Biomimetic treatment method and device

Abstract

The present invention relates to a biomimetic processing method and a corresponding device (10). The method comprises: determining, from context data (DT1), a primary function (F1) for solving a technical problem (P1); deriving a secondary function (F2) by abstraction; identifying target functions (Fx) by extracting keywords (MT1) and selecting from taxonomy data (DT2); determining, from biomimetic data (DT3), a primary biological model (ML1) responding to the target functions (Fx); identifying a biological characteristic (C1) of the primary biological model; deriving a secondary biological model (ML2) associated with said biological characteristic; and selecting, from identified biological models, at least one biological model of interest (Mx) for responding to the technical problem. Figure 1

Description

Title of the invention: Biomimetic treatment method and device Technical field

[0001] The present invention relates to biomimetic treatment methods and devices, and particularly aims at such methods and devices for determining a biological model of interest that addresses a technical problem according to a biomimetic approach. Technological background

[0002] Biomimetics is an engineering approach that draws inspiration from natural systems and processes to solve technical problems. It takes advantage of the strategies developed by living organisms during evolution to propose innovations in various fields such as aerospace, medicine, and robotics. For example, in aerospace, the design of structures inspired by the morphology of birds to improve aerodynamics or in medicine, the development of new materials for tissue regeneration by drawing inspiration from the properties of skin or bones.

[0003] However, despite its potential, the biomimetic design process faces several challenges. One of the main problems is that the biological models used are sometimes unsuitable for the intended technical applications. Natural systems, due to their diversity and complexity, cannot always be mimicked in a straightforward or simplified manner. The transition from a biological model to a technical application often poses challenges in terms of design and implementation, thus limiting the effectiveness of biomimetic solutions.

[0004] In addition, there is a lack of specific biomimetic design tools, which makes it difficult to fully exploit the potential of natural systems for technical applications. Excessive simplifications or misinterpretations of biological phenomena can lead to ineffective or irrelevant solutions.

[0005] Thus, although biomimicry offers a promising avenue for the development of innovative and sustainable solutions to technical problems, biomimetic design and processing tools need to be improved to overcome current limitations. Summary of the present invention

[0006] One of the objects of the present invention is to solve at least one of the problems or deficiencies of the technological background described above.

[0007] One of the objects of the present invention is to determine in an efficient and structured manner a high-performance biological model for solving a technical problem according to a biomimetic approach.

[0008] One of the objects of the present invention is to determine a relevant biological model making it possible to resolve a problem comprising the capture, storage and / or use of carbon.

[0009] According to a first aspect, the present invention relates to a biomimetic processing method, implemented by a processing device, which method comprises: a) determining, from context data obtained as input, at least one primary function for solving a technical problem; b) derivation of at least one secondary function by abstraction of said at least one primary function; (c) identification of target functions by extracting keywords from primary and secondary functions and by selecting functions from taxonomy data according to the keywords; (d) determination, from biomimetic data, of at least one primary biological model responding to the target functions; (e) identification of at least one biological characteristic of said at least one primary biological model; (f) derivation of at least one secondary biological model, other than said at least one primary biological model, associated with said at least one biological characteristic; and g) selection, from among biological models identified during said process, of at least one biological model of interest to respond to the technical problem.

[0010] The method according to the invention may include other characteristics which may be taken separately or in combination, in particular among the embodiments which follow.

[0011] According to a particular embodiment, the derivation b) comprises at least one phase of abstraction of said at least one primary function by transposition from a technological domain to a biological domain.

[0012] According to a particular embodiment, during identification f), said at least one secondary biological model is identified upon detection that it shares in common said at least one biological characteristic with said at least one primary biological model.

[0013] According to a particular embodiment, said at least one biological characteristic defines a biological strategy exploited by a biological entity.

[0014] According to a particular embodiment, the method comprises a first iterative process comprising, for each primary or secondary biological model identified respectively in d) or f): - identification of a new function defined in said biological model or of at least one new keyword associated with said biological model; and - repeating steps d) to f) using the new function as a target function or using said at least one new keyword to determine a new primary biological model in d).

[0015] According to a particular embodiment, the first iterative process comprises: - if the new function has a relevance score at least equal to a threshold value, a said new function is identified and serves as a target function to determine a said new primary biological model during a new iteration of the determination d); and - if the new function has a relevance score lower than the threshold value, at least one said new keyword is identified and used to determine a said new primary biological model during a new iteration of the determination d).

