Computer method and system for predicting the stability of a sample

Abstract

The present invention relates to a method (100) for predicting the stability of at least one sample, which method comprises the following steps: a. choosing (110) at least one type of formulae; b. providing (120) at least one list of ingredients, preferably via the input interface (210); c. selecting (130) a first artificial intelligence model; d. proposing (140) at least one operating mode; e. at each control point: i. capturing (150) at least one image; ii. analysing (160) the captured image; iii. classifying (170) the analysed image, comprising at least one step of assigning (180) at least one first stability score; f. predicting (190) the stability of the sample.

Description

[0001] Description

[0002] Title of the invention: Method and computer system for predicting the stability of a sample.

[0003] [1]TECHNICAL FIELD

[0004] [2]The present invention relates to the field of chemical formulation assisted by artificial intelligence. It finds particularly advantageous application in the field of predicting the stability of a chemical sample.

[0005] [3] STATE OF THE ART

[0006] [4]When a problem arises during the formulation of a product, such as crystals, precipitates, solubility problem, finding the underlying cause can be a tedious task.

[0007] [5]It is often necessary to involve other R&D staff, which can significantly extend development time.

[0008] [6]Alternatively, it may be necessary to carry out many other tests, often to change only between 1 and 3 ingredients in formulas which generally have between ten and more than thirty ingredients. This is not cost-effective in terms of time or raw materials.

[0009] [7]An object of the present invention is therefore to overcome at least in part these various technical problems.

[0010] [8]Other objects, features, and advantages of the present invention will become apparent from the following description and accompanying drawings. It is understood that other advantages may be incorporated.

[0011] [9]SUMMARY

[0012]

[0010] The present invention relates to a method for predicting the stability of at least one sample, preferably comprising at least one chemical composition, preferably during the different stages of its development, said method being configured to be implemented by at least one computer prediction system, said method comprising at least the following steps: a. Choice, by at least one user, preferably via an input interface, of at least one type of formula, said type of formula being taken from at least one list of predetermined types of formula, preferably said list of predetermined types of formula may comprise at least the following types of formula: i. creams, emulsions, lotions, gels and oils for the skin; ii. beauty masks; iii. foundations (liquids, pastes, powders); iv. powders for makeup, powders to be applied after bathing, powders for body hygiene; v. sun products; vi.sunless tanning products; vii. skin whitening products; viii. anti-wrinkle products. ix. shaving products (soaps, foams, lotions); x. make-up and make-up removal products; xi. products intended to be applied to the lips; xii. bath and shower preparations (salts, foams, oils, gels); xiii. toilet soaps, deodorant soaps; xiv. dental and oral hygiene products; xv. external intimate hygiene products; xvi. deodorants and antiperspirants; xvii. hair dyes; xviii. hair waving, straightening and fixing products; xix. hair styling products; xx. hair cleaning products (lotions, powders, shampoos); xxi. hair care products (lotions, creams, oils); xxii. hair styling products (lotions, hairsprays, brilliantines); xxiii. perfumes, toilet waters and colognes; xxiv. depilatories; xxv. nail care and make-up products; b.Provision, by said user, of at least one list of ingredients, preferably via said input interface; c. Automatic selection, preferably by at least one selection module, of a first artificial intelligence model AI1, preferably using a first convolutional neural network RNC1, from a first set of artificial intelligence models, said first artificial intelligence model AI1 being configured to correspond to said chosen formula; d. Automatic proposal, preferably by at least one supervision module, and advantageously using said first artificial intelligence model AI1, of at least one operating mode adapted to said chosen formula and / or said provided ingredients, said operating mode comprising at least one series of control points, said control points being configured to trigger at least one image capture of at least part of said sample being prepared; e.At each control point: i. Capture of at least one image, preferably by microscopy, of at least part of a sample, preferably being developed, by at least one image capture module; ii. Analysis of said captured image, preferably in real time, by an analysis module, via a second AI2 artificial intelligence model, preferably using a second RNC2 convolutional neural network, so as to provide a first quality score; iii. Classification, by at least one classification module, of said analyzed image via a third AI3 artificial intelligence model, preferably using a third RNC3 convolutional neural network, said third AI3 artificial intelligence having been trained from at least one image database, said classification step comprising at least: • A step of assigning at least one first stability score to said sample so that:.

[0013] ° If said first stability score is greater than or equal to a predetermined threshold, said sample is classified as stable;

[0014] ° If said first stability score is lower than a predetermined threshold, said sample is classified as unstable; f. Prediction of the stability of the sample, by at least one prediction module, by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate a preliminary overall stability score.

[0015]

[0011] The present invention thus allows monitoring and control of the quality of a sample during preparation. Indeed, by predicting the stability of a sample, the present invention makes it possible to anticipate the success or failure of the preparation. The present invention also makes it possible to improve stability during manufacturing in order to reduce the risk of failure.

[0016]

[0012] According to another aspect, the present invention also relates to a computer program product, preferably recorded on a non-transitory memory medium, comprising instructions, which when carried out by at least one of a processor, a computer, executes the method according to the present invention.

