Method and system for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa

Abstract

The method (100) for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates comprises the steps of: - collecting (105) a microbiome sample on a facial site associated with facial coordinates, - measuring (110) an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample, - calculating (115) at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and - providing (120) at least one calculated value of facial microbiome taxa and the associated facial coordinates.

Description

[0001] METHOD AND SYSTEM FOR PROVIDING A PLURALITY OF LOCALIZED VALUES REPRESENTATIVE OF QUANTITIES OF AT LEAST ONE FACIAL MICROBIOME TAXA

[0002] Technical field of the invention

[0003]

[0001] The present invention relates to a method for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, to a system for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, as well as ratios between microbial taxa and / or microbial diversity indexes and to a corresponding computer program product and computer-readable storage medium.

[0004]

[0002] The present invention is applicable to the field of skin care product design and manufacturing.

[0005] Background of the invention

[0006]

[0003] Microbiome science is a complex matter which has, in the context of skin care product design and manufacturing, significant implications.

[0007]

[0004] Indeed, certain microbiome taxa contribute positively or negatively to skin health. Skin health, in this instance, may be defined in terms of: maintenance of the skin barrier function modulation of immune response of the skin, protection against pathogens, and / or influence on skin pH.

[0008]

[0005] Thus, there exists a need for skin care products which favor microbiome taxa contributing positively to skin health and / or limit the influence of microbiome taxa which contribute negatively.

[0009]

[0006] One key difficulty in assessing the impact of such skin care products is the significant variation in microbiome composition in different locations on the human body and human face. Therefore, knowledge of the microbiome composition in these different locations is key to design and manufacture suited skin care products.

[0010]

[0007] In current approaches to the above problem, relative quantities of microbiome taxa on a human face are determined based on 16S rRNA sequencing (for bacteria) and ITS sequencing (for fungi), or whole genome sequencing (also called “shotgun sequencing”) based on selective sampling of a number of sites on the human face.

[0011]

[0008] US2022 / 325323A describes a method for treating or preventing a metabolic syndrome or a condition associated therewith in a subject in need thereof. The method comprises for example determining of the abundance or abundance ratio of Corynebacteri aceae species and / or Staphylococcaceae. The method may comprise, inter alia, collecting a biological sample from skin of a subject and analyzing the microbiome in the sample derived from the skin. In passing it is mentioned that the step of analyzing the skin microbiome may comprise identification of a specific nucleotide sequence via amplification , such as by PCR . The method further comprises administering to a subject determined as being at an increased risk , a therapeutically effective amount of a pharmaceutical or a nutraceutical composition comprising a certain agent thereby treating or preventing a metabolic syndrome or a condition associated therewith in a subject.

[0012]

[0009] EP3036338B1 describes a system for assessing microbiota of skin. The system includes the use of a skin-covering material having an inner surface and an outer surface, the inner surface substantially conforming in shape to a topography of a skin surface of an individual and including a microbe-capture region. The system also includes an image-capture device including circuitry to capture an image of the inner surface of the skin-covering material and to transform the captured image into a digital output. This system is cumbersome and resource intensive.

[0013]

[0010] Unfortunately, the approach of US2022 / 325323A only provides limited input producing relative quantities of specific limited microbiome taxa associated with a specific limited site, whilst the approach of EP3036338B1 only works in the context of a brute force method in which all sites on the human face are sampled.

[0014]

[0011] Hence, the current models struggle to capture, in an economic and scalable manner, the distribution of microbial communities across facial regions and other parts of the skin. It would be an advancement in the art to provide a method, system and / or corresponding computer program to close this gap.

[0015] Summary of the invention

[0016]

[0012] The present invention is intended to remedy all or part of these disadvantages.

[0017]

[0013] To this effect, according to a first aspect, the present invention aims at a method for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, which comprises the steps of:

[0018] - collecting a microbiome sample on a facial site associated with facial coordinates,

[0019] - measuring an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,

[0020] - calculating at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and

[0021] - providing at least one calculated value of facial microbiome taxa and the associated facial coordinates.

[0014] Such an approach allows for the provision of absolute quantities of microbiome taxa measured in different sites of the human face as well as calculated microbiome taxa quantities for sites in which no measurements are performed.

[0022]

[0015] In particular embodiments, during the step of providing, a digital representation of a human face is provided, said at least one calculated value being displayed as a layer of the representation of said human face.

[0023]

[0016] Such provisions allow for easier cognitive understanding of the composition of one’s facial microbiome composition.

[0024]

[0017] In particular embodiments, the method object of the present invention further comprises a step of acquiring at least one image of a human face and which, during the step of providing, said at least one calculated value being displayed as a layer of said acquired image.

[0025]

[0018] Such provisions allow for customization of the rendering of the face as well as real-time visibility of one’s facial microbiome composition.

[0026]

[0019] In particular embodiments, a facial microbiome taxon corresponds to either a bacterial genus, species or strain or a fungal genus, species or strain.

[0027]

[0020] In particular embodiments, the method object of the present invention further comprises a step of measuring physical, chemical and / or biological properties of a facial site associated with facial coordinates, the step of calculating being performed as a function of said measured physical, chemical and / or biological properties.

[0028]

[0021] Said embodiments allow for a more accurate calculation of the microbiome taxa associated with a site of a human face.

[0029]

[0022] In particular embodiments, the step of measuring further comprises the steps of:

[0030] - measuring an absolute quantity of a facial microbiome taxon for at least one microbiome sample, via a real-time polymerase chain reaction analysis of each said collected microbiome sample, to yield a first dataset comprising values of absolute quantities of facial microbiome taxon associated with a first set of facial sites,

[0031] - generating with the help of the first dataset, a predictive value for additional quantities of facial microbiome taxon for a second set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the second set of facial sites are different from the facial sites in the first set of facial sites,

[0032] - adding the additional quantities of facial microbiome taxon for the second set of facial sites to the first dataset to yield a second dataset, and in which the step of calculating is configured to calculate, as a function of the second dataset, at least one value representative of a quantity of facial microbiome taxa for a third set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the third set of facial sites are different from the facial sites in the first and second set of facial sites, and wherein each calculated value is associated with facial coordinates, wherein this calculation is configured to perform an interpolation of the values of the second dataset to obtain at least one said value representative of a quantity for the third set of facial coordinaites, and and in which the step of providing is configured to provide said calculated value of facial microbiome taxa and the associated facial coordinates upon a three-dimensional representation of a face of a user.

[0023] In particular embodiments, the step of calculating comprises a step of thin plate spline interpolation configured to associate interpolated absolute microbiome taxa quantities with facial coordinates.