[0016] According to a particular embodiment, the method comprises a second iterative process comprising, for each biological characteristic identified in e): - identification of a new relevant word associated with said biological characteristic; and - repeating steps c) to e) using the new relevant word as a keyword to identify new target functions.

[0017] According to a particular embodiment, the technical problem comprises the capture, storage and / or use of carbon.

[0018] According to a second aspect, the present invention relates to a treatment device configured to implement the method according to the first aspect of the invention. In particular, the device of the second aspect of the invention may comprise a memory associated with a processor configured to implement the steps of the biomimetic treatment method according to the first aspect of the present invention.

[0019] It should be noted that the various embodiments mentioned above in relation to the biomimetic treatment method according to the first aspect of the invention as well as the associated advantages apply in a similar manner to the treatment device according to the second aspect of the invention.

[0020] According to a third aspect, the present invention relates to a computer program which comprises instructions adapted for executing the steps of the biomimetic treatment method according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor. In other words, the different steps of the biomimetic method are determined by computer program instructions. This computer program is configured to be implemented in a treatment device of the second aspect. of the invention, or more generally in a computer.

[0021] Such a computer program may use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0022] According to a fourth aspect, the present invention relates to a recording medium (or information medium), readable by the processing device according to the second aspect or more generally by a computer (or a processor), on which is recorded a computer program comprising instructions for executing the steps of the biomimetic treatment method according to the first aspect of the present invention.

[0023] On the one hand, the recording medium may be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a microelectronic circuit type ROM memory, or a magnetic recording means or a hard disk.

[0024] Furthermore, this recording medium may also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or hertzian radio or by self-directed laser beam or by other means. The computer program according to the present invention may in particular be downloaded from an Internet-type network.

[0025] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to perform or to be used in performing the method in question.

[0026] The invention advantageously makes it possible to determine (or generate) in an efficient and structured manner a high-performance biological model for solving a technical problem according to a biomimetic approach. It can, for example, make it possible to determine a relevant biological model making it possible to solve a problem relating to the capture, storage and / or use of carbon. Brief description of the figures

[0027] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting exemplary embodiments of the present invention below, with reference to the appended figures 1 and 2, in which:

[0028] [Fig.l] schematically illustrates an environment comprising a vehicle carrying a control device, according to at least one particular and non-limiting embodiment of the present invention; and

[0029] [Fig.2] schematically illustrates the vehicle of [Fig.l], comprising a device control and apparatus, according to at least one particular and non-limiting embodiment of the present invention. Description of examples of implementation

[0030] A method and a device for biomimetic treatment will now be described in the following with reference to Figures 1 and 2. Unless otherwise indicated, elements common or similar to several figures bear the same reference signs and have identical or similar characteristics, so that these common elements are generally not described again for the sake of simplicity.

[0031] The terms "first(s)", "second(s)", etc.) are used in this document by arbitrary convention to enable different elements (such as operations, means, etc.) implemented in the embodiments described below to be identified and distinguished.

[0032] As previously indicated, the invention relates in particular to a biomimetic treatment method, and a corresponding device, aimed at determining or generating a relevant biological model making it possible to effectively respond to a technical problem according to a biomimetic approach. By nature, such an approach is based on characteristics or properties (shapes, processes, functions, etc.) of living things to solve technical problems, preferably in the form of sustainable solutions for the environment.

[0033] In the following exemplary embodiments, the invention is described in the context of solving a particular example of a technical problem, namely the problem of capturing, storing and / or using carbon. Although principles of the particular details of implementing the invention are described in this particular context, it should be noted that the invention is not limited to these particular cases and can be generalized to solving various types of technical problems (or issues), in particular in engineering, industry, the design of devices, for example of the mechanical, mechatronic, electronic type, etc.

[0034] Other aspects and advantages of the present invention will emerge from the exemplary embodiments described below with reference to the drawings mentioned above.