[0017]

[0013] According to another aspect, the present invention also relates to a non-transitory memory medium comprising at least one computer program product according to the present invention.

[0018]

[0014] According to yet another aspect, the present invention also relates to a prediction computer system configured to execute the method according to any one of the preceding claims, said prediction computer system comprising at least: a. An input interface configured to allow at least one user to: i. Choose at least one type of formulas; ii. Provide at least one list of ingredients; b. A selection module configured to automatically select at least one first AI1 artificial intelligence model from a first set of artificial intelligence models, said first AI1 artificial intelligence model being configured to correspond to said chosen formula; c.A supervision module configured to automatically propose at least one operating mode adapted to said chosen formula and / or said provided ingredients, preferably using said first artificial intelligence model AI1, said operating mode comprising at least one series of control points, said control points being configured to trigger at least one image capture; d. An image capture module configured to capture at least one image of at least part of a sample, preferably by microscopy; e. An analysis module configured to analyze at least one captured image, preferably in real time, via a second artificial intelligence model AI2 and to associate with at least one captured image a first quality score; f. A classification module configured to i.classifying at least one analyzed image via a third AI3 artificial intelligence model, said third AI3 artificial intelligence having been trained from at least one image database, said classification step comprising at least: ii. Assigning at least a first stability score to said sample so that:.

[0019] • If said first stability score is greater than or equal to a predetermined threshold, said sample is classified as stable;

[0020] • If said first stability score is lower than a predetermined threshold, said sample is classified as unstable; g. A prediction module configured to predict the stability of the sample by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate a preliminary overall stability score

[0021]

[0015] The present invention thus allows the prediction of the stability of a sample during its manufacture, making it possible to intervene and therefore reduce the risks of failure.

[0022]

[0016] BRIEF DESCRIPTION OF THE FIGURES

[0023]

[0017] The aims, objects, as well as the characteristics and advantages of the invention will emerge more clearly from the detailed description of an embodiment thereof which is illustrated by the following accompanying drawings in which:

[0024]

[0018] [Fig.1] Figure 1 represents certain steps of a prediction method according to an embodiment of the present invention.

[0025]

[0019] [Fig.2] Figure 2 represents a prediction system according to an embodiment of the present invention.

[0026]

[0020] [Fig.3] Figure 3 shows certain steps in manufacturing a sample according to an embodiment of the present invention.

[0027]

[0021] [Fig.4] Figure 4 represents certain steps of a test phase according to an embodiment of the present invention.

[0028]

[0022] [Fig.5] Figure 5 represents various steps of capturing and then analyzing images of at least part of a sample according to an embodiment of the present invention.

[0029]

[0023] The drawings are given as examples and are not limiting of the invention. They constitute schematic representations of principle intended to facilitate the understanding of the invention and are not necessarily on the scale of practical applications. In particular, the dimensions are not representative of reality.

[0030]

[0024] DETAILED DESCRIPTION

[0031]

[0025] Before beginning a detailed review of embodiments of the invention, optional features which may possibly be used in combination or alternatively are set out below:

[0032]

[0026] According to an example, the present invention comprises, after the step of predicting the stability of the sample, a stability testing phase comprising at least the following steps: a. Storing said sample according to a first set of storage conditions, preferably using a storage device; b. Observing, by at least one observation module, preferably over a predefined period of time, said sample so as to detect any modification of said sample, this observation step comprising at least: i. Capturing, by at least said image capture module, at least one image, preferably by microscopy, of at least a portion of a sample; ii. Analyzing said captured image, preferably in real time, via the second artificial intelligence model AI2, preferably using the second convolutional neural network RNC2, so as to provide a second quality score; iii.Classification, by at least said classification module, of said image analyzed via the third artificial intelligence model I A3, preferably using the third convolutional neural network RNC3, said third artificial intelligence IA3 having been trained from at least one image database, said classification step comprising at least:.

[0033] • A step of assigning at least a second stability score to said sample so that:

[0034] ° If said second stability score is greater than or equal to a predetermined threshold, said sample is classified as stable;

[0035] ° If said second stability score is lower than a predetermined threshold, said sample is classified as unstable; c. Prediction, by at least the prediction module, of the stability of the sample by mathematically combining the second stability scores of each observation with the second quality scores of each observation so as to calculate an overall stability score.

[0036]

[0027] According to one example, the list of predetermined formula types includes at least: a. creams, emulsions, lotions, gels and oils for the skin; b. beauty masks; c. foundations (liquids, pastes, powders); d. powders for make-up, powders for applying after bathing, powders for personal hygiene; e. sunscreens; f. sunless tanning products; g. skin whitening products; h. anti-wrinkle products. i. shaving products (soaps, foams, lotions); j. make-up and make-up removal products; k. products intended to be applied to the lips; l. bath and shower preparations (salts, foams, oils, gels); m. toilet soaps, deodorant soaps; n. dental and oral hygiene products; o. external intimate hygiene products; p. deodorants and antiperspirants; q. hair dyes; r. hair waving, straightening and fixing products; s. hair styling products; t.hair cleaning products (lotions, powders, shampoos); u. hair care products (lotions, creams, oils); v. hair styling products (lotions, hairsprays, brilliantines); w. perfumes, eaux de toilette and colognes; x. depilatories; y. nail care and make-up products.

