EP-4736165-A1 - METHOD TO BUILD A DIGITAL TWIN

EP4736165A1EP 4736165 A1EP4736165 A1EP 4736165A1EP-4736165-A1

Abstract

A Method for building a digital twin for a living system, whereas such a system is an organism, preferably a plant, an algae, a water-borne animal, a fungus, and/or a worm, wherein A Provision of a number of multiple living systems of the same type; B Growing the living systems of the same type under different growing conditions; C Determination of one or more parameters of the living system that is or are subjected to a change in over the time period of the growth; wherein multiple data sets are determined and stored, each data set has said parameter or parameters and a time stamp of the time of determination; and D Generation of a digital twin for said living system based on said multiple data set representing different growth stages of said living system.

Inventors

CIRILLO, FABIO
RUTZ, Benjamin

Assignees

Cirillo, Fabio
Rutz, Benjamin

Dates

Publication Date: 20260506
Application Date: 20240614

Claims (11)

1 . Method for building a digital twin for a living system, whereas such a system is an organism, preferably a plant, an algae, a water-borne animal, a fungus, and/or a worm, characterized by at least the following steps: A Provision of a number of multiple living systems of the same type; B Growing the living systems of the same type under different growing conditions; C Determination of one or more parameters of the living system that is or are subjected to a change in over the time period of the growth; wherein multiple data sets are determined and stored, each data set has said parameter or parameters and a time stamp of the time of determination; and D Generation of a digital twin for said living system based on said multiple data set representing different growth stages of said living system.
2. Method according to claim 1 characterized in that the method is applied for controlled environment and further comprises in Step E the determination of optimized growing conditions with respect to at least one parameter of the living system that is subjected to a change over the time period of the growth, said parameter is selected per each group between group 1 : I the concentration of an ingredient generated by or digested by the living system; II the concentration of an excrement and/or exhaust and/or; III the suppression of an ingredient generated by the living system; and group 2: VI the speed of growth; V the amount of biomass; VI the movement of a living system and/or; VII the amount and/or the mass and/or the form of descendants or offspring, such as fruits and/or blossoms.
3. Method according to claim 1 or 2, characterized in that the parameter of step C is selected from the following parameters: i speed of growth of the living system; ii color of the living system; iii form of specific parts of the living system; iv form, color, number and/or size of fruits or other offspring of the living system; v concentration of ingredients in excrements and/or exhausts; vi ability, type and/or direction of movement; vii concentration of ingredients that are formed during the growth viii intake of nutrients of the living systems
4. Method according to one of the preceding claims, characterized in that the data set further comprises the growing condition of each living system, especially the growing condition at the time of the determination of said parameter.
5. Method according to one of the preceding claims, characterized in that the generation of the digital twin for said living system is further based on said multiple data set representing different growth stages of said one or more living systems of a different type.
6. Method according to one of the preceding claims, characterized in that the generation of the digital twin for said living system is further based on said multiple data set representing at least one health condition of said one or more living systems of a different type, that can be either one or a mixture of mobility, water content, nutrition concentration, and movement of specific parts of the living system.
7. Method according to one of the preceding claims characterized in that the method comprises the use, relying on, and/or referring to at least one potentially affecting the process of the growth, development, sprouting, differentiation, propagation, cultivation, multiplication, and/or reproduction of such a living system.
8. Method according to one of the preceding claims characterized in that the method comprises at least one input factor and at least one response, feedback, measurable outcome, and/or vital signal.
9. Method according to one of the preceding claims characterized in that Step D and/or Step E further comprises analysis, information gathering, logic application, trends, and/or coincidences to elucidate potential and/or specific biomarkers and/or digital biomarkers.
10. Method according to one of the preceding claims characterized in that the analysis in Step D and/or E is performed by means of statistical analysis, neuronal networks, machine learning, artificial analysis, regression analysis, and/or trending.
11 . Method according to one of the preceding claims characterized in that Step D and/or Step E further comprise prospective simulation, retrospective simulation, extrapolation, interpolation, targeting, minimizing, maximizing, ranging, and/or multi-variant simulation.