[0035] [Fig.l] schematically illustrates a treatment device 10 (or biomimetic treatment device) according to a particular embodiment of the invention. As illustrated, the treatment device 10 (also called hereinafter “device”), comprises in this example at least one processor 40 and a non-volatile memory 4L. The device 10 is configured to implement a biomimetic treatment method as described below to solve a technical problem noted PL. For this purpose, the device 10 may comprise a computer program PG1. stored in the non-volatile memory 41 (Flash or ROM type memory for example), this computer program PG1 comprising instructions for implementing the biomimetic treatment method (or process) as described below. The processor 40 is thus configured to execute in particular the instructions defined by the computer program PG1.

[0036] The device 10 may for example take the form of a (or comprise a) computer, or a combination of computers. For example, the device 10 is (or comprises) at least one server.

[0037] As shown in [Fig.l], the device 10 can receive data as input, namely DTI context data, DT2 taxonomy data and DT3 biomimetic data. By executing a processing taking this data as input, the device 10 is configured to produce as output a result comprising the identification of a biological model of interest Mx which is efficient for solving the technical problem PL During this processing, the device 10 can generate intermediate data used to arrive at the final result. In particular, the device 10 can determine: at least one primary function F1, at least one secondary function F2, keywords MT1, target functions Fx, at least one primary biological model ML1, at least one biological characteristic Cl of said at least one primary biological model ML1 and at least one secondary biological model ML2.

[0038] The device 10 may, if necessary, store each of these data in a memory during its processing, for example in a volatile memory and / or a non-volatile memory accessible by, or integrated into, the device 10.

[0039] The nature and use of each of the aforementioned data are described in detail in the following exemplary embodiments.

[0040] As indicated above, the control system 10 is configured to implement a biomimetic treatment method. This treatment method is now described in conjunction with FIGS. 1 and 2 according to particular embodiments. For this purpose, the device 10 can execute the instructions of the computer program PG1 to implement the steps of the biomimetic treatment method.

[0041] It is assumed in the following that the device 10 is configured to determine or generate a relevant biological model noted MLx to resolve a technical problem noted PL. By way of example, it is considered here that this problem PI comprises the capture, storage and / or use of carbon.

[0042] During a step S2, the device 10 determines, from context data DTI obtained as input, at least one primary function F1 to resolve the technical problem PL. For the sake of simplification of the present description, it is considered subsequently that a single primary function F1 is determined in S2. are possible in which a plurality of primary functions Fl are determined.

[0043] This DTI context data, characterizing the technical problem PI, can be obtained, recovered or generated in any appropriate way, for example by recovery from a memory or by reception from a device external to the device 10 (for example by consultation of an internal or external database).

[0044] As an example, we subsequently consider that the primary function Fl determined in S2 is defined as follows: “Remove CO2 from the atmosphere”, although other primary functions Fl are possible. It is necessary to analyze the problem in order to identify a problem. We then identify the required function, i.e. the function that responds to the problem. In our case, it is about removing CO2 from the atmosphere.

[0045] During a step S4, the device 10 derives at least one secondary function F2 by abstraction of the primary function F1. According to one example, this derivation step S4 comprises at least one phase of abstraction of the primary function F1 by transposition from a technological domain to a biological domain.

[0046] It is thus possible to advantageously abstract the primary function Fl by transposing the problem PI from technology to biology via more or less high abstraction phases. This derivation S4 by abstraction makes it possible to establish a resolution space in which potential biological models can be identified.

[0047] In particular, successive abstraction phases taking as input the result of the previous abstraction phase can be executed to determine a plurality of secondary functions F2. For example, during a first abstraction phase, it is subsequently considered that the device 10 identifies the secondary function F2 “How to remove and / or capture CO2 from the atmosphere” by abstraction of the primary function F1, then during a second abstraction phase identifies the secondary function F2 “How to separate, consume or remove gases”. It is thus possible to define an expanded resolution space which makes it possible not to close doors and to identify a wide variety of secondary functions F2 relevant to the problem PI considered, which makes it possible to maximize the chances of identifying the best biological model of interest MLx to respond to the technical problem PI considered.

[0048] During a step S6, the device 10 identifies target functions Fx by extracting keywords MT1 from the primary function F1 and the secondary functions F2, and then by selecting functions from taxonomy data DT2 according to the keywords. To do this, the device 10 obtains, receives or retrieves, as input, the taxonomy data DT2 in any suitable manner, by example by consulting a database, internal or external to the device 10.