[0033]

[0024] In particular embodiments, the method object of the present invention further comprises:

[0034] - a step of selecting a microbiome-targeting bioactive compound digital identifier, representative of a materializable microbiome-targeting bioactive compound, and

[0035] - a step of computing an adjusted quantity of facial microbiome taxa, each calculated value being associated with facial coordinates, the step of providing being configured to provide the adjusted quantity of facial microbiome taxa.

[0036]

[0025] Such provisions allow for easier cognitive understanding of the impact of a skin care product on one’s facial microbiome composition.

[0037]

[0026] In particular embodiments, the method object of the present invention further comprises a step of sending a digital command representative of an instruction of materialising at least one microbiometargeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.

[0038]

[0027] In particular embodiments, the method object of the present invention further comprises a step of materialising at least one microbiome-targeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.

[0039]

[0028] According to a second aspect, the present invention aims at a computer program product characterized in that it comprises instructions which upon execution by a computer cause the computer to execute the method object of the present invention.

[0040]

[0029] According to a third aspect, the present invention aims at a computer-readable storage medium storing programming instructions which upon execution by a computer cause the computer to execute the method object of the present invention.

[0041]

[0030] According to a fourth aspect, the present invention aims at a system for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, which comprises means of:

[0042] - collecting a microbiome sample on a facial site associated with facial coordinates, - measuring an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,

[0043] - calculating at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and

[0044] - providing at least one calculated value of facial microbiome taxa and the associated facial coordinates.

[0045]

[0031] The second to fourth aspects of the present invention exhibit the same advantages as the related first aspect.

[0046] Brief description of the drawings

[0047]

[0032] Other advantages, purposes and particular characteristics of the invention shall be apparent from the following non-exhaustive description of at least one particular embodiment of the present invention, in relation to the drawings annexed hereto, in which:

[0048] [Figure 1] represents, schematically, a succession of steps of a particular embodiment of the method object of the present invention,

[0049] [Figure 2] represents, schematically, a particular embodiment of the system object of the present invention,

[0050] [Figure 3] represents, schematically, a computer system with which an embodiment of the method subject of the present invention can be implemented,

[0051] [Figure 4A] represents, schematically, the facial map of local microbial species diversities, measured by Shannon's index, and

[0052] [Figure 4B] represents, schematically, the facial map of local Cutibacterium acnes species presence.

[0053] [Figure 5] represents in the image on the right hand side: a visual provided a combination of the machine learning device and thin plate spline, as illustrated in example 2, using only real-time PCR data of samples of a limited number of 7 sampling sites (“model 7 sites”); and in the image on the left hand side: a visual provided by only using thin plate spline, based on real-time PCR of samples of 23 sampling sites (“experimental”).

[0054] Detailed description of the invention

[0055]

[0033] This description is not exhaustive, as each feature of one embodiment may be combined with any other feature of any other embodiment in an advantageous manner. Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0056]

[0034] The indefinite articles ‘a’ and ‘an’, as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean ‘at least one’.

[0057]

[0035] The phrase ‘and / or’, as used herein in the specification and in the claims, should be understood to mean ‘either or both’ of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with ‘and / or’ should be construed in the same fashion, i.e. ‘one or more’ of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the ‘and / or’ clause whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to ‘A and / or B’, when used in conjunction with open-ended language such as ‘comprising’ can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0058]

[0036] As used herein in the specification and in the claims, ‘or’ should be understood to have the same meaning as ‘and / or’ as defined above. For example, when separating items in a list, ‘or’ or ‘and / or’ shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as ‘only one of or ‘exactly one of, or, when used in the claims, ‘consisting of, will refer to the inclusion of exactly one element of a number or list of elements. In general, the term ‘or’ as used herein shall only be interpreted as indicating exclusive alternatives (i.e. ‘one or the other but not both’) when preceded by terms of exclusivity, such as ‘either,’ ‘one of,’ ‘only one of, or ‘exactly one of. ‘Consisting essentially of,’ when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0059]

[0037] As used herein in the specification and in the claims, the phrase ‘at least one’, in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase ‘at least one’ refers, whether related or unrelated to those elements specifically identified. Thus, as a nonlimiting example, ‘at least one of A and B’ (or, equivalently, ‘at least one of A or B’, or, equivalently ‘at least one of A and / or B’) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

[0060]

[0038] In the claims, as well as in the specification above, all transitional phrases such as ‘comprising,’ ‘including,’ ‘carrying,’ ‘having,’ ‘containing,’ ‘involving,’ ‘holding,’ ‘composed of, and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases ‘consisting of and ‘consisting essentially of shall be closed or semi-closed transitional phrases, respectively.

[0061]

[0039] It should be noted at this point that the figures are not to scale.

[0062]

[0040] Unless explicitly indicated otherwise, the various embodiments of the invention described herein can be cross-combined.

[0063]

[0041] The “microbiome” where used as such, preferably refers to the collection of all microorganisms, such as bacteria, fungi, viruses, and archaea, that live in a particular environment

[0064]

[0042] In a general manner, the terms ‘digital identifier’ or ‘digital representation’ refer to any bijective digital representation of a physical item, such as a molecule. Such a digital identifier may correspond to, for example, an entry in a database. A digital identifier may refer to a label representative of the name, chemical structure, or internal reference of an ingredient, for example.

[0065]

[0043] In the context of the present description, the term “materialized” is intended as existing outside of the digital environment of the present invention. ‘Materialized’ may mean, for example, readily found in nature or synthesized in a laboratory or chemical plant. In any event, a materialized fragrance molecule digital identifier presents a tangible reality. The terms ‘to be materialized or ‘materializable refer to the act of materialization of a fragrance ingredient associated with a digital identifier.

[0066]

[0044] As used herein, the terms “means of inputting” referto, for example, a keyboard, mouse and / or touchscreen adapted to interact with a computing system in such a way to collect user input. In variants, the means of inputting are logical in nature, such as a network port of a computing system configured to receive an input command transmitted electronically. Such an input means may be associated to a GUI (Graphic User Interface) shown to a user or an API (Application programming interface). In other variants, the means of inputting may be a sensor configured to measure a specified physical parameter relevant for the intended use case. Examples of means of inputting are disclosed in regard to figure 2.

[0045] As used herein, the terms “computing system”, “computer”, or “computer system” designate any electronic calculation device, whether unitary or distributed, capable of receiving numerical inputs and providing numerical outputs by and to any sort of interface, digital and / or analog. Typically, a computing system designates either a computer executing a software having access to data storage or a client-server architecture wherein the data and / or calculation is performed at the server side while the client side acts as an interface. Examples of such computing systems are disclosed in regard to figure 2.

[0067]

[0046] In the context of the present invention, the terms “microbiome taxa” refer to microorganisms - such as bacteria, fungi, viruses, and archaea - that inhabit a specific environment, including the human body. Such a facial microbiome taxon preferably corresponds to either a bacterial or a fungal genus, species or strain or strain. Such microorganisms may refer to, for example, C. acnes, S. aureus, S. epidermidis, S. hominis, Malassezia restricta and M. globosa among other known microorganisms known to impact skin / scalp health.