[0037]

[0028] According to one example, the first quality score is a function of the visible state of the sample.

[0038]

[0029] According to one example, the second quality score is a function of the visible state of the sample.

[0039]

[0030] According to one example, the first quality score is determined using at least a first database comprising a first plurality of sample images and a first plurality of quality scores, each image of said first plurality of images being associated with a quality score of said first plurality of quality scores.

[0040]

[0031] According to one example, the second quality score is determined using at least a second database comprising a second plurality of sample images and a second plurality of quality scores, each image of said second plurality of images being associated with a quality score of said second plurality of quality scores.

[0041]

[0032] According to one example, determining the first quality score comprises selecting a sample image from said first plurality of sample images and its associated quality score, then modifying said associated quality score based on a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets, etc.

[0042]

[0033] According to one example, determining the second quality score comprises selecting a sample image from said second plurality of sample images and its associated quality score, then modifying said associated quality score based on a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets, etc.

[0043]

[0034] According to one example, the first quality score and the second quality score are a function of the visible state of the sample.

[0044]

[0035] According to one example, the first quality score and the second quality score are determined using at least one database comprising a plurality of sample images and a plurality of quality scores, each image of said plurality of images being associated with a quality score of said plurality of quality scores.

[0045]

[0036] According to one example, determining the first quality score and the second quality score comprises selecting a sample image from said plurality of sample images and its associated quality score, then modifying said associated quality score based on a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets, etc.

[0046]

[0037] According to one example, the step of providing at least one list of ingredients comprises at least one step of comparing at least one ingredient of said list of ingredients with at least one database.

[0047]

[0038] According to one example, the step of providing at least one list of ingredients comprises at least one step of providing said user with at least one notification of incompatibility between at least one ingredient of said list of ingredients with at least one other ingredient of said list of ingredients.

[0048]

[0039] According to one example, the step of providing at least one list of ingredients comprises at least one step of automatic suggestion by at least one fourth artificial intelligence model, preferably using a fourth convolutional neural network RNC4, of at least one ingredient absent from said list of ingredients and / or of at least one substitute ingredient for at least one ingredient from the list of ingredients.

[0049]

[0040] According to an example, the present invention comprises: a. after the step of proposing an operating mode, a step of modifying said operating protocol by said user so as to generate a new operating protocol; b. after the step of modifying said operating protocol, a step of updating said control points according to said new operating protocol.

[0050]

[0041] According to an example, the present invention comprises, if the sample obtains a stability score lower than said predetermined threshold, at least one step of proposing at least one measure for restoring the stability of said sample, said measure being taken from at least: a. Addition of a new ingredient; b. Substitution of an ingredient; c. Change or adaptation of the manufacturing protocol; d. Modification of the temperature; e. Modification of the order of introduction of the ingredients; f. ..etc...

[0042] According to an example, the present invention comprises at least one segmentation step before the classification step.

[0051]

[0043] According to one example, the present invention comprises, at each control point and after the step of analyzing said captured image, at least one step of identifying and characterizing abnormal elements (crystallization, precipitation, aggregation, poor dispersion, etc.).

[0052]

[0044] According to one example, the step of analyzing said captured image comprises a step of providing associated formulation advice relating to the identified abnormal elements.

[0053]

[0045] According to one example, the present invention comprises at least one real-time analysis step, preferably in a video mode (non-frozen image), comprising: a. a step of segmenting said image; b. a step of classifying said image; c. a step of highlighting a region to attract the user's attention; d. a step of assigning a label to said region, such as for example: crystal, precipitate, object, etc.;

[0054]

[0046] According to one example, the present invention comprises, after the step of predicting the stability of said sample, at least one step of generating at least one report, preferably by a writing module, said report comprising at least said stability score.

[0055]

[0047] According to one example, the present invention comprises an observation module configured to detect any modification of said sample, preferably over a predefined period of time.

[0056]

[0048] According to one example, the present invention comprises a writing module configured to generate at least one report, said report comprising at least said stability score.

[0057]

[0049] Furthermore, the following description listing the principles, aspects, and implementations of the present invention, as well as specific examples thereof, is intended to encompass both their structural and functional equivalents, whether presently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that all block diagrams herein represent conceptual views of illustrative circuits incorporating the principles of the present invention. Similarly, it will be understood that all flowcharts, and the like, represent various processes that may be substantially represented on computer-readable media and thereby executed by a computer or processor, whether or not that computer or processor is explicitly shown.