[0049] The DT2 taxonomy data, the characteristics of which may vary depending on the case, define a biomimicry taxonomy. This taxonomy defines functions in a structured organization (classification system) according to parent / child relationships between functions. As an example, one can use the biomimicry taxonomy developed by the Biomimicry Institute (a private American institute). This taxonomy categorizes different ways in which organisms and natural systems respond to functional challenges into groups of related functions.

[0050] Thus, during the identification step S6, the device 10 can identify relevant keywords (or terms) concerning the problem PI, or more precisely in relation to the functions F1 and F2 previously determined. The keywords MT1 can correspond to characters, sequences of characters, words, sentences or portions of sentences for example. These keywords can be selected by browsing the taxonomy defined by the taxonomy data DT2 so as to extract keywords associated with the functions F1 and F2, or even with categories, or subcategories, associated with these functions.

[0051] According to the example envisaged here, the device 10 thus identifies for example the following keywords from the functions F1 and F2 considered: “Carbon”, “Capture” and “Storage”. It is subsequently considered by way of example that the following target functions Fx are thus identified during step S6: “Get, store or distribute resources > Capture, adsorption or filtration” (in English: “Get, store or distribute resources > Capture, absorb or filter”) for the capture functions and “Get, store or distribute resources > Store” (in English: “Get, store or distribute resources > store”) for the storage functions.

[0052] By using the keywords MT1 from the functions Fl and F2, as well as the taxonomic data DT, we can maximize the chances of identifying the best biological model of interest MLx to respond to the technical problem PI considered. In this way, we can in particular diversify the searches by exploring the taxonomy according to various keywords associated with the primary and secondary functions.

[0053] During a determination step S8, the device 10 determines, from biomimetic data DT3, at least one primary biological model ML1 responding to the target functions Fx. To do this, the device 10 obtains, receives or retrieves, as input, the biomimetic data DT3 in any appropriate manner, for example by consulting a database, internal or external to the device 10.

[0054] This step makes it possible to identify one or more primary biological models ML1 responding to the targeted functions Fx. For example, a search can be carried out for this purpose in a database storing a collection of possible biological models, for example an open access database, such as Asknature®, which is dedicated to biomimicry, or Wiley®, INPN®, BioOne® or GBIF®. In this way, we can identify relevant biological models that respond to the PI problem as well as to the relevant Fl and F2 functions established previously.

[0055] It may subsequently be considered by way of example that the device 10 identifies during step S8 the following models as primary biological models ML1: a plant model, a microalgae model, a model linked to Rubisco (or RuBisCO, from its full name ribulose-1,5-bisphosphate carboxylase / oxygenase), a model linked to cobalt oxide, etc. Rubisco is a key enzyme in photosynthesis which in particular allows the fixation of carbon dioxide (CO2) in plant biomass. For the sake of

[0056] During an identification step S10, the device 10 identifies at least one biological characteristic Cl of the primary biological models ML1 previously determined in S8 ([Fig.2]). A biological characteristic Cl may be a principle or a property implemented by a biological entity associated with the primary biological model ML1 in question.

[0057] According to one example, each biological characteristic Cl defines a biological strategy (or property) exploited by a biological entity associated with the primary biological model ML1 concerned.

[0058] Thus, during step S10, it is possible to identify principles and structures exploited by the primary biological model(s) ML1 determined in S8 ([Fig.2]). The device 10 analyzes the biological strategies used by the primary biological model(s) ML1 in order to subsequently be able to identify other biological models (called secondary models) based on identical or analogous strategies, as described below.

[0059] During an identification step S12, the device 10 derives at least one secondary biological model ML2, other than the primary biological models ML1 determined in S8, associated with the biological characteristics Cl identified in S10 ([Fig.2]). In other words, once the biological characteristics Cl have been determined in S10, the device 10 deduces (S 12) one or more secondary biological models ML2.

[0060] According to one example, during identification S12, one or more secondary biological models ML2 are identified upon detection that they share at least one biological characteristic Cl in common with the primary biological model(s) ML1.

[0061] For example, to meet the carbon capture function, the device 10 can identify the mutant gene CNI (chromosomal mutant “Neurospora crassa”), present in snapdragons, which allows homogeneous growth within the leaves of the tree, which allows the leaves not to be curved but rather flat, thus increasing the surface area of ​​the leaves exposed to the sun, which therefore makes it possible to maximize photo- synthesis. We therefore note that photosynthesis is a principle that consumes CO2.