Description

METHOD TO BUILD A DIGITAL TWIN The current invention relates to a method to build a digital twin. The evolution of the digital twin technology allowed the next industrial revolution, known as Industry 4.0. Physical objects, transferred as CAD or similar designs into the digital world, paired with movement, material, and logic data allow for simulative use and in-silico experimentation without the necessity to construct those physical objects. As the application of such objects is widely used in engineering, aviation, robotics, buildings/constructions, and agriculture, little to none of this approach is used holistically on living plants. W02020201567 and WO2020224779A1 describe a cell-based digital twin system to predict cell responses on tested compounds and thus, isolate a living cell from the organism. Other digital twin solutions used in medicine are described in CN115005981 that use a human digital twin approach for surgical path planning, again isolating a specific aspect from the entire living organism. WO2022236064A2 does describe a digital twin system for an industrial environment with sensor readings and data but does not apply the digital twin to a living organism, such as a plant. The publications by Westermann, L. (Bringen Baume mehr Ertrag, 15. Juni 2023) and Verdouw, Cor (twins in smart farming, agricultural systems, Vol. 189, 2021 ) describe the use of a digital twin for precision agriculture using prediction of crop, crop time and other states of the plant in the environment, i.e. in an uncontrolled environment. All of these systems won’t allow a holistic approach to the digital twin model creation of a living system from seed, seedling or egg to the harvest-ready living system, with the subsequent derivation of in-depth knowledge on system reactions and feedback to further utilize such a digital twin model for biosynthetic pathway elucidation, digital biomarker, optimal conditions of breeding, cultivation, and/or health sustaining of a living organism, such as a plant. Moreover, none of these systems uses multi-spectrum detections, e.g. UV, VIS, NIR, IR, and/or holistic analytics such as multi-spectrum paired with gene sequencing (genomics), transcriptions (transcriptom ics) or other omics. The object of the current invention is the provision of a method to build a digital twin for a living system to simulate the growth of said living system under different conditions in a controlled environment, including hermetically closed environments. The current invention achieves this object with a method with the features of claim 1 . A method according to the invention is provided for the building of a digital twin for a living system and for the use of said digital twin. Said living system is an organism, and could preferably be a plant, an algae, a water-borne animal, a fungus, and/or a worm. Said organisms can be cultivated in a small space with high efficiency. Humans and/or larger animals are thus not in the focus of the current method. The method comprises at least the following steps: Step A: Provision of a number of multiple living systems of the same type, i.e. genus and/or species; Said number might be at least 4 living systems of the same genus, such as a specific genus of a plant but optionally of different species. More preferably said number might be at least 16 living systems of the same genus. Said living systems might be clones from the same plant or different species. Step B: Growing the living systems of the same genus under different controlled growing conditions; Said controlled conditions might be the simulation of a desert, rainforest, normal climate, or the like. Said controlled growing conditions might comprise a different temperature, sunlight, wavelength, type of earth, amount of moisture, such as rain or fog, and further conditions, to simulate natural habitats. Step C: Determination of one or more parameters of the living system that is or are subjected to a change over the time period of the growth; wherein multiple data sets are determined and stored, each data set has said parameter or parameters and a time stamp of the time of determination; Said parameter might be the movement of a water-borne animal, the height of a plant over a period of growth, the mass growth of the plant over a time period of growth, the growth of substances, such as minerals and/or vitamins and/or active ingredients, over a period of growth and the degradation of substances over a period of growth. Said substances might be inside the living system, such as a plant, such as an ingredient or the degradation of nutrients outside or inside the living system. Each parameter is stored with a time stamp. Thus, the parameter is stored together with the specific time within the time period of growth to allow for time-series analysis. Step D: Generation of a digital twin for said living system based on said multiple data set representing different growth or health stages of said living system. The