her genetic fingerprint information to a genetic fingerprint information acquisition unit 226 described later.

The genetic profiling equipment may be operated by a platform service provider or a genetic profiling service provider. In the former case, the genetic profiling equipment may be configured to be directly connected to the computer device 200 or may be configured to be connected to the computer device 200 through a network as a remote distributed computer resource. In the latter case, the genetic profiling equipment may be connected to the consumer computer device of the genetic profiling service provider.

Block 226 is a genetic fingerprint information acquisition unit for acquiring genetic fingerprint information of an abstraction service user from at least one of genetic profiling equipment, distributed computer resources, consumer computer devices of abstract service users, and consumer computer devices of genetic profiling service providers. (226). In various embodiments, the genetic fingerprint information acquisition unit 226 may obtain genotype detection value information from a plurality of hypermutable genetic loci from genetic fingerprint information of an abstraction service user. Multiple hypermutational loci are D3S1358 (on chromosome 3), TH01 (on chromosome 11), D21S11 (on chromosome 21), D18S51 (on chromosome 18), PENTA-E (on chromosome 15) present on chromosome 5), D5S818 (present on chromosome 5), D13S317 (present on chromosome 13), D7S820 (present on chromosome 7), D16S539 (present on chromosome 16), CSF1PO (present on chromosome 5), PENTA -D (on chromosome 21), Vwa (on chromosome 12), D8S1179 (on chromosome 8), TPOX (on chromosome 2), FGA (on chromosome 4), AMELOGENIN (X, Y chromosome present), D19S433 (present on chromosome 19), D2S1338 (present on chromosome 2) may be included. One or two pieces of genotype information can be obtained for each hypermutable genetic locus. Genotype detection values at these hypermutable loci may be directly input by abstract service users.

Block 228 may be an abstraction item determination unit 228 that determines the contour shape and size of genetic fingerprint abstraction, arrangement rules of hypermutable genetic loci, background, and unit genotype image. The abstraction item determination unit 228 may inquire the user about the outline shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable genetic locus, the background and the unit genotype image, etc., and determine them based on the user's input, or may determine the previously stored pattern. may be determined automatically based on Of course, a pattern may be presented to the user, and based on the pattern selected by the user, the outline shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable locus, and the unit genotype image may be determined. The abstraction item determiner may store a separate pattern according to gender, recognize the user's gender based on the genotype detection value of the user's AMELOGENIN locus, and present a pattern according to gender.

The abstraction item determiner 228 may select at least one of a square, a rectangle, a circle, an ellipse, a fan shape, a star, a hexagon, an octagon, a decagon, and a graph shape as an outline shape of the genetic fingerprint abstraction. The contour shape of the genetic fingerprint abstraction of the present invention is not limited to the above-mentioned form and may be implemented in various other forms. FIG. 3 is an exemplary view of a genetic fingerprint abstraction created according to the present invention with a quadrangular contour shape, and FIG. 4 is an exemplary view of a genetic fingerprint abstraction generated according to the present invention with a fan-shaped contour shape. The graph type may represent contour shapes with different lengths for each genetic locus.

The abstraction item determiner 228 may determine an arrangement order, an arrangement direction for each locus, a guideline length for each locus, a position of a minimum genotype value, a position of a maximum genotype value, and the like with respect to the arrangement rule of the hypermutable locus.

Here, the arrangement order refers to D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, Vwa, D8S1179, TPOX, FGA, D19S433, D2S1338 required for abstraction creation It may be information indicating in what order the selected genetic loci are arranged. 3 and 4, the arrangement sequence is D3S1358, TH01, D21S11, D18S51, PENTA-E, ?? , FGA, D19S433, D2S1338, genetic fingerprint abstractions are shown.

Alignment direction is information that indicates in which direction the selected genetic loci are arranged, which is necessary for abstraction generation. When the contour shape of the genetic fingerprint abstraction is a rectangle or graph, the locus alignment direction can be determined in a vertical or horizontal direction. and when the contour shape of the genetic fingerprint abstraction is circular, elliptical, star, hexagonal, octagonal, decagonal, or fan-shaped, the arrangement direction of genetic loci may be determined in a radial direction having different rotation angles for each genetic locus. FIG. 3 shows a genetic fingerprint abstraction in which the genetic loci are arranged in a vertical direction, and FIG. 4 shows a genetic fingerprint abstraction in which the genetic loci are arranged in a radial direction.