[0062] During a selection step S14, the device 10 selects, from among the biological models ML1 and ML2 identified during said method, at least one biological model of interest Mx to respond to the technical problem PI.

[0063] The invention advantageously makes it possible to determine (or generate) in an efficient and structured manner an effective MLx biological model for solving a PI technical problem according to a biomimetic approach. It is for example possible to determine a relevant MLx biological model making it possible to effectively solve a problem relating to the capture, storage and / or use of carbon, as described above in particular examples.

[0064] Thanks to the invention, it is possible to advantageously broaden the scope of the search for biological models in which a biological model of interest must be chosen. It is possible to efficiently and automatically diversify the search for candidate biological models in order to deduce the most relevant model(s).

[0065] The invention can help its users during a biomimetic approach to identify the most relevant biological model(s) for addressing a given technical problem. The search process can be refined to avoid missing potentially relevant models.

[0066] According to a particular example represented in [Fig.2], the biomimetic processing method implemented by the device 10 comprises a first iterative process S20 comprising, for each primary and / or secondary biological model (ML1, ML2) identified respectively in S8 and / or S12: - identification of a new F3 function defined in said biological model or of at least one new MT2 keyword associated with said biological model; and - reiteration of steps S8 to S12 using the new function F3 as target function Fx or using said at least one new keyword MT2 to determine a new primary biological model ML1 in S8 ([Fig.2]).

[0067] It is thus possible to use either the new function F3 as a target function Fx or to use one or more new keywords MT2 to determine a new primary biological model ML1, the strategy used among these being a function of the relevance of the new function F3 identified in relation to a threshold value noted TH1 ([Fig.2]). To this end, the device 10 can for example evaluate a relevance score for the new function F3 with respect to the technical problem PI and compare this score with the threshold value TH1.

[0068] According to an example, the first iterative process S20 comprises: - if the new function has a relevance score at least equal to the threshold value TH1, a so-called new function F3 is identified and serves as a target function Fx to determine a so-called new primary biological model ML1 during a new iteration of determination S8 ([Fig.2]); and - if the new function has a relevance score lower than the threshold value TH1, at least one said new keyword MT2 is identified and used to determine a said new primary biological model ML1 during a new iteration of the determination S8.

[0069] It is thus advantageous to repeat iteratively, once or a plurality of times, steps S8-S12, in order to improve the result of the method and therefore obtain a better quality biological model of interest MLx to respond to the technical problem PI.

[0070] Thus, a biological model ML1 identified in S8 can have several functions, including at least one new function F3 which is distinct from the function(s) F1 and F2 previously identified. For example, “giant larvacean” marine organisms filter gases but also have the function “Chemically break down organic compounds”. If one of these “new” functions F3 is sufficiently relevant to the problem PI to be solved, the method can then return to step S8 ([Fig. 2]) to search for biological solutions and therefore search for other biological examples having this same function. The method can thus continue as already described. If, however, this new function F3 is not sufficiently relevant to the problem PI, new relevant biological words MT2 can be determined and used during a new iteration of step S8.

[0071] According to a particular example represented in [Fig.2], the biomimetic treatment method implemented by the device 10 comprises a second iterative process S22 comprising, for each biological characteristic Cl identified in S10: - identification of a new relevant word MT2 associated with said biological characteristic Cl; and - reiteration of steps S6 to S10 using the new relevant word MT2 as a keyword to identify new target functions Fx.

[0072] Thus, during step S10 of identifying the principles involved by the primary biological models ML1, the device 10 can identify new relevant biological words MT2 (distinct from the words MT1 previously identified), which allows us to go back to step S6 ([Fig.2]), thus increasing the relevance and the number of potentially relevant biological models.

[0073] The iteration processes S20 and S22 can be executed cumulatively, or only one or the other. These iterative processes advantageously make it possible to discover new relevant biological models and thus to improve the result of the method. Once all the relevant biological models have been identified during the method, said method can continue with step S12 of selecting the or interest patterns.

[0074] As illustrated in [Fig. 1], the processor 40 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 10 further comprises at least one memory 41 corresponding for example to a volatile and / or non-volatile memory and / or comprises a memory storage device which may comprise volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0075] The computer code of the embedded software program(s) comprising the instructions to be loaded and executed by the processor 40 is for example stored on the memory 4L. The memory 41 can constitute an information medium according to a particular embodiment in that it comprises a computer program (for example PG1) comprising instructions for carrying out the steps of the method of the invention.

