A computer-implemented method for learning a neural network configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape, the editable feature tree comprising a tree arrangement of geometrical operations applied to leaf geometrical shapes, the 3D shape representing the geometry of a product to be manufactured in the real world, the method comprising: - providing a dataset including discrete geometrical representations each of a respective 3D shape; - providing a candidate set of leaf geometrical shapes; and - learning the neural network based on the dataset and on the candidate set; the method being characterized in that the candidate set comprises at least one continuous subset of leaf geometrical shapes, each element of said candidate set being available for being potentially inferred during inference, each candidate leaf geometrical shape of the subset being obtainable by a continuous deformation of another candidate leaf geometrical shape of the subset, such that each intermediate state of the continuous deformation is itself a candidate leaf geometrical shape of the subset, wherein the candidate set comprises a set product between: - a discrete set of primitive shape types, and - for each primitive shape type, a respective discrete set of one or more parameter domains each of a respective continuous parameter, each parameter domain having respective parameters values of the respective continuous parameter, each primitive shape type forming, with a respective parameter value for each of the respective discrete set of one or more parameter domains, a respective element of the candidate set, wherein, for each of one or more primitive shape types, the one or more respective continuous parameters comprise one or more dimensional parameters and one or more positioning parameters, wherein the discrete set of primitive shape types comprises a cuboid type, a sphere type, one or more cylinder types, and one or more prism types, wherein the neural network comprises recurrent neural network cells, each RNN cell outputting at a respective time step ( t), respective first data for inference of a respective primitive shape type and respective second data for inference of a respective parameter value for each of the respective discrete set of one or more parameter domains, wherein the neural network is adapted to be used as part of a process of designing a 3D modeled object, the process of designing the 3D modeled object being included in a process of manufacturing a product corresponding to the 3D modeled object. - providing a dataset including discrete geometrical representations each of a respective 3D shape; - providing a candidate set of leaf geometrical shapes; and - learning the neural network based on the dataset and on the candidate set; the method being characterized in that the candidate set comprises at least one continuous subset of leaf geometrical shapes, each element of said candidate set being available for being potentially inferred during inference, each candidate leaf geometrical shape of the subset being obtainable by a continuous deformation of another candidate leaf geometrical shape of the subset, such that each intermediate state of the continuous deformation is itself a candidate leaf geometrical shape of the subset, wherein the candidate set comprises a set product between: - a discrete set of primitive shape types, and - for each primitive shape type