The guideline for each locus may be a line guiding unit genotype images corresponding to the genotype detection values of the locus to be positioned on the same line. As shown in FIGS. 3 and 4 , when the locus arrangement direction is vertical or radial, each guideline 302 or 402 places the unit genotype image in the vertical or radial direction. In FIGS. 3 and 4 , the guideline is displayed as a dotted line, but the present invention is not limited thereto, and the guideline may be displayed as a solid line or may be processed transparently. That is, the guideline may be an invisible line.

The length of the guideline for each genetic locus may be determined according to the size of the genetic fingerprint abstraction. The guideline length for each genetic locus may be all the same, or the guideline length may be set differently for each genetic locus. That is, the guideline length of the locus may be determined based on the number of genotype types or the difference between the maximum value of the genotype and the minimum value of the genotype. In this case, if one end of all genetic loci is matched, the height of the other end is changed, creating a graphical genetic fingerprint abstraction.

The genotype minimum position may indicate the position of the genotype minimum value among both ends of the guideline of the genetic locus. When one end of the guideline of a genetic locus is a genotype minimum position, the other end may be a genotype maximum position.

In the exemplary view of FIG. 3 , the upper end 306 of the locus guideline may be the genotype maximum value position, and the lower end 308 may be the genotype minimum value position, or vice versa. In addition, in the exemplary view of FIG. 4 , the central end 406 of the locus guideline may be the genotype minimum value position, and the outer end 408 may be the genotype maximum value position, or vice versa.

The abstraction item determiner 228 may determine one of a background color, a background image, and a background picture, and may determine a location of a genetic fingerprint abstraction on the background and text to be added to the background screen. Through this text, it is possible to input the abstraction service user's name, production date, message, etc.

The abstraction item determiner 228 may determine a unit genotype image by selecting the shape, size, length, color, brightness, etc. of an image representing one genotype. 3 and 4 show unit genotype images 304 and 404 in a rectangular shape. The shape of the unit genotype image may be determined as a rectangle, a circle, a star shape, or the like. When the shape of the unit genotype image is a rectangle, the horizontal and vertical lengths can be freely selected.

The unit genotype image is located on each locus guideline, and the location of the central point of the unit genotype image may be determined based on the genotype detection value extracted from the user's genotype fingerprint.

Block 230 may be a genotype detection value normalization unit 230 that converts the genotype detection value of the hypermutable locus obtained in the genetic fingerprint information acquisition unit to a value within the guideline length of the corresponding genetic locus.

5 is a diagram summarizing the number of genotype types, the minimum genotype value, and the maximum genotype value for each hypermutable genetic locus.

There are 10 genotype types of the D3S1358 locus, the minimum genotype value is 11, and the maximum genotype value is 20. There are 18 genotype types of the TH01 locus, the minimum genotype value is 3, and the maximum genotype value is 13.3. There are 32 genotype types of the D21S11 locus, the minimum genotype value is 23, and the maximum genotype value is 39. There are 36 genotype types of the D18S51 locus, the minimum genotype value is 7, and the maximum genotype value is 27. There are 20 genotype types of the PENTA-E locus, the minimum genotype value is 5, and the maximum genotype value is 24. There are 12 genotype types of the D5S818 locus, the minimum genotype value is 6, and the maximum genotype value is 17. There are 10 genotype types of the D13S317 locus, the minimum genotype value is 7, and the maximum genotype value is 16. There are 12 genotype types of the D7S820 locus, the minimum genotype value is 5, and the maximum genotype value is 16. There are 13 genotype types of the D16S539 locus, the minimum genotype value is 5, and the maximum genotype value is 16. There are 12 genotype types of the CSF1PO locus, the minimum genotype value is 5, and the maximum genotype value is 16. There are 9 genotype types of the PENTA-D locus, the minimum genotype value is 7, and the maximum genotype value is 15. The genotype types of the Vwa locus are 18, the genotype minimum is 10, and the genotype maximum is 25. There are 14 genotype types of the D8S1179 locus, the minimum genotype value is 7, and the maximum genotype value is 12. There are 10 genotype types for the TPOX locus, with a minimum genotype of 5 and a maximum of 14 genotypes. There are 46 genotype types of the FGA locus, the minimum genotype value is 16, and the maximum genotype value is 51.2. There are 20 genotype types of the D19S433 locus, the minimum genotype value is 9, and the maximum genotype value is 18.2. There are 17 genotype types of the D2S1338 locus, the minimum genotype value is 14, and the maximum genotype value is 29.