[0076] According to a particular and non-limiting exemplary embodiment, the device 10 comprises a communication interface 42 for communicating with external devices, for example a remote server or the “cloud”. The communication interface 42 may comprise one or more of the following interfaces:

[0077] - RF radio frequency interface, for example of the Wi-Fi® type (according to IEEE 802.11), by example in the 2.4 or 5 GHz frequency bands, or Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox type using UBN radio technology (Ultra Narrow Band), or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced;

[0078] - USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus" in French);

[0079] - HDMI interface (from the English “High Definition Multimedia Interface”, or “High Definition Multimedia Interface” in French).

[0080] According to another particular and non-limiting exemplary embodiment, the communication interface 43 is configured to allow communication to be established with other devices (such as the previously mentioned databases). The communication interface 43 corresponds for example to a transmitter configured to transmit and receive information and / or data via a communication channel (not shown).

[0081] According to a particular and non-limiting exemplary embodiment, the control device 10 can provide output signals to one or more external devices, such as a display screen, touch-sensitive or not, one or more speakers and / or other peripherals (projection system) via respective output interfaces. According to a variant, one or the other of the external devices is integrated into the device 10.

[0082] As understood by a person skilled in the art, all the embodiments and variants described above, some of which have been deliberately simplified to facilitate explanations, constitute only non-limiting examples of implementation of the present disclosure. In particular, a person skilled in the art may envisage any adaptation or combination of the embodiments and variants described above, in order to meet a particular need.

[0083] The present invention is therefore not limited to the embodiments described above but extends in particular to a treatment method which would include secondary steps without thereby departing from the scope of the present invention. The same would apply to a device configured for the implementation of such a method.

Claims

Claims

1. A biomimetic processing method, implemented by a processing device, said method comprising: a) determining, from context data (DTI) obtained as input, at least one primary function (Fl) for solving a technical problem; b) deriving at least one secondary function (F2) by abstracting said at least one primary function; c) identifying target functions (Fx) by extracting keywords (MT1) from the primary and secondary functions and by selecting functions from taxonomy data (DT2) in accordance with the keywords; d) determining, from biomimetic data (DT3), at least one primary biological model (ML1) responding to the target functions (Fx); e) identifying at least one biological characteristic (Cl) of said at least one primary biological model;f) derivation of at least one secondary biological model (ML2), other than said at least one primary biological model, associated with said at least one biological characteristic; and g) selection, from among biological models identified during said method, of at least one biological model of interest (Mx) to respond to the technical problem.;

2. Method according to claim 1, in which the derivation b) comprises at least one phase of abstraction of said at least one primary function by transposition from a technological domain to a biological domain.

3. Method according to claim 1 or 2, wherein during identification f), said at least one secondary biological model is identified upon detection that it shares in common said at least one biological characteristic with said at least one primary biological model.

4. A method according to any preceding claim, wherein said at least one biological characteristic defines a biological strategy exploited by a biological entity.

5. A method according to any preceding claim, wherein the method comprises a first iterative process comprising, for each primary or secondary biological model identified respectively in d) or f): - identification of a new function (F3) defined in said biological model or of at least one new keyword associated with said biological model; and - reiteration of steps d) to f) using the new function as a target function or using said at least one new keyword to determine a new primary biological model in d).

6. Method according to claim 6, in which the first iterative process comprises: - if the new function has a relevance score at least equal to a threshold value, a said new function is identified and serves as a target function to determine a said new primary biological model during a new iteration of the determination d); and - if the new function has a relevance score lower than the threshold value, at least one said new keyword is identified and used to determine a said new primary biological model during a new iteration of the determination d).

7. Method according to any one of the preceding claims, wherein the method comprises a second iterative process comprising, for each biological characteristic identified in e): - identification of a new relevant word associated with said biological characteristic; and - reiteration of steps c) to e) using the new relevant word as a keyword to identify new target functions.

8. A method according to any preceding claim, wherein the technical problem comprises the capture, storage and / or use of carbon.

9. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor.

10. Biomimetic treatment device (10), said device comprising a memory (41) associated with at least one processor (40) configured for implementing the steps of the method according to any one of claims 1 to 8.

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