[0068]

[0047] In the context of the present invention, the terms “microbiome-targeting bioactive compound” refer to a substance that exerts effects on the microbiome, the collective community of microorganisms in a particular environment, such as the human face. These compounds can be natural or synthetic and are characterized by their ability to influence the composition, behavior, and overall functionality of microbial communities. The effects of these bioactive compounds on the microbiome can range from promoting the growth of beneficial microorganisms to inhibiting pathogenic ones, thereby potentially impacting the health of the host organism or the ecological balance of an environment.

[0069]

[0048] Such microbiome-targeting bioactive compounds may refer to "prebiotics", which support growth of microbial species, ultimately providing health benefit for the host, "probiotics" and "postbiotics" which can modulate composition of the microflora, again providing health benefit (such as anti-aging for example) for the host.

[0070]

[0049] In the context of the present invention, the terms “facial site” refer to specific areas on the face that are targeted for treatment or analysis. Skin care routines and products often differentiate between these areas because the characteristics of the skin and needs can vary significantly across different parts of the face. For example, the skin under the eyes is thinner and more delicate than the skin on the forehead or cheeks, and therefore might require different types of care or products.

[0071]

[0050] Such facial sites may correspond to facial sites such as disclosed in Voegeli R, Gierschendorf J, Summers B, Rawlings AV. Facial skin mapping: from single point bio-instrumental evaluation to continuous visualization of skin hydration, barrier function, skin surface pH, and sebum in different ethnic skin types. Int J Cosmet Sci. 2019 Oct;41 (5):411-424. doi: 10.111 l / ics.12562. Epub 2019 Aug 30. PMID: 31325176; PMCID: PMC6851972.

[0072]

[0051] Such sites are associated with facial coordinates, which are specific points on the face that are identified and mapped, usually in the context of digital image processing or facial recognition technology. These coordinates are used to measure and analyze facial features, providing a mathematical and geometric representation of the face. By identifying key points — such as the corners of the eyes, the tip of the nose, the edges of the mouth, and the outline of the jaw — software algorithms can create a detailed map of the face.

[0052] In the context of the present invention, it should be noted that a “digital identifier” refers to a bijective digital representation, in a computing system, of a materialized or materializable item.

[0073]

[0053] In the context of the present invention, it should be noted that “materializable” or “materialized” refers to the capacity of an item to exist in the physical world, either readily or through man-made production.

[0074]

[0054] In the context of the present invention, a “localized value” corresponds to a value associated with coordinates in a referential, such as the human face. Such values be associated to areas (i.e., implicit coordinate clouds which correspond to localizations on a human face) around facial features designated by said facial features, such as “mouth”, which references parts of the face which are closer to the mouth than to another feature, such as “right eye” for example.

[0075]

[0055] Figure 1 represents, schematically, a particular succession of steps of an embodiment of the method 100 object of the present invention. This method 100 for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, comprises the steps of:

[0076] - collecting 105 a microbiome sample on a facial site associated with facial coordinates,

[0077] - measuring 110 an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,

[0078] - calculating 115 at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and

[0079] - providing 120 at least one calculated value of facial microbiome taxa and the associated facial coordinates.

[0080]

[0056] The step of collecting 105 can be performed by using any known microbiome sampling device and associated operating method, such as D-Squame tape stripping. Such devices may correspond to swab-based devices which are commonly used for microbiome sampling, involving the use of swabs to collect microbial samples from various surfaces or body parts. These devices typically consist of a handle and a swab tip made of materials suitable for sampling microbiota.

[0081]

[0057] Such devices may correspond to sponge-based sampling devices which employ porous materials, such as sponges, to absorb microbial samples.

[0082]

[0058] Such devices may correspond to wearable sampling devices which are designed to collect microbiome samples directly from the human body in a non-invasive manner.

[0059] Such devices may correspond to disposable sampling devices which are single-use tools designed for one-time microbiome sampling applications, minimizing the risk of sample crosscontamination, and simplifying sample handling procedures.

[0083]

[0060] During this step of collecting 105, at least one facial site is sampled. Preferably, more than one facial site is sampled. There is no maximum to the number of facial sites that can be sampled.

[0084]

[0061] The more facial sites are sampled, the more accurate the calculation of interpolated values is.

[0062] In some preferred embodiments, twenty to thirty facial sites are sampled. When the subsequent calculation 115 is carried out suitably on the basis of a spline-based technique for data interpolation and smoothing (such as thin plate spline (TPS)) without help of any machine learing device, the number of facial sites being sampled is preferably equal to or more than 15, more preferably equal to or more than 20, most preferably equal to or more than 23. There is no upper limit, but for practical purposes the number of facial sites being sampled may be equal to or less than 100, suitably equal to or less than 50 or even equal to or less than 30.

[0085]

[0063] In other preferred embodiments the number of facial sites being sampled can advantageously be lowered by applying a combination of modelling by means of a machine learing device and a splinebased technique for data interpolation and smoothing (such as thin plate spline (TPS)). When the subsequent measuring 110 and / or calculation 115 are carried out suitably on the basis of a combination of modelling by means of a machine learing device and a spline-based technique for data interpolation and smoothing, the number of facial sites being sampled is preferably in the range from equal to or more than 2, more preferably from equal to or more than 5 and most preferably from equal to or more than 7 to equal to or less than 30, more preferably equal to or less than 25, even more preferably equal to or less than 20 and most preferably equal to or less than 15. As illustrated in the examples, when applying certain preferred calculation steps, for example combining calculations by a machine learning device and thin plate spline, the number of facial sites that need to be sampled can advantageously be reduced to a number of facial sites in the range from 7 to 15 and even to a range from 7 to 12.

[0086]

[0064] The sampling is preferably performed on the hemiface of a human person.

[0087]

[0065] The step of collecting 105 may be performed by a trained professional or by a regular user.

[0088]

[0066] The step of measuring 110 may be performed by any device suited for the detection and quantification of facial microbiome taxa. Such a device is preferably suited for the analysis of target taxa of interest.

[0089]

[0067] Such a device may correspond to, for example, a real-time polymerase chain reaction (realtime PCR) analysis of each collected sample.

[0090]

[0068] Alternatively droplet digital PCR (ddPCR) may be applied. “Droplet digital PCR” is herein preferably understood to refer to a PCR method for target quantification wherein each sample is portioned into thousands of droplets, with each droplet containing either 0 or 1 (or more) copies of the target sequence. In ddPCR, after partitioning in about 20,000 droplets, PCR is performed, and the droplets are read out.