As described above, the number of genotype types of the hypermutable locus, the minimum value of the genotype, and the maximum value of the genotype are all different. For these different genotype minimum and genotype maximum values, the genotype minimum is located at one end of the guideline for the locus, and the genotype maximum is located at the other end of the guideline for the locus. The genotype detection value should be placed in the middle of the guideline. That is, it should be normalized based on the length of the genotype detection value guideline, and the formula for the genotype detection value normalization unit 230 to normalize the genotype detection value based on the guideline length of the corresponding genetic locus is Equation 1 below same.

[Equation 1]

If the genotype detection value is normalized as shown in Equation 1, the genotype detection value is normalized to a value between 0 and the length of the guideline, and then it can be placed in the corresponding position of the guideline.

6 is an example of a genetic fingerprint of an abstract service user, and is a diagram showing a normalized value when the genotype detection value and the guideline length are all equal to 20. At the genetic locus D3S1358, two genotype detection values of 16 and 18 were derived from the two alleles, and normalizing them to the guideline length of 20 could be 5.0 and 7.0. In the genetic locus TH01, one genotype detection value of 9 is equally derived from the two alleles, and when this is normalized to the guideline length of 20, it may be 9.0. At the genetic locus D21S11, two genotype detection values of 29 and 34 are derived, and normalizing them to the guideline length of 20 can be 3.1 and 5.6. At the genetic locus D18S51, two genotype detection values of 13 and 14 are derived, and normalizing them to the guideline length of 20 can be 4.4 and 5.2. In the genetic locus PENTA-E, two genotype detection values of 8 and 12 are derived, and normalizing them to the guideline length of 20 can be 2.5 and 5.8. In the genetic locus D5S818, one genotype detection value of 11 is derived, and normalizing this to the guideline length of 20 can be 5.9. At the genetic locus D13S317, one genotype detection value out of 10 is derived, and when normalized to the guideline length of 20, it may be 3.8. At the genetic locus D7S820, one genotype detection value of 8 is derived, and normalizing this to the guideline length of 20 can be 3.8. At genetic locus D16S539, one genotype detection value of 9 is derived, and normalizing this to the guideline length of 20 may be 5.1. At the genetic locus CSF1PO, two genotype detection values of 11 and 12 are derived, and normalizing them to the guideline length of 20 may be 7.5 and 8.8. In the genetic locus PENTA-D, two genotype detection values of 9 and 10 are derived, and normalizing them to the guideline length of 20 can be 2.7 and 4.0. In the genetic locus vWA, two genotype detection values of 14 and 18 are derived, and normalizing them to the guideline length of 20 can be 3.2 and 6.4. At the genetic locus D8S1179, two genotype detection values of 12 and 14 are derived, and normalizing them to the guideline length of 20 can be 5.0 and 7.0. At the genetic locus TPOX, two genotype detection values of 9 and 11 are derived, and when normalized to the guideline 20, they may be 5.7 and 8.6. In the genetic locus FGA, two genotype detection values of 20 and 22 are derived, and when normalized to the guideline 20, it may be 1.6 or 2.3. At the genetic locus D19S433, one genotype detection value of 14 is derived, and if this is normalized to the guideline 20, it may be 5.5. At the genetic locus D2S1338, two genotype detection values of 20 and 23 are derived, and normalizing them to the guideline 20 may be 4.1 and 6.2.

Through this normalization, the genotype detection value can be converted to a value between 0 and the guideline length.

Block 232 is an abstraction generation unit that creates a genetic fingerprint abstraction by arranging unit genotype images at the guideline position of the locus corresponding to the normalized value of the genotype detection value for each genetic locus and color-processing the background and unit genotype image ( 232).

The abstraction generation unit 232 places a unit genotype image at the location of the guideline corresponding to the genotype detection value of each locus based on the outline shape and size of the genetic fingerprint abstraction determined in the abstraction item determination unit and the arrangement rule of the hypermutable genetic locus. to place An example of such an arrangement is as shown in FIGS. 3 and 4 . When arranging unit genotype images, it is preferable to use normalized values of genotype detection values. That is, in the example of FIG. 6, the unit genotype images of the locus D3S1358 are located at positions 5.0 and 7.0, respectively, the unit genotype images of the locus TH01 are located at positions 9.0, and the unit genotype images of the locus D21S11 are located at positions 3.1 and 7.0, respectively. 5.6, the unit genotype image of locus D8S1179 is located at 5.0 and 7.0, respectively, the unit genotype image of locus TPOX is located at 5.7 and 8.6, respectively, and the unit genotype image of locus FGA is located at 1.6, respectively , located at position 2.3, the unit genotype image of locus D19S433 is located at position 5.5, and the unit genotype image of locus D2S1338 can be located at positions 4.1 and 6.2, respectively.

The abstraction generating unit 232 may assign a single color to the background and the unit genotype image, or may perform a gradation process between the background color and the unit genotype image color.

In addition, the abstraction generating unit 232 may detect the gender based on the genotype detection value of the AMELOGENIN locus of the user, and may color-process a different color based on the detected gender.

Although not shown, the genetic fingerprint abstraction generation system 222 of the present invention selects and extracts hypermutable genetic locus and its genotype detection value information required to generate a genetic fingerprint abstract from the genetic fingerprint information obtained from the genetic fingerprint information acquisition unit. It may further include a genetic locus selection unit. The genetic locus selector may extract at least a plurality of hypermutable genetic loci among the hypermutable genetic loci constituting the genetic fingerprint and information on their genotype detection values. To this end, the types of hypermutable genetic loci constituting genetic fingerprint abstraction may be set in advance. In the present invention, genetic fingerprint abstraction can be generated using only some of the hypermutable genetic loci among hypermutable genetic loci used in genetic testing according to the abstraction pattern, outline shape and size, and the locus selection unit performs genetic fingerprint abstraction. It is possible to select the hypermutable genetic loci required for generation and their genotype detection values.

In addition, the genetic fingerprint abstraction generated by the abstraction generator 232 may be synthesized with the user's avatar, and may be synthesized as a background of the user's avatar or as an accessory of the user's avatar. In addition, genetic fingerprint abstraction is a digital artwork, which can be turned into a non-fungible token (NFT) and distributed on various NFT trading platforms.

Information about abstract service users, information necessary for generating genetic fingerprint abstracts, and the like may be stored in the data storage 212 . Accordingly, the data storage 212 may include a DB in which information on abstract service users is stored and a DB in which information on gene fingerprint abstract generation is stored.

In various embodiments, the DB storing information about the abstraction service user includes the abstraction service user's identification ID, name, date of birth, gender, details of consent to personal information, separated biological sample delivery route, separated biological sample acquisition date, and separated biological sample. Sample analysis equipment, genetic fingerprint information, abstract genetic fingerprint images, etc. may be stored.

The outline shape and size of the genetic fingerprint abstraction for each pattern, the arrangement rule of the hypermutable genetic locus, and the unit genotype image can be divided into gender and stored in the DB in which information on the generation of the genetic fingerprint abstraction is stored. In addition, the genotype minimum and genotype maximum values of each locus are stored so that they can be used when normalizing the genotype detection values. In addition, information necessary to generate genetic fingerprint abstraction may be stored.

Alternatively, some or all of the DBs mentioned above may include memory 214 on process unit 202, memory 234(1) on CPU type processing unit 206, memory on GPU type processing unit 208 ( 234(2)), memory 234(3) on FPGA type accelerator 210(1), memory 234(4) on DSP type accelerator 210(2), and/or other accelerators 210(N )) may be stored in a separate memory 234, such as the memory 234(M).

Bus 240 may be bus 112 and may include one or more of a system bus, a data bus, an address bus, a PCI bus, a Mini-PCI bus, and any of a variety of local, peripheral, and/or independent buses. , CRM 204 may be operatively coupled to processing unit 202 .

7 is a configuration diagram illustrating an abstraction generation system using genetic fingerprints according to an embodiment of the present invention.

The genetic fingerprint abstraction generation system includes an abstraction user information acquisition unit 702 that acquires information about abstraction service users, and genetic fingerprint information that obtains genetic fingerprint information including genotype detection values for each hypermutable genetic locus of abstraction service users. Unit 704, an abstraction item determining unit 706 for determining the outline shape and size of genetic fingerprint abstraction, arrangement rules for hypermutable genetic loci and unit genotype images, and genotype detection values of hypermutable genetic loci within the length of the guideline Genotype detection for each locus based on the contour shape and size of the genetic fingerprint abstraction determined by the genotype detection value normalization unit 708 and the abstraction item determination unit 706, which convert to a normalized value of and an abstraction generation unit 710 for generating a genetic fingerprint abstraction by arranging unit genotype images at guideline positions of genetic loci corresponding to normalization values of values and color-processing the background and unit genotype images.

The genetic fingerprint abstract generation system further includes a genetic locus selection unit that selects and extracts some hypermutable genetic loci and their genotype detection value information necessary for generating genetic fingerprint abstraction from the genetic fingerprint information obtained by the genetic fingerprint information acquisition unit 704. may also include To this end, the types of hypermutable genetic loci constituting genetic fingerprint abstraction may be preset in the data storage 712 .

The abstraction service user information obtaining unit 702 may obtain information about the abstraction service user including the abstraction service user's identification ID, name, date of birth, and a separated biological sample delivery route, and store the acquired information in the data storage 712. The abstraction service user information acquisition unit 702 requests the consumer computer device of the abstraction service user to input information about the abstraction service user, and obtains information about the abstraction service user based on the information input from the consumer computer device of the abstraction service user. can be obtained

The genetic fingerprint information acquisition unit 704 obtains genetic fingerprint information of an abstraction service user from at least one of directly connected genetic profiling equipment, distributed computer resources, a consumer computer device of an abstract service service user, and a consumer computer device of a gene profiling service provider. It can be obtained and stored in data store 412 . The genetic fingerprint information acquisition unit 704 may obtain information on a detected value of a genotype detected for each of a plurality of hypermutable genetic loci from genetic fingerprint information of an abstract service user.

According to an embodiment of the present invention, genetic profiling equipment is connected to the genetic fingerprint abstract generation system, and the genetic profiling equipment receives and analyzes a separated biological sample of the abstract service user to detect genetic fingerprint information of the abstract service user. and can be provided to the genetic fingerprint information acquisition unit 704.

According to another embodiment of the present invention, a gene profiling device is connected to a remote distributed computer resource of a genetic fingerprint abstraction generation system, and the gene profiling device receives and analyzes a separated biological sample of an abstraction service user. The genetic fingerprint information of can be detected and provided to the genetic fingerprint information acquisition unit 704 .

According to another embodiment of the present invention, an abstract service user or a genetic profiling service provider may input genetic fingerprint information of an abstract service user using a consumer computer device and provide the genetic fingerprint information acquisition unit 704 with the input. .

The abstraction item determination unit 706 may inquire the abstraction service user about the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable genetic locus, the background, and the unit genotype image, respectively, and determine based on the input of the abstraction service user. , may be automatically determined based on a pattern pre-stored in the data storage 712. At this time, a plurality of patterns may be presented to the abstraction service user, and based on the pattern selected by the abstraction service user, the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable locus, the background and unit genotype image, etc. may be determined. The data storage 712 stores a plurality of patterns defined according to gender, and the abstraction item determiner 706 recognizes the user's gender based on the genotype detection value of the AMELOGENIN locus of the abstraction service user, and Patterns can also be presented to abstraction service users.

The abstraction item determiner 706 may select at least one of a square, a rectangle, a circle, an ellipse, a star, a hexagon, an octagon, a decagon, and a fan shape as an outline shape of the genetic fingerprint abstraction. The abstraction item determiner 706 may determine an arrangement order, an arrangement direction for each locus, a guideline length for each locus, a position of a minimum genotype value, a position of a maximum genotype value, and the like with respect to the arrangement rule of the hypermutable locus.

Here, the arrangement order refers to D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, Vwa, D8S1179, TPOX, FGA, D19S433, D2S1338 required for abstraction creation It may be information indicating in what order the selected genetic loci are arranged. Alignment direction is information indicating in which direction the genetic loci selected are necessary for abstraction creation. When the contour shape of the genetic fingerprint abstraction is square, rectangular, or graph-shaped, the loci are arranged vertically or horizontally. can be determined, and if the contour shape of the genetic fingerprint abstraction is circular, elliptical, star, hexagonal, octagonal, decagonal, or fan-shaped, the arrangement direction of genetic loci may be determined in a radial direction having different rotation angles for each genetic locus. The guideline for each locus is a line guiding unit genotype images corresponding to the detected genotype values of the locus to be located on the same line, and may be a visible line or an invisible line. The length of the guideline for each locus may be determined according to the size of the genetic fingerprint abstraction, and the length of the guideline for each locus may be the same for all loci guidelines, or the length of the guideline for each locus may be different. may be set. The genotype minimum position may indicate the position of the genotype minimum value among both ends of the guideline of the genetic locus. When one end of the guideline of a genetic locus is a genotype minimum position, the other end may be a genotype maximum position.

The abstraction item determining unit 706 may determine one of a background color, a background image, and a background picture, and may determine a location of a genetic fingerprint abstraction on the background and text to be added to the background screen. Through this text, it is possible to input the abstraction service user's name, production date, message, etc.

The abstraction item determining unit 706 may determine a unit genotype image by selecting the shape, size, length, color, brightness, etc. of an image representing one genotype. The unit genotype image is located on each locus guideline, and the location of the central point of the unit genotype image may be determined based on the genotype detection value extracted from the user's genotype fingerprint.

The genotype detection value normalization unit 708 normalizes the genotype detection value of the locus to be within the guideline length of the corresponding locus based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each locus. To this end, the data storage 712 preferably stores the minimum genotype value and the maximum genotype value for each hypervariable genetic locus.

The formula for normalizing the genotype detection value based on the guideline length of the locus is as shown in Equation 1. Normalizing the genotype detection value normalizes the genotype detection value to a value between 0 and the length of the guideline, and then can be placed in the corresponding position of the guideline.

The abstraction generation unit 710 determines the position of the guideline corresponding to the genotype detection value of each locus based on the outline shape and size of the genetic fingerprint abstraction determined by the abstraction item determination unit 706 and the arrangement rule of the hypermutable genetic locus. The unit genotype image is placed, and the background and the unit genotype image are color-processed to create a genetic fingerprint abstraction. Here, a single color may be assigned to the background and the unit genotype image, or a gradation process may be performed between the background color and the unit genotype image color. The abstraction generating unit 232 may detect the gender based on the genotype detection value of the AMELOGENIN locus of the user, and may process the color in a different color based on the detected gender.

8 is a process flow diagram illustrating a method for generating a genetic fingerprint abstraction according to an embodiment of the present invention. The operation of the example process is shown in individual blocks and described with reference to these blocks. A process is shown as a logical flow of blocks, and each block may represent one or more operations that may be implemented in hardware, software, or a combination of both. In the context of software, operations represent computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processors, enable the one or more processors to perform the recited operations. Computer-executable instructions typically include routines, programs, objects, modules, components, data structures, those that perform particular functions or implement particular abstract data types. The order in which operations are described should not be construed as limiting, and any number of operations described may be subdivided into multiple sub-operations in any order or executed in parallel.

At block S802, the computer system may obtain information about the abstract service user. Information on abstraction service users may include information such as the abstraction service user’s identification ID, name, date of birth, gender, personal information consent details, and separated biological sample delivery routes. It can be stored in a data store. Additional information such as the date of obtaining the separated biological sample, equipment for analyzing the separated biological sample, genetic fingerprint information, abstract genetic fingerprint image, etc. may be additionally stored in the DB in which information on abstract service users is stored.

In block S804, the computer system may obtain genetic fingerprint information of the abstract service user. The genetic fingerprint information of the abstract service user may be acquired from at least one of a genetic profiling device, a distributed computer resource, a consumer computer device of the abstract service user, and a consumer computer device of the gene profiling service provider. The genetic fingerprint information of users of the abstraction service may include genotype detection value information detected for each hypermutation locus, and the plurality of hypermutation loci are D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, Vwa, D8S1179, TPOX, FGA, AMELOGENIN, D19S433, D2S1338 may be included. One or two pieces of genotype detection value information can be obtained for each hypermutational genetic locus.

In block S806, the computer system selects and extracts information on the hypermutable genetic locus and its genotype detection value necessary for generating the genetic fingerprint abstraction from the genetic fingerprint information of the user of the abstraction service. To this end, the types of hypermutable genetic loci constituting genetic fingerprint abstraction may be stored in advance in a data repository.

At block S808, the computer system may determine the contour shape and size of the genetic fingerprint abstraction, the placement rules of the hypervariant loci, the background and the unit genotype image. The computer system may inquire the abstraction service user about the outline shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable genetic locus, and the unit genotype image, respectively, and determine based on the input of the abstraction service user, or pre-stored in the data repository. It can also make decisions automatically based on patterns. At this time, a plurality of patterns may be presented to the abstraction service user, and based on the pattern selected by the abstraction service user, the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable genetic locus, and the unit genotype image may be determined. The contour shape of the genetic fingerprint abstraction may be determined as at least one of a square, rectangle, circle, ellipse, sector, star, hexagon, octagon, decagon, and graph shape, and for the arrangement rule of the hypervariable genetic locus, the arrangement order, genetic The arrangement direction for each locus, the length of the guideline for each locus, the location of the minimum value of the genotype in the guideline, the location of the maximum value of the genotype in the guideline, etc. may be determined. In addition, a unit genotype image may be determined based on the shape, size, length, color, etc. of an image representing one genotype.

In block S810, the computer system may convert the genotype detection value of the hypermutable locus to be a normalized value within the length of the guideline. The computer system normalizes the genotype detection value of the locus to be within the length of the guideline of the locus based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each locus. If the genotype detection value is normalized, the genotype detection value becomes a value between 0 and the length of the guideline, and then it can be placed in the corresponding position of the guideline.

In block S812, the computer system determines the guideline of the genetic locus corresponding to the normalized value of the genotype detection value for each locus, based on the contour shape and size of the genetic fingerprint abstraction determined in block S808 and the arrangement rule of the hypermutable locus. A genetic fingerprint abstraction can be created by placing unit genotype images in locations and color-processing the background and unit genotype images. Here, a single color may be assigned to the background and the unit genotype image, or a gradation process may be performed between the background color and the unit genotype image color.

All of the methods and processes described above may be implemented and fully automated as software code modules executed by one or more general purpose computers or processors. Code modules may be stored on any type of computer readable storage medium or other computer storage device. Some or all of the methods may be implemented with specialized computer hardware.

Any routine descriptions, elements or blocks of flow diagrams described herein and/or shown in the accompanying drawings represent code, modules, segments or portions that contain one or more executable instructions for implementing a particular logical function or element. It should be understood as potentially indicating. Routine. Alternate examples are included within the scope of the examples described herein, in which elements or functions may be deleted or executed in order from those shown or discussed, either substantially synchronously or in reverse order, depending on the functionality to be understood herein.

It should be understood that many variations and modifications may be made to the above-described embodiments, and that elements are among other permissible examples. All such modifications and variations are intended to be included within the scope of this disclosure and protected by the following claims. Embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. A computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on a computer readable recording medium may be specially designed and configured for the present invention or may be known and usable to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes such as those produced by a compiler. The hardware device may be configured to act as one or more software modules to perform processing according to the present invention and vice versa.

In the above, the present invention has been described by specific details such as specific components and limited embodiments and drawings, but these are provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , Those skilled in the art to which the present invention pertains may seek various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and all modifications equivalent or equivalent to the claims as well as the claims described later belong to the scope of the spirit of the present invention. something to do.

702: abstraction service user information acquisition unit
704: genetic fingerprint information acquisition unit
706: abstraction item determination unit
708: genotype detection value normalization unit
710: abstraction generation unit

Claims (16)

an abstraction user information acquisition unit that acquires information about an abstraction service user;
a genetic fingerprint information acquiring unit that obtains genetic fingerprint information including one or two genotype detection values for each hypermutable genetic locus of the abstract service user;
Each locus that guides the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypervariable locus, the background, the unit genotype image, and the unit genotype image corresponding to one or two genotype detection values per locus to be collinear. an abstraction item determining unit that determines guidelines;
a genotype detection value normalization unit that converts one or two genotype detection values for each genetic locus into a normalization value within the length of the guideline for each genetic locus; and
Based on the contour shape and size of the genetic fingerprint abstraction determined in the abstraction item determination unit and the arrangement rule of the hypermutable locus, the location of the guideline corresponding to the normalized value of one or two genotype detection values for each locus Including an abstraction generation unit for arranging unit genotype images and generating genetic fingerprint abstractions by color processing the background and the arranged unit genotype images;
An abstraction generation system using genetic fingerprinting.
According to claim 1,
Further comprising a genetic locus selection unit for selecting and extracting some hypermutational genetic locus and genotype detection value information necessary for generating the genetic fingerprint abstraction from the genetic fingerprint information obtained by the genetic fingerprint information acquisition unit,
An abstraction generation system using genetic fingerprinting.
According to claim 1,
The hypermutated loci are D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, vWA, D8S1179, TPOX, FGA, AMELOGENIN, D19S433 and D2S1338 contain at least one of doing,
An abstraction generation system using genetic fingerprinting.
According to claim 1,
The contour shape of the genetic fingerprint abstraction is,
Including at least one of a square, a rectangle, a circle, an ellipse, a fan shape, a star, a hexagon, an octagon, a decagon, and a graph shape,
An abstraction generation system using genetic fingerprinting.
According to claim 1,
The arrangement rule of the hypermutable locus is
Including at least one of the position of the minimum genotype and the position of the maximum genotype among the arrangement order of the hypermutable locus, the arrangement direction for each locus, the guideline length for each locus, and the guideline for each locus,
An abstraction generation system using genetic fingerprinting.
The method of claim 1, wherein the abstraction item determining unit comprises:
determining the background color, background image, background picture, location of the genetic fingerprint abstraction on the background and text to be added to the background screen;
An abstraction generation system using genetic fingerprinting.
According to claim 1,
The unit genotype image,
Including at least one of the shape, size, length, color and brightness of an image representing one genotype,
An abstraction generation system using genetic fingerprinting.
According to claim 1,
The genotype detection value normalization unit
Normalizing the genotype detection value to be a value within the length of the guideline based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each hypermutable locus,
An abstraction generation system using genetic fingerprinting.
In a computer system, a method for generating an abstraction using a genetic fingerprint implemented by at least one processor, comprising:
obtaining information about an abstraction service user;
obtaining genetic fingerprint information including one or two genotype detection values for each hypermutable genetic locus of the abstract service user;
Each locus that guides the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypervariable locus, the background, the unit genotype image, and the unit genotype image corresponding to one or two genotype detection values per locus to be collinear. determining guidelines;
converting one or two of the genotype detection values for each genetic locus into a normalized value within the length of a guideline for each genetic locus; and
Based on the determined outline shape and size of the genetic fingerprint abstraction and the arrangement rule of the hypermutable locus, the unit genotype image is placed at the position of the guideline corresponding to the normalized value of one or two genotype detection values for each locus. And generating a genetic fingerprint abstraction by color processing the background and the arranged unit genotype image.
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
Further comprising the step of selecting and extracting some hypermutation locus and genotype detection value information necessary for generating the genetic fingerprint abstraction from the obtained genetic fingerprint information.
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The hypermutated loci are D3S1358, TH01, D21S11, D18S51, PENTA-E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA-D, vWA, D8S1179, TPOX, FGA, AMELOGENIN, D19S433 and D2S1338 contain at least one of doing,
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The contour shape of the genetic fingerprint abstraction is,
Including at least one of a square, a rectangle, a circle, an ellipse, a fan shape, a star, a hexagon, an octagon, a decagon, and a graph shape,
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The arrangement rule of the hypermutable genetic locus is,
Including at least one of the position of the minimum genotype and the position of the maximum genotype among the arrangement order of the hypermutable locus, the arrangement direction for each locus, the guideline length for each locus, and the guideline for each locus,
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The step of determining the contour shape and size of the genetic fingerprint abstraction, the arrangement rule of the hypermutable genetic locus, the background and the unit genotype image,
Determining the background color, background image, background picture, location of the genetic fingerprint abstraction on the background and text to be added to the background screen,
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The unit genotype image,
Including at least one of the shape, size, length, color and brightness of an image representing one genotype,
A method for generating abstractions using genetic fingerprinting.
According to claim 9,
The step of converting the genotype detection value to a normalized value within the length of the guideline for each locus,
Normalizing the genotype detection value to be a value within the length of the guideline based on the minimum genotype value, the maximum genotype value, and the length of the guideline for each hypermutable locus,
A method for generating abstractions using genetic fingerprinting.

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