[0058]

[0050] The functions of the various elements shown in the figures, including any functional block referred to as a "processor" or "module", may be provided by the use of dedicated hardware as well as hardware capable of executing software in association with an appropriate computer program or instructions. When provided by a processor, the instructions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. In some embodiments of the present invention, the processor may be a general-purpose processor, such as a central processing unit (CPU), for example.Furthermore, the explicit use of the term "processor" should not be interpreted as referring exclusively to hardware capable of executing software and may implicitly include, but is not limited to, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or custom, may also be included.

[0059]

[0051] Software modules, or simply modules that are assumed to be software, may be represented herein as any combination of flowchart elements or other elements indicating the execution of process steps and / or a textual description. Such modules may be executed by hardware that is expressly or implicitly represented. Furthermore, it is to be understood that the module may include, for example, but not limited to, computer program logic, computer program instructions, software, firmware, hardware circuitry, or a combination thereof that provides the required capabilities.

[0052] According to one embodiment, the present invention relates to a method for predicting the stability of at least one sample. Preferably, this sample comprises at least one chemical composition.Advantageously, the present invention is configured to predict the stability of said sample during the different stages of its development.

[0060]

[0053] Preferably, said method is configured to be implemented by at least one prediction computer system.

[0061]

[0054] According to one embodiment, and as illustrated by Figures 1 and 2, said method 100 comprises at least the following steps: a. Choice 110, by at least one user, preferably via an input interface 210, of at least one type of formulas; Preferably, said type of formulas is taken from at least one list of predetermined types of formulas; Advantageously, said list of predetermined types of formulas may comprise at least the following types of formulas: i. creams, emulsions, lotions, gels and oils for the skin; ii. beauty masks; iii. foundations (liquids, pastes, powders); iv. powders for makeup, powders to be applied after bathing, powders for body hygiene; v. sun products; vi. sunless tanning products; vii. products for whitening the skin; viii. anti-wrinkle products. ix. shaving products (soaps, foams, lotions); x. makeup and makeup removal products;xi. products intended to be applied to the lips; xii. bath and shower preparations (salts, foams, oils, gels); xiii. toilet soaps, deodorant soaps; xiv. dental and oral hygiene products; xv. external intimate hygiene products; xvi. deodorants and antiperspirants; xvii. hair dyes; xviii. hair waving, straightening and fixing products; xix. hair styling products; xx. hair cleaning products (lotions, powders, shampoos); xxi. hair care products (lotions, creams, oils); xxii. hair styling products (lotions, hairsprays, brilliantines); xxiii. perfumes, eau de toilette and eau de cologne; xxiv. depilatories; xxv. nail care and make-up products. b. Provision 120, by said user, of at least one list of ingredients, preferably via said input interface 210;Preferably, the step of providing at least one list of ingredients may comprise at least one step of comparing at least one ingredient of said list of ingredients with at least one database, preferably an ingredient database; c. Selection 130, preferably automatic, advantageously by at least one selection module 220, of a first artificial intelligence model AI1 from at least a first set of artificial intelligence models. Preferably, said first intelligence model is configured to use a first convolutional neural network RNC1. Advantageously, said first artificial intelligence model AI1 is configured to correspond to said chosen formula; Indeed, advantageously, the present invention comprises a plurality of artificial intelligence models AI, each having been trained to correspond to at least one type of formula;d. Proposal 140, preferably automatic, advantageously by at least one supervision module 230, of at least one operating mode, preferably using said first artificial intelligence model IA1; Preferably, said operating mode is configured to be adapted to said chosen formula and / or said provided ingredients; said operating mode advantageously comprises at least one series of control points; Preferably, said control points are configured to trigger at least one image capture of at least part of said sample being prepared;Advantageously, an operating mode relates to at least one procedure to be followed for the development of a sample. e. At each control point, for example TO, T1, ..., Tfin illustrated in figure 3: i. Capture 150 of at least one image, preferably by microscopy, of at least part of a sample, preferably during development, advantageously by at least one image capture module 240; According to one embodiment, one or more image captures can be carried out automatically and / or manually by the user; ii. Analysis 160 of said captured image, preferably in real time, advantageously by at least one analysis module 250. This analysis is advantageously carried out via a second artificial intelligence model IA2; This second artificial intelligence model IA2 preferably uses a second convolutional neural network RNC2;This analysis is configured to provide a first quality score relative to said sample; Advantageously, this first quality score is a function of the visible state of said sample, preferably of the portion of the sample captured; According to one embodiment, the first quality score can be determined using at least one database comprising a plurality of sample images and a plurality of quality scores; preferably, each image of said plurality of images is associated with a quality score of said plurality of quality scores;Preferably, determining the quality score comprises selecting a sample image from said plurality of sample images and its associated quality score, then modifying said associated quality score based on a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets, etc. iii. Classification 170 of said analyzed image, preferably in real time, advantageously by at least one classification module 260. This classification is advantageously performed via a third AI3 artificial intelligence model;This third AI3 artificial intelligence model preferably uses a third convolutional neural network RNC3. According to one embodiment, this third AI3 artificial intelligence model has been trained from at least one image database. Preferably, said classification step comprises at least:;

[0062] • A step 180 of assigning at least a first stability score to said sample, preferably in the process of being developed, so that:

[0063] ° If said first stability score is greater than or equal to a predetermined threshold, said sample is classified as stable;

[0064] ° If said first stability score is lower than a predetermined threshold, said sample is classified as being unstable; f. Prediction 190, preferably by at least one prediction module 270, of the stability of the sample, preferably during preparation and / or at the end of preparation, by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate a preliminary overall stability score.