[0091]

[0069] At the output of this step of measuring 110, quantified values of the presence of at least one facial microbiome taxon are obtained. These values are associated with coordinates of collection, which correspond either to a facial site digital identifier, represented in a computer memory by a label, such as “temple” for example, or to numerical coordinates in a three-dimensional referential.

[0092]

[0070] When multiple facial microbiome taxa are obtained, conveniently a dataset can be generated comprising the quantitative data on the presence of each of a multitude of (selected) micro-organisms in each of a multitude of facial sites, where each facial site may conveniently be associated with a specific facial coordinates.

[0093]

[0071] In particular embodiments, the step of measing 110 further comprises the steps of:

[0094] - measuring 110a an absolute quantity of a facial microbiome taxon for at least one microbiome sample, via a real-time polymerase chain reaction analysis of each said collected microbiome sample, to yield a first dataset comprising values of absolute quantities of facial microbiome taxon associated with a first set of facial sites,

[0095] - generating 110b, with the help of the first dataset, a predictive value for additional quantities of facial microbiome taxon for a second set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the second set of facial sites are different from the facial sites in the first set of facial sites,

[0096] - adding 110c the additional quantities of facial microbiome taxon for the second set of facial sites to the first dataset to yield a second dataset, and in which the step of calculating 115 is configured to calculate, as a function of the second dataset, at least one value representative of a quantity of facial microbiome taxa for a third set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the third set of facial sites are different from the facial sites in the first and second set of facial sites, and wherein each calculated value is associated with facial coordinates, wherein this calculation is configured to perform an interpolation of the values of the second dataset to obtain at least one said value representative of a quantity for the third set of facial coordinaites, and and in which the step of providing (120) is configured to provide said calculated value of facial microbiome taxa and the associated facial coordinates upon a three-dimensional representation of a face of a user.

[0097]

[0072] Advantageously, measuring step 110 can be made less resource-intense by making use of predictive models, for example with the help of a machine learning device. Accordingly, the present invention provides a method 100 as described herein, which comprises the steps of: - collecting 105 microbiome samples on a first set of facial sites, each facial site associated with its own facial coordinates,

[0098] - measuring 110a the value of an absolute quantity (also referred to as “measured absolute quantity”) of a facial microbiome taxon for each microbiome sample of the collected microbiome samples, for example via a real-time polymerase chain reaction analysis, to yield a first dataset comprising measured absolute quantities of each facial microbiome taxon for each facial site in the first set of facial sites,

[0099] - generating 110b, with the help of the first dataset, a predicted value for the absolute quantity (also referred to as “predicted absolute quantity”) of facial microbiome taxon in the microbiome of each facial site in a second set of facial sites, each facial site associated with its own facial coordinates, wherein the facial sites in the second set of facial sites are different from the facial sites in the first set of facial sites, said set of predicting using a trained machine learning device configured to predict absolute quantity of values facial microbiome taxon in the microbiome for a second set facial coordinates as a function of measured absolute quantity of values facial microbiome taxon in the microbiome for a first set of facial coordinates.

[0100] - adding 110c the predicted absolute quantitiy of each facial microbiome taxon for each facial site in the second set of facial sites to the first dataset to yield a second dataset,

[0101] - preferably calculating 115, with the help of the second dataset, multiple values representative of a quantity of facial microbiome taxa for a third set of facial sites, each associated with its own facial coordinates, wherein the facial sites in the third set of facial sites are different from the facial sites in the first and second set of facial sites, and wherein each calculated value is associated with its own facial coordinates, wherein this calculation comprises an interpolation of the values of the second dataset, yielding a multitude of calculated value of facial microbiome taxa and the associated facial coordinates

[0102] - preferably providing 120 the multitude of calculated value of facial microbiome taxa and the associated facial coordinates as a visual.

[0103] Preferably generating step 110b is carried out with the help of a machine learning device. Such a machine learning device may preferably be trained with part of the data in the first dataset (“training data”) and tested with another part of the data in the first dataset (“test data”). Preferably the data in the first dataset, such as the training data and test data, comprise or consist of data on the measured absolute quantity of at least one facial microbiome taxon associated with one facial site (with its own facial coordinates). Based on such exemplar data, the machine learning device may be trained in order to generate a set of additional data, where this additional data preferably also comprises or consists of data on the predicted absolute quantity of at least one facial microbiome taxon associated with one facial site (with its own facial coordinates). The method has been exemplified in the example. Preferred machine learning devices include devices as illustrated in the examples. Further preferences for the method are as described above and below.

[0104]

[0073] In particular embodiments, the steps of collecting 105 and measuring 110 are outside of the scope of the invention, and the method comprises instead a step of inputting at least one value representative of a measured facial microbiome taxa quantity, said quantity being associated with facial coordinates or a facial site digital identifier. Such an association may be performed via a step of inputting facial coordinates or selecting a facial site digital identifier, prior to or after the step of inputting at least one value representative of a measured facial microbiome taxa.

[0105]

[0074] Such steps of selecting or inputting may use means of inputting or an input device 340 such as shown in figure 3. Such an input device 340 may be associated with a graphic user interface (GUI) allowing for a user to input or select values.

[0106]

[0075] The step of calculating 115 is performed, for example, by a computer program executed by a computing device. This computer program is configured to, for example, execute a thin plate spline interpolation, which is commonly used in image processing, such as disclosed in Multiple Landmark Warping Using Thin-Plate Splines, by Mark Whitbeckf and Hongyu Guo. In the thin plate spline interpolation method the x,y facial coordinates are placed on a 2D representation of the face or any other body site in analogy to u,v coordinates on texture images typically used in computer graphics to provide colors to 3D objects. The corresponding measured absolute values are then placed in the third dimension e.g., represented by the z axis. Next, points with the same x,y coordinates but a constant z coordinate e.g., the mean of all measured values, are generated. The thin plate spline algorithm “bends” the resulting planar plate in order to cover all measured sites. The resulting deformed surface (“thin” plate) is described by coefficients, which allow the calculation of any point on the plate. As a result, a 2D mesh of z values can be calculated representing the interpolated surface and colored in a similar way as with usual height maps. Therefore, the values are typically clamped between 0 and 1 .

[0107]

[0076] In such embodiments, the step 115 of calculating comprises a step (155) of thin plate spline interpolation configured to associate interpolated absolute microbiome taxa quantities with facial coordinates. In other embodiments, the step 115 of calculating may comprise a step of cubic spline interpolation. In other embodiments, the step 115 of calculating may comprise a step of performing any spatial analysis interpolation such as inverse distance weighted interpolation, triangulated irregular network interpolation, regularized splines with tension interpolation, kriging or trend surface interpolation.

[0108]

[0077] The step of providing 120 may use an output device 335 such as shown in figure 3.

[0109]

[0078] During this step of providing 120, the calculated values may be provided upon a GUI, either in alphanumerical form or in a more complex form, such as by showing, in a two or three-dimensional space, a human face upon which the calculated values are overlayed in the appropriate facial sites or facial coordinates. Such values may be represented by a color gradient or intensity, in which a particular color or color intensity is representative of a higher quantity for a given microbiome taxon.

[0110]

[0079] During this step of providing 120, a filter may be presented to a user, allowing the selective visualization of the quantity of a given microbiome taxon of interest to the user.

[0111]

[0080] It should be noted that at least one value provided during the step of providing 120 may correspond to an absolute value, to a ratio of absolute values or to a diversity index, which corresponds to a statistical parameter representative of the variety of microbiome taxa present at a particular facial site.

[0112]

[0081] As it is understood, in particular embodiments, during the step 120 of providing, a digital representation of a human face is provided, said at least one calculated value being displayed as a layer of the representation of said human face.

[0113]

[0082] In more specific embodiments, the method 100 object of the present invention comprises a step 125 of acquiring at least one image of a human face and which, during the step 120 of providing, said at least one calculated value being displayed as a layer of said acquired image.

[0114]

[0083] Such a step 125 of acquiring may be performed via a digital camera. Once at least one image is acquired, the position of facial sites or facial coordinates are located via, for example, an image recognition algorithm configured to locate facial features of the human face. Based on such feature recognition, the human face in the capture image may be fit a to a generic and parametric face model for example. Alternatively, simple calculations may be performed to locate facial sites (such as “facial site X is located at 30% of the distance between facial feature Y and facial feature Z”).

[0115]

[0084] The steps 125 of acquiring and providing 120 may be continuously performed, so as to provide real-time layering of the calculated microbiome taxa quantities on the face of a user.

[0116]

[0085] In particular embodiments, the method 100 object of the present invention comprises a step 130 of measuring physical, chemical and / or biological properties of a facial site associated with facial coordinates, the step 115 of calculating being performed as a function of said measured physical, chemical and / or biological properties. Such properties may correspond to pH, hydration, and sebum concentration, which are known to significantly determine quantities of species that are dependent on acidity, humidity of the environment and availability of nutritional sources, such as fatty acids (derived from sebum).

[0117]

[0086] In such embodiments, a machine learning device may be trained via a supervised learning strategy on exemplar data comprising measured physical, chemical and / or biological property values and the associated measured absolute quantity of at least one facial microbiome taxon. Based on such exemplar data, the machine learning device may be trained in order to generate a set of hyperparameters which allow, once used on further measured physical, chemical and / or biological property values, the prediction of the absolute quantity of at least one facial microbiome taxon for a specific site or coordinates of the human face. In a preferred embodimet the measured properties may comprise or consist of a measurement of the absolute quantities of at least one facial microbiome taxon. Conveniently this allows for the facial microbiome of healthy subjects and subjects with a disturbed microbiome to be compared, allowing for a proper treatment of the subjects with a disturbed micro bio me.

[0118]

[0087] Such a step 130 of measuring may be performed by any device known for determined physical, chemical and / or biological properties.

[0119]

[0088] In particular embodiments, the method 100 object of the present invention further comprises: a step 135 of selecting a microbiome-targeting bioactive compound digital identifier, representative of a materializable microbiome-targeting bioactive compound, and a step 140 of computing an adjusted quantity of facial microbiome taxa, each calculated value being associated with facial coordinates, the step 120 of providing being configured to provide the adjusted quantity of facial microbiome taxa.

[0089] The step 135 of selecting may be performed, for example, by an input device 240 such as shown in figure 3, associated with a GUI allowing for a user to select a micro biome-targeting bioactive compound digital identifier in a list of microbiome-targeting bioactive compound digital identifiers.

[0120]

[0090] Each microbiome-targeting bioactive compound digital identifier can be associated with an impact parameter on at least one microbiome taxon. Such an impact parameter may correspond, for example, to a relative reduction or increase in the quantity of said microbiome taxon. Alternatively, such an impact parameter may correspond to an increase or decrease of physical, chemical and / or biological properties of a facial site, which result in reduction or increase in the quantity of said microbiome taxon. Such an impact parameter may also be associated with a facial site digital identifier, allowing for differentiated local impacts for a microbiome-targeting bioactive compound.

[0121]

[0091] The step of computing 140 is performed, for example, by a computer program executed by a computing device. This computer program is configured to model the influence of effects of a bioactive on the microbiome taxon, such as support of growth of a particular species which usually reduces growth of another competing species (since the space and nutritional availability is the limiting factor), leading ultimately to a different ratio between these two species.

[0122]

[0092] Such a computer program may use a matrix of microbiome taxon interaction, in which parameters values reflecting the real-life interaction between two or more species in a finite space as the quantity of one of said species increases. Such interactions are disclosed, for example, in Kodera SM, Das P, Gilbert JA, Lutz HL. Conceptual strategies for characterizing interactions in microbial communities. iScience. 2022 Jan 15;25(2): 103775.

[0123]

[0093] Such a computer program may also use a matrix linking microbiome-targeting bioactive compound digital identifier to at least one microbiome taxon digital identifier and associating a quantity variation for each said microbiome taxon digital identifier. Such a quantity variation may correspond to a percentage of reduction or increase in absolute quantity or an absolute reduction or increase amount in the absolute quantity of a microbiome taxon.

[0124]

[0094] Based on this information, the computer program may, upon selection of at least one microbiome-targeting bioactive compound digital identifier, compute the modified absolute quantity for each microbiome taxon digital identifier associated with said microbiome-targeting bioactive compound digital identifier.

[0125]

[0095] In particular embodiments, the method 100 object of the present invention further comprises a step 145 of sending a digital command representative of an instruction of materialising at least one microbiome-targeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.

[0126]

[0096] Such a step 145 of sending may be performed by a user, via a GUI, or by a computer program, via an API. Such an instruction may be addressed to a microbiome-targeting bioactive compound materialization site, such as a laboratory or factory for example.

[0127]

[0097] In particular embodiments, the method 100 object of the present invention further comprises a step 150 of materialising at least one microbiome-targeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.

[0128]

[0098] The nature of such a step 150 of materializing depends on the nature of the fragrance ingredients in the composition and are known to persons skilled in the art of microbiome-targeting bioactive compound materialization.

[0129]

[0099] As it can be understood, such an invention also aims at a computer program product, characterized in that it comprises instructions which upon execution by a computer cause the computer to execute a method object of the present invention.

[0130]

[0100] As it can be understood, such an invention also aims at a computer-readable storage medium storing programming instructions which upon execution by a computer cause the computer to execute a method object of the present invention.

[0131]

[0101] Figure 2 represents, schematically, a particular embodiment of the system 200 object of the present invention. This system 200 for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, characterized in that it comprises means of:

[0132] - collecting 205 a microbiome sample on a facial site associated with facial coordinates,

[0133] - measuring 210 an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,

[0134] - calculating 215 at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and

[0135] - providing 220 at least one calculated value of facial microbiome taxa and the associated facial coordinates.

[0136]

[0102] Particular examples of means of collecting 205, measuring 210, calculating 215 and providing 220 are disclosed in relation to figure 1 .

[0137]

[0103] Figure 3 further represents a block diagram that illustrates an example computer system 300 with which an embodiment of the present invention may be implemented. In the example of figure 8, a computer system 305 and instructions for implementing the disclosed technologies in hardware, software, or a combination of hardware and software, are represented schematically, for example as boxes and circles, at the same level of detail that is commonly used by persons of ordinary skill in the art to which this disclosure pertains for communicating about computer architecture and computer systems implementations.

[0138]

[0104] The computer system 305 includes an input / output (IO) subsystem 320 which may include a bus and / or other communication mechanism(s) for communicating information and / or instructions between the components of the computer system 305 over electronic signal paths. The I / O subsystem 320 may include an I / O controller, a memory controller and at least one I / O port. The electronic signal paths are represented schematically in the drawings, for example as lines, unidirectional arrows, or bidirectional arrows.

[0139]

[0105] At least one hardware processor 310 is coupled to the I / O subsystem 320 for processing information and instructions. Hardware processor 310 may include, for example, a general-purpose microprocessor or microcontroller and / or a special-purpose microprocessor such as an embedded system or a graphics processing unit (GPU) or a digital signal processor or ARM processor. Processor 310 may comprise an integrated arithmetic logic unit (ALU) or may be coupled to a separate ALU.

[0140]

[0106] Computer system 305 includes one or more units of memory 325, such as a main memory, which is coupled to I / O subsystem 320 for electronically digitally storing data and instructions to be executed by processor 310. Memory 325 may include volatile memory such as various forms of random-access memory (RAM) or other dynamic storage device. Memory 325 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 310. Such instructions, when stored in non-transitory computer-readable storage media accessible to processor 310, can render computer system 305 into a special-purpose machine that is customized to perform the operations specified in the instructions.

[0141]

[0107] Computer system 305 further includes non-volatile memory such as read only memory (ROM) 330 or other static storage device coupled to the I / O subsystem 320 for storing information and instructions for processor 310. The ROM 330 may include various forms of programmable ROM (PROM) such as erasable PROM (EPROM) or electrically erasable PROM (EEPROM). A unit of persistent storage 315 may include various forms of non-volatile RAM (NVRAM), such as FLASH memory, or solid-state storage, magnetic disk, or optical disk such as CD-ROM or DVD-ROM and may be coupled to I / O subsystem 320 for storing information and instructions. Storage 315 is an example of a non-transitory computer-readable medium that may be used to store instructions and data which when executed by the processor 310 cause performing computer-implemented methods to execute the techniques herein.

[0142]

[0108] The instructions in memory 325, ROM 330 or storage 315 may comprise one or more sets of instructions that are organized as modules, methods, objects, functions, routines, or calls. The instructions may be organized as one or more computer programs, operating system services, or application programs including mobile apps. The instructions may comprise an operating system and / or system software; one or more libraries to support multimedia, programming or other functions; data protocol instructions or stacks to implement TCP / IP, HTTP or other communication protocols; file format processing instructions to parse or render files coded using HTML, XML, JPEG, MPEG or PNG; user interface instructions to render or interpret commands for a graphical user interface (GUI), command-line interface or text user interface; application software such as an office suite, Internet access applications, design and manufacturing applications, graphics applications, audio applications, software engineering applications, educational applications, games or miscellaneous applications. The instructions may implement a web server, web application server or web client. The instructions may be organized as a presentation layer, application layer and data storage layer such as a relational database system using structured query language (SQL) or no SQL, an object store, a graph database, a flat file system or other data storage.

[0143]

[0109] Computer system 305 may be coupled via I / O subsystem 320 to at least one output device 335. In one embodiment, output device 335 is a digital computer display or Human Machine Interface. Examples of a display that may be used in various embodiments include a touchscreen display or a light-emitting diode (LED) display or a liquid crystal display (LCD) or an e-paper display. Computer system 305 may include other type(s) of output devices 335, alternatively or in addition to a display device. Examples of other output devices 335 include printers, ticket printers, plotters, projectors, sound cards or video cards, speakers, buzzers or piezoelectric devices or other audible devices, lamps or LED or LCD indicators, haptic devices, actuators, or servos.

[0144]

[0110] At least one input device 340 is coupled to I / O subsystem 320 for communicating signals, data, command selections or gestures to processor 310. Examples of input devices 340 include touchscreens, microphones, still and video digital cameras, alphanumeric and other keys, keypads, keyboards, graphics tablets, image scanners, joysticks, clocks, switches, buttons, dials, slides.

[0111] Another type of input device is a control device 345, which may perform cursor control or other automated control functions such as navigation in a graphical interface on a display screen, alternatively or in addition to input functions. Control device 345 may be a touchpad, a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 310 and for controlling cursor movement on display 335. The input device may have at least two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. Anothertype of input device is a wired, wireless, or optical control device such as a joystick, wand, console, steering wheel, pedal, gearshift mechanism or other type of control device. An input device 340 may include a combination of multiple different input devices, such as a video camera and a depth sensor.

[0145]

[0112] In another embodiment, computer system 305 may comprise an Internet of things (loT) device in which one or more of the output device 335, input device 340, and control device 345 are omitted. Or, in such an embodiment, the input device 340 may comprise one or more cameras, motion detectors, thermometers, microphones, seismic detectors, other sensors or detectors, measurement devices or encoders and the output device 335 may comprise a special-purpose display such as a single-line LED or LCD display, one or more indicators, a display panel, a meter, a valve, a solenoid, an actuator or a servo.

[0146]

[0113] Computer system 305 may implement the techniques described herein using customized hardwired logic, at least one ASIC or FPGA, firmware and / or program instructions or logic which when loaded and used or executed in combination with the computer system causes or programs the computer system to operate as a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 305 in response to processor 310 executing at least one sequence of at least one instruction contained in main memory 325. Such instructions may be read into main memory 325 from another storage medium, such as storage 315. Execution of the sequences of instructions contained in main memory 325 causes processor 310 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0147]

[0114] The term “storage media” as used herein refers to any non-transitory media that store data and / or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and / or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage 315. Volatile media includes dynamic memory, such as memory 325. Common forms of storage media include, for example, a hard disk, solid state drive, flash drive, magnetic data storage medium, any optical or physical data storage medium, memory chip, or the like.

[0148]

[0115] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus of I / O subsystem 320. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0149]

[0116] Various forms of media may be involved in carrying at least one sequence of at least one instruction to processor 310 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a communication link such as a fiber optic or coaxial cable or telephone line using a modem. A modem or router local to computer system 305 can receive the data on the communication link and convert the data to a format that can be read by computer system 305. For instance, a receiver such as a radio frequency antenna or an infrared detector can receive the data carried in a wireless or optical signal and appropriate circuitry can provide the data to I / O subsystem 320 such as place the data on a bus. I / O subsystem 320 carries the data to memory 325, from which processor 310 retrieves and executes the instructions. The instructions received by memory 325 may optionally be stored on storage 315 either before or after execution by processor 310.

[0150]

[0117] Computer system 305 also includes a communication interface 360 coupled to bus 320. Communication interface 360 provides a two-way data communication coupling to network link(s) 365 that are directly or indirectly connected to at least one communication network, such as a network 370 or a public or private cloud on the Internet. For example, communication interface 360 may be an Ethernet networking interface, integrated-services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of communications line, for example an Ethernet cable or a metal cable of any kind or a fiber-optic line or a telephone line. Network 370 broadly represents a local area network (LAN), wide-area network (WAN), campus network, internetwork, or any combination thereof. Communication interface 360 may comprise a LAN card to provide a data communication connection to a compatible LAN, or a cellular radiotelephone interface that is wired to send or receive cellular data according to cellular radiotelephone wireless networking standards, or a satellite radio interface that is wired to send or receive digital data according to satellite wireless networking standards. In any such implementation, communication interface 360 sends and receives electrical, electromagnetic, or optical signals over signal paths that carry digital data streams representing various types of information.

[0151]

[0118] Network link 365 typically provides electrical, electromagnetic, or optical data communication directly or through at least one network to other data devices, using, for example, satellite, cellular, WiFi, or BLUETOOTH technology. For example, network link 365 may provide a connection through a network 370 to a host computer 350.

[0119] Furthermore, network link 365 may provide a connection through network 370 or to other computing devices via internetworking devices and / or computers that are operated by an Internet Service Provider (ISP) 375. ISP 375 provides data communication services through a world-wide packet data communication network represented as Internet 380. A server computer 355 may be coupled to Internet 380. Server 355 broadly represents any computer, data center, virtual machine, or virtual computing instance with or without a hypervisor, or computer executing a containerized program system such as DOCKER or KUBERNETES. Server 355 may represent an electronic digital service that is implemented using more than one computer or instance and that is accessed and used by transmitting web services requests, uniform resource locator (URL) strings with parameters in HTTP payloads, API calls, app services calls, or other service calls. Computer system 305 and server 355 may form elements of a distributed computing system that includes other computers, a processing cluster, server farm or other organization of computers that cooperate to perform tasks or execute applications or services. Server 355 may comprise one or more sets of instructions that are organized as modules, methods, objects, functions, routines, or calls. The instructions may be organized as one or more computer programs, operating system services, or application programs including mobile apps. The instructions may comprise an operating system and / or system software; one or more libraries to support multimedia, programming or other functions; data protocol instructions or stacks to implement TCP / IP, HTTP or other communication protocols; file format processing instructions to parse or render files coded using HTML, XML, JPEG, MPEG or PNG; user interface instructions to render or interpret commands for a graphical user interface (GUI), command-line interface or text user interface; application software such as an office suite, Internet access applications, design and manufacturing applications, graphics applications, audio applications, software engineering applications, educational applications, games or miscellaneous applications. Server 355 may comprise a web application server that hosts a presentation layer, application layer and data storage layer such as a relational database system using structured query language (SQL) or no SQL, an object store, a graph database, a flat file system or other data storage.

[0152]

[0120] Computer system 305 can send messages and receive data and instructions, including program code, through the networks), network link 365 and communication interface 360. In the Internet example, a server 355 might transmit a requested code for an application program through Internet 380, ISP 375, local network 370 and communication interface 360. The received code may be executed by processor 310 as it is received, and / or stored in storage 315, or other non-volatile storage for later execution.

[0153]

[0121] The execution of instructions as described in this section may implement a process in the form of an instance of a computer program that is being executed and consisting of program code and its current activity. Depending on the operating system (OS), a process may be made up of multiple threads of execution that execute instructions concurrently. In this context, a computer program is a passive collection of instructions, while a process may be the actual execution of those instructions. Several processes may be associated with the same program; for example, opening up several instances of the same program often means more than one process is being executed. Multitasking may be implemented to allow multiple processes to share processor 310. While each processor 310 or core of the processor executes a single task at a time, computer system 305 may be programmed to implement multitasking to allow each processor to switch between tasks that are being executed without having to wait for each task to finish. In an embodiment, switches may be performed when tasks perform input / output operations, when a task indicates that it can be switched, or on hardware interrupts. Time-sharing may be implemented to allow fast response for interactive user applications by rapidly performing context switches to provide the appearance of concurrent execution of multiple processes simultaneously. In an embodiment, for security and reliability, an operating system may prevent direct communication between independent processes, providing strictly mediated and controlled inter-process communication functionality.

[0154] Example 1

[0155]

[0122] For this example a machine learning device and workflow were developed to predict microbial distribution using a dataset from five Caucasian female subjects. The device and workflow employed absolute quantities of microbial taxonomic units, alongside advanced selection techniques to address sparsity.

[0156]

[0123] For each female subject, 23 facial sites were sampled. The microorganisms in each of these samples were determined by means of real-time quantitative Polymerase Chain Reaction (qPCR). Filtering was applied as not each sample had enough read counts for some micro-organism species. A total of 105 samples illustrating a total of 15 different micro-organisms species remained after filtering. Then a CLR (Centered Log Ratio) transformation was applied to the data as explained in detail below.

[0157]

[0124] With the machine learning devices that were developed, it was possible to predict the microbiome of additional facial skin locations on the basis of a limited number of experimentally measured facial skin locations. These predictions were visualized as high-resolution microbiome maps. Results demonstrated the machine learning device’s ability to capture microbial variability across facial regions, highlighting key facial locations and adaptation to distinct niches.

[0158] Method

[0159]

[0125] Modelling and statistical evaluation was performed using Jupyter notebook and python 3.10. Open source mathematical and statistical packages were used, such as numpy, scipy, pandas, statsmodels, scikit-learn.

[0126] For the data-processing, a CLR transformation was performed. In this CLR transformation, read counts from the qPCR were converted to pseudocounts by adding a single read to all microbes. The geometric mean (gm) of the distribution was calculated as: and the log ratio (c) was computed as follows: wherein ai is the pseudocount for a given species i and n is the total number of features.

[0160] First a spreadsheet with the values of each of the 15 microorganism species for each of the 105 samples was created. Subsequently a machine learning device selection was carried out, as described below.

[0161] Machine Learning Device selection

[0162]

[0127] To eliminate bias from random picking, a random number generation was applied. To eliminate subject bias, LOGO (Leave One Group Out, i.e. in this case a “test” subject) was applied. In addition control for cross validation type (LOGO vs stratified random selection which includes data leakage from the subject) was applied. From the data a training set and a test set were generated. Different models were explored, all applying LOGO for the cross validation. That is, for each model testing, a “training dataset” was prepared in which randomly the data of one subject was excluded. The excluded data (i.e. of the excluded subject) was subsequently used as “test dataset”. The prediction of a model trained with a specific training dataset was subsequently compared to the actual values in the corresponding test dataset to provide a test-score, reflecting the accuracy of the prediction. For this test-score a value of 1 would represent a completely accurate prediction and a value of 0 would represent a completely inaccurate prediction. In addition a cross-validation score was determined to check against overfitting of the dataset.

[0163]

[0128] The training and testing was repeated about 30 times (cycles) for each model, each time randomly excluding the data of one subject. For each cycle a cross-validation score (cv-score) and a test-score were determined. Subsequently the average of the cv-scores and the average of the testscores were determined. The average results for some of the models are listed in below Table 1 . As illustrated by these results, the models could provide a good prediction of the microbiome of the skin of the test subject. Models applying Principal Component Analysis (PCA) provided the best results. Table 1

[0164]

[0129] With the support of thin plate spline interpolation subsequently an interactive picture of the human face was generated.

[0165]

[0130] The above advantagouesly allows one to close the critical gap in understanding the distribution of microbial communities across facial regions and in developing predictive frameworks that require minimal sampling.

[0166] Example 2

[0167]

[0131] In this example, visuals of a simple thin plate spline interpolation based on real-time qPCR- generated microbiome taxa data of 23 sampling sites (“experimental”) were compared with visuals of a thin plate spline interpolation generated with partly real-time qPCR-generated microbiome taxa data and partly machine learning predicted microbiome taxa data (“model 7 sites”).

[0168]

[0132] The data generated by a machine learning model approach was generated in a similar manner as provided in example 1 . In this machine learning model approach, the model was based on a training dataset with merely the data of 7 sampling sites and prediction values for an additional 14 locations of the face, giving a total of 21 facial locations. Subsequently a thin plate spline interpolation was applied to the combined 21 facial locations to generate a visual.

[0169]

[0133] As can be seen in figure 5 the visuals provided by the machine learning device using only a limited number of 7 sampling sites (“model 7 sites”) was comparable to the visuals based on the 23 sampling sites (“experimental”).

Claims

Claims1 . Method (100) for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, characterized in that it comprises the steps of:- collecting (105) a microbiome sample on a facial site associated with facial coordinates,- measuring (110) an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,- calculating (115) at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and- providing (120) at least one calculated value of facial microbiome taxa and the associated facial coordinates.

2. Method (100) according to claim 1 , in which, during the step (120) of providing, a digital representation of a human face is provided, said at least one calculated value being displayed as a layer of the representation of said human face.

3. Method (100) according to claim 2, which comprises a step (125) of acquiring at least one image of a human face and which, during the step of providing, said at least one calculated value being displayed as a layer of said acquired image.

4. Method (100) according to any one of claims 1 to 3, in which a facial microbiome taxon corresponds to either a bacterial genus, species or strain or a fungal genus, species or strain.

5. Method (100) according to any one of claims 1 to 4, which comprises a step (130) of measuring physical, chemical and / or biological properties of a facial site associated with facial coordinates, the step (115) of calculating being performed as a function of said measured physical, chemical and / or biological properties.

6. Method (100) according to any one of claims 1 to 5 in which the step of measuring (110) comprises the steps of:- measuring (110a) an absolute quantity of a facial microbiome taxon for at least one microbiome sample, via a real-time polymerase chain reaction analysis of each said collected microbiome sample, to yield a first dataset comprising values of absolute quantities of facial microbiome taxon associated with a first set of facial sites,- generating (110b), with the help of the first dataset, a predictive value for additional quantities of facial microbiome taxon for a second set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the second set of facial sites are different from the facial sites in the first set of facial sites,- adding (110c) the additional quantities of facial microbiome taxon for the second set of facial sites to the first dataset to yield a second dataset, and in which the step of calculating (115) is configured to calculate, as a function of the second dataset, at least one value representative of a quantity of facial microbiome taxa for a third set of facial sites, each facial site being associated with dedicated facial coordinates, wherein the facial sites in the third set of facial sites are different from the facial sites in the first and second set of facial sites, and wherein each calculated value is associated with facial coordinates, wherein this calculation is configured to perform an interpolation of the values of the second dataset to obtain at least one said value representative of a quantity for the third set of facial coordinaites, and and in which the step of providing (120) is configured to provide said calculated value of facial microbiome taxa and the associated facial coordinates upon a three-dimensional representation of a face of a user.

7. Method (100) according to any one of claims 1 to 6, in which the step (115) of calculating comprises a step (155) of thin plate spline interpolation configured to associate interpolated absolute microbiome taxa quantities with facial coordinates.

8. Method (100) according to any one of claims 1 to 7, which comprises:- a step (135) of selecting a microbiome-targeting bioactive compound digital identifier, representative of a materializable microbiome-targeting bioactive compound, and- a step (140) of computing an adjusted quantity of facial microbiome taxa, each calculated value being associated with facial coordinates, the step (120) of providing being configured to provide the adjusted quantity of facial microbiome taxa.

9. Method (100) according to claim 8, which further comprises a step (145) of sending a digital command representative of an instruction of materialising at least one microbiome-targeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.

10. Method (100) according to claim 9, which further comprises a step (150) of materialising at least one microbiome-targeting bioactive compound corresponding to the selected microbiome-targeting bioactive compound digital identifier.11 . Computer program product characterized in that it comprises instructions which upon execution by a computer cause the computer to execute the method according to any one of claims 1 to 10.

12. Computer-readable storage medium storing programming instructions which upon execution by a computer cause the computer to execute the method according to any one of claims 1 to 10.

13. System (200) for providing a plurality of localized values representative of quantities of at least one facial microbiome taxa, said values being associated with facial coordinates, characterized in that it comprises means of:- collecting (205) a microbiome sample on a facial site associated with facial coordinates),- measuring (210) an absolute quantity of a facial microbiome taxon in said microbiome sample, via a real-time polymerase chain reaction analysis of the collected microbiome sample,- calculating (215) at least one value representative of a quantity of facial microbiome taxa, each calculated value being associated with facial coordinates as a function of the measured absolute quantity of facial microbiome taxon, the coordinates associated with said calculated at least one value being different from the coordinates associated with the collected microbiome sample, and- providing (220) at least one calculated value of facial microbiome taxa and the associated facial coordinates.

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