[0065]

[0055] The present invention thus allows real-time analysis of the stability of a sample during and at the end of production. This allows for considerable time savings.

[0066]

[0056] According to a preferred embodiment, the present invention may comprise, after the step of predicting the stability of the sample, a stability testing phase.

[0067]

[0057] Advantageously, this sample stability test phase may comprise at least the following steps, as illustrated in Figure 4 for example: a. Storage of said sample according to a first set of storage conditions, preferably using a storage device; These storage conditions may further comprise: i. Storage temperature; ii. Storage atmosphere; iii. Humidity level; iv. ...etc... b. Observation, by at least one observation module 280, preferably over a predefined period of time, of said sample so as to detect any modification of said sample, this observation step comprising at least: i. Capture, by at least said image capture module, of at least one image, preferably by microscopy, of at least part of a sample; ii.Analysis of said captured image, preferably in real time, via the second artificial intelligence model IA2, preferably using the second convolutional neural network RNC2, so as to provide a second quality score; Advantageously, this second quality score is a function of the visible state of said sample, preferably of the part of the sample captured, ieobserved; According to one embodiment, the second quality score may be determined using at least one database comprising a plurality of sample images and a plurality of quality scores; preferably, each image of said plurality of images is associated with a quality score of said plurality of quality scores; preferably, determining the quality score comprises selecting a sample image of said plurality of sample images and its associated quality score, then modifying said associated quality score as a function of a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets, etc. iii.Classification, by at least said classification module, of said image analyzed via the third artificial intelligence model I A3, preferably using the third convolutional neural network RNC3, said third artificial intelligence IA3 having been trained from at least one image database, said classification step comprising at least:.

[0068] • A step of assigning at least a second stability score to said sample so that:

[0069] ° If said second stability score is greater than or equal to a predetermined threshold, said sample is classified as stable;

[0070] ° If said second stability score is lower than a predetermined threshold, said sample is classified as unstable; c. Prediction, by at least said prediction module, of the stability of the sample by mathematically combining the second stability scores of each observation with the second quality scores of each observation so as to calculate an overall stability score.

[0071]

[0058] The present invention thus makes it possible, once the sample has been produced, to predict its stability over time.

[0072]

[0059] Advantageously, the quality score can be determined by comparison with a database of quality scores associated with images serving as reference.

[0073]

[0060] According to one embodiment, said method may comprise, after the step of predicting the stability of said sample, at least one step of generating, preferably by at least one writing module 290, at least one report; Said report may comprise information concerning the preparation of said sample such as the ingredients, or the protocol followed; This report may also comprise at least one image taken during the preparation of said sample; Said report advantageously comprises the stability score.

[0074]

[0061] According to one embodiment, the step of providing the list of ingredients, preferably by the user, may comprise at least one step of comparing at least one ingredient from said list of ingredients with at least one database. This comparison, or otherwise called cross-referencing, thus makes it possible to check for a potential incompatibility, for example, or even to suggest a replacement ingredient, for example.

[0075]

[0062] Preferably, the step of providing the list of ingredients may comprise at least one step of providing, preferably to said user, at least one notification of incompatibility between at least one ingredient of said list of ingredients with at least one other ingredient of said list of ingredients. Advantageously, the present invention makes it possible to anticipate potential incompatibilities between the various ingredients of the list of ingredients.

[0076]

[0063] Advantageously, the step of providing the list of ingredients may comprise at least one step of automatic suggestion of at least one ingredient absent from said list of ingredients and / or of at least one substitute ingredient for at least one ingredient from the list of ingredients. This suggestion step may be carried out for example via a fourth artificial intelligence model I A4, preferably using a fourth convolutional neural network RNC4; This automatic suggestion step is advantageously carried out by at least one suggestion module.

[0077]

[0064] Thus the present invention provides, even before the start of the preparation of a sample, valuable information to the user, such as a proposal to replace an ingredient with an equivalent ingredient or one of better quality or having a lower price.

[0078]

[0065] Advantageously, the fourth AI4 artificial intelligence model was trained via a database comprising lists of ingredients and their characteristics as well as their uses in relation to various formulas.

[0079]

[0066] According to one embodiment, the present invention also allows an update of the operating protocol by the user. Preferably, the method may comprise, after the step of proposing an operating mode, a step of modifying said operating protocol by said user so as to generate a new operating protocol. This allows the user to make adjustments from an automatically proposed operating protocol. Advantageously, this type of adjustment is followed by a step of updating the control points. Thus, advantageously, the present invention may comprise, after the step of modifying said operating protocol, a step of updating said control points according to said new operating protocol.

[0080]

[0067] The present invention, by providing a preliminary stability score during the preparation of a sample, allows the user to improve his sample and therefore his formulation by seeking to improve said score, preferably during the preparation of said sample.

[0081]

[0068] Thus, according to one embodiment, the present invention may comprise, if the sample obtains a preliminary stability score lower than said predetermined threshold, at least one step of proposing at least one measure for restoring the stability of said sample. Advantageously, said measure may comprise at least: a. Addition of a new ingredient; b. Substitution of an ingredient; c. Change or adaptation of the manufacturing protocol; d. Modification of the temperature; e. Modification of the order of introduction of the ingredients; f. ...etc...

[0082]

[0069] In addition to assisting the user in developing a stable sample, the present invention can also provide decision-making assistance during sample development.

[0083]

[0070] According to one embodiment, and as illustrated in Figure 5, the present invention provides support for the development of a sample via the use of image analysis techniques, preferably in real time, and advantageously based on at least one artificial intelligence model.

[0084]

[0071] Thus, for example, the present invention may comprise at least one segmentation step before and / or after the classification step. Figure 5 illustrates, for example, various forms of a sample, for example a microscopy image of an emulsion, of an emulsion comprising objects, of a transparent sample and finally of a transparent sample comprising objects, as examples. It will be noted that various types of lighting may be used (normal or polarized light). This segmentation step may make it possible to graphically isolate one or more areas of the captured image corresponding to possible structural anomalies of the sample. Figure 5 illustrates, for example, the segmentation and / or classification of captured images. Then, an analysis may be carried out so as to classify the identified objects, for example.Advantageously, at each control point and after the step of analyzing said captured image, the present invention may comprise at least one step of identifying and characterizing abnormal elements, also called an object classification step. These abnormal elements may include, for example: crystallization, precipitation, aggregation, poor dispersion, etc.

[0085]

[0072] These various image analyses allow the attribution of a quality score to a sample at various times during its manufacturing phase.

[0086]

[0073] In the event that one or more abnormal elements are identified by the present invention, the latter may comprise a step of providing associated formulation advice relating to the identified abnormal elements, preferably in order to eliminate these possible abnormal elements and / or to correct the formulation of said sample.

[0087]

[0074] Advantageously, the present invention can also make it possible to inform the user, preferably in real time, with information concerning the sample being prepared.

[0088]

[0075] Thus, according to one embodiment, and as illustrated in Figure 5, said method may comprise at least one analysis step, preferably in real time, advantageously by the analysis module, and preferably using said second artificial intelligence model IA2. Advantageously, this analysis step may preferably be carried out in a video mode, i.e. from a video, i.e. from a plurality of images, i.e. from a non-frozen image. This analysis step may comprise for example: a.a step of segmenting said image, preferably by the analysis module; This segmentation step is configured to identify one or more areas of one or more images in order to identify optical differences; Preferably, this segmentation step can be carried out by conventional image analysis techniques; this segmentation step allows identification of anomalies from an image of at least part of a sample being produced; b. a step of classifying said image, preferably by the analysis module; This classification step is configured to classify one or more elements identified during the segmentation step; this classification can be done on the basis of a list of anomaly types for example; This classification step is advantageously carried out using said at least second AI2 artificial intelligence model; c.Optionally, a step of highlighting a region to attract the user's attention; This step of highlighting a region can be carried out by visually delimiting a portion of said image, a portion comprising at least one anomaly; d. Optionally, a step of assigning a label to said region, such as for example: crystal, precipitate, object, etc.; This step of assigning a label thus allows the user to visually identify and locate an anomaly, for example, in the sample being prepared.

[0089]

[0076] The present invention thus makes it possible, in addition to indicating to the user the stability score of the sample, to provide him with information on possible anomalies and their nature.

[0090]

[0077] The present invention thus provides valuable assistance during the manufacture of a sample. Preferably, the present invention also provides valuable assistance during the stability testing phase, after the manufacturing steps.

[0091]

[0078] According to one embodiment, the present invention also relates to a prediction computer system configured to execute the method according to the present invention. Said prediction computer system advantageously comprises at least one processor; said processor is preferably configured to execute a series of instructions.

[0092]

[0079] According to one embodiment, said computer prediction system comprises at least: a. An input interface configured to allow at least one user to: i. Choose at least one type of formula; ii. Provide at least one list of ingredients; b. A selection module configured to automatically select at least said first artificial intelligence model AI1 from a first set of artificial intelligence models; c. A supervision module configured to automatically propose at least one operating mode adapted to said chosen formula and / or said provided ingredients, preferably using said first artificial intelligence model AI1; d. An image capture module configured to capture at least one image of at least part of a sample, preferably by microscopy; e.An analysis module configured to analyze at least one captured image, preferably in real time, via said second artificial intelligence model AI2 and to associate said first quality score with at least one captured image; f. A classification module configured to i. Classify at least one analyzed image via said third artificial intelligence model AI3; g.A prediction module configured to predict the preliminary stability of the sample by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate an overall preliminary stability score; Preferably, said prediction module is also configured to predict the stability of the sample by mathematically combining the second stability scores of each observation with the second quality scores of each observation so as to calculate an overall stability score; Advantageously, said prediction module is configured to predict the stability of the sample by mathematically combining the stability scores of each control point with the quality scores of each control point so as to calculate a stability score; h.Preferably, an observation module configured to detect any modification of said sample, preferably over a predefined period of time, advantageously during a stability test phase; i. Preferably, a writing module configured to generate at least said report comprising at least said stability score;

[0093]

[0080] Thus, the present invention makes it possible to predict the stability of a sample at various times during its manufacture and storage.

[0094]

[0081] The invention is not limited to the embodiments previously described and extends to all the embodiments covered by the claims.

[0095]

[0082] Digital references

[0096] 100 Prediction Method

[0097] 110 Choosing a type of formula

[0098] 120 Provision of a list of ingredients

[0099] 130 Selection of a first AI1 artificial intelligence model

[0100] 140 Proposal for an operating procedure

[0101] 150 Capturing an image

[0102] 160 Analysis of a captured image

[0103] 170 Classification of an analyzed image

[0104] 180 Assignment of an initial stability score

[0105] 190 Prediction of a preliminary stability score

[0106] 200 Prediction System

[0107] 210 Input interface

[0108] 220 Selection Module

[0109] 230 Supervision module

[0110] 240 Capture Module

[0111] 250 Analysis Module

[0112] 260 Classification Module

[0113] 270 Prediction Module

[0114] 280 Observation Module

[0115] 290 Writing Module

Claims

Claims

1. Method (100) for predicting the stability of at least one sample, preferably comprising at least one chemical composition, preferably during the different stages of its development, said method (100) being configured to be implemented by at least one computer prediction system (200), said method comprising at least the following steps: a. Choice (110), by at least one user, preferably via an input interface (210), of at least one type of formulas, said type of formulas being taken from at least one list of predetermined types of formulas; b. Provision (120), by said user, of at least one list of ingredients, preferably via said input interface (210); c.Automatic selection (130), preferably by at least one selection module (220), of a first artificial intelligence model IA1, preferably using a first convolutional neural network RNC1, from a first set of artificial intelligence models, said first artificial intelligence model IA1 being configured to correspond to said chosen formula; d. Automatic proposal (140), preferably by at least one supervision module (230), using said first artificial intelligence model IA1, of at least one operating mode adapted to said chosen formula and / or to said provided ingredients, said operating mode comprising at least one series of control points, said control points being configured to trigger at least one image capture of at least part of said sample being prepared; e. At each control point: i.Capturing (150) at least one image, preferably by microscopy, of at least part of a sample, preferably being prepared, by at least one image capture module (240); ii. Analyzing (160) said captured image, preferably in real time, by an analysis module (250), via a second artificial intelligence model IA2, preferably using a second convolutional neural network RNC2, so as to provide a first score. of quality; iii. Classification (170), by at least one classification module (260), of said image analyzed via a third AI3 artificial intelligence model, preferably using a third RNC3 convolutional neural network, said third AI3 artificial intelligence having been trained from at least one image database, said classification step comprising at least: • A step of assigning (180) at least a first stability score to said sample so that: ■ If said first stability score is greater than or equal to a predetermined threshold, said sample is classified as stable; ■ If said first stability score is lower than a predetermined threshold, said sample is classified as being unstable; f. Prediction (190) of the stability of the sample, by at least one prediction module (270), by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate a preliminary overall stability score.

2. Method (100) according to the preceding claim comprising, after the step of predicting (190) the stability of the sample, a stability testing phase comprising at least the following steps: a. Storing said sample according to a first set of storage conditions, preferably using a storage device; b. Observing, by at least one observation module (280), preferably over a predefined period of time, said sample so as to detect any modification of said sample, this observation step comprising at least: i. Capturing, by at least said image capture module, at least one image, preferably by microscopy, of at least part of a sample; ii. Analyzing said captured image, preferably in real time, via the second artificial intelligence model AI2, preferably using the second convolutional neural network RNC2, so as to provide a second quality score; iii.Classification, by at least said classification module, of said image analyzed via the third AI3 artificial intelligence model, preferably using the third convolutional neural network. RNC3, said third artificial intelligence IA3 having been trained from at least one image database, said classification step comprising at least: • A step of assigning at least a second stability score to said sample so that: ■ If said second stability score is greater than or equal to a predetermined threshold, said sample is classified as stable; ■ If said second stability score is lower than a predetermined threshold, said sample is classified as unstable; c. Prediction, by at least the prediction module, of the stability of the sample by mathematically combining the second stability scores of each observation with the second quality scores of each observation so as to calculate an overall stability score. [Claim s] A method (100) according to any preceding claim wherein the first quality score and the second quality score are a function of the visible condition of the sample.

4. A method (100) according to any preceding claim wherein the first quality score and the second quality score are determined using at least one database comprising a plurality of sample images and a plurality of quality scores, each image of said plurality of images being associated with a quality score of said plurality of quality scores. [Claim s] Method (100) according to the preceding claim wherein determining the first quality score and the second quality score comprises selecting a sample image from said plurality of sample images and its associated quality score, then modifying said associated quality score as a function of a plurality of parameters taken from at least: the presence of artifacts, the presence of droplets, the size of the droplets, the homogeneity of the droplets. [Claim s] Method (100) according to any one of the preceding claims in which the step of providing (120) at least one list of ingredients comprises at least one step of comparing at least one ingredient of said list of ingredients with at least one database.

7. A method (100) according to any preceding claim wherein the step of providing (120) at least one list of ingredients comprises at least one step of providing said user with at least one notification of incompatibility between at least one ingredient of said list of ingredients with at least one other ingredient of said list of ingredients. [Claim s] Method (100) according to any one of the preceding claims wherein the step of providing (120) at least one list of ingredients comprises at least one step of automatic suggestion by at least one fourth artificial intelligence model, preferably using a fourth convolutional neural network RNC4, of at least one ingredient absent from said list of ingredients and / or of at least one substitute ingredient for at least one ingredient from the list of ingredients.

9. Method (100) according to any one of the preceding claims comprising: a. after the step of proposing (140) an operating mode, a step of modifying said operating protocol by said user so as to generate a new operating protocol; b. after the step of modifying said operating protocol, a step of updating said control points according to said new operating protocol.

10. Method (100) according to any one of the preceding claims comprising, if the sample obtains a stability score lower than said predetermined threshold, at least one step of proposing at least one measure for restoring the stability of said sample, said measure being taken from at least: a. Addition of a new ingredient; b. Substitution of an ingredient; c. Change or adaptation of the manufacturing protocol; d. Modification of the temperature; e. Modification of the order of introduction of the ingredients.

11. Method (100) according to any one of the preceding claims comprising at least one segmentation step before the classification step.

12. Method (100) according to any one of the preceding claims comprising, at each control point and after the step of analysis (160) of said captured image, at least one step of identification and characterization of abnormal elements.

13. Method (100) according to the preceding claim wherein the step of analyzing (160) said captured image comprises a step of providing advice on formulation associated with the abnormal elements identified.

14. Method (100) according to any one of the preceding claims comprising at least one step of real-time analysis, preferably in a video mode (non-frozen image), comprising: a. a step of segmenting said image; b. a step of classifying said image; c. a step of highlighting a region to attract the user's attention; d. a step of assigning a label to said region;

15. Method (100) according to any one of the preceding claims comprising, after the step of predicting (190) the stability of said sample, at least one step of generating at least one report, by a writing module (290), said report comprising at least said stability score.

16. A computer program product, preferably recorded on a non-transitory memory medium, comprising instructions, which when performed by at least one of a processor, a computer, executes the method (100) according to any one of the preceding claims.

17. Non-transitory memory medium comprising at least one computer program product according to the preceding claim.

18. A computer prediction system (200) configured to execute the method (100) according to any one of claims 1 to 15, said computer prediction system (200) comprising at least: a. an input interface (210) configured to allow at least one user to: i. Choose at least one type of formula; ii. Provide at least one list of ingredients; b. A selection module (220) configured to automatically select at least one first AI1 artificial intelligence model from a first set of artificial intelligence models, said first AI1 artificial intelligence model being configured to correspond to said chosen formula; c. A supervision module (230) configured to automatically propose at least one operating mode adapted to said chosen formula and / or said provided ingredients, said operating mode comprising at least one series of control points, said control points being configured to trigger at least one image capture; d. An image capture module (240) configured to capture at least one image of at least part of a sample, preferably by microscopy; e. An analysis module (250) configured to analyze at least one captured image, preferably in real time, via a second AI2 artificial intelligence model and to associate with at least one captured image a first quality score; f. A classification module (260) configured to i. classify at least one analyzed image via a third AI3 artificial intelligence model, said third AI3 artificial intelligence having been trained from at least one image database, said classification step comprising at least: ii.Assign at least a first stability score to said sample so that:. ■ If said first stability score is greater than or equal to a predetermined threshold, said sample is classified as stable; ■ If said first stability score is lower than a predetermined threshold, said sample is classified as unstable; g. A prediction module (270) configured to predict the stability of the sample by mathematically combining the first stability scores of each control point with the first quality scores of each control point so as to calculate a preliminary overall stability score.

19. Computer prediction system (200) according to the preceding claim comprising an observation module (280) configured to detect any modification of said sample, preferably over a predefined period of time.

20. A computer prediction system (200) according to any one of the two preceding claims comprising a writing module (290) configured to generate at least one report, said report comprising at least said stability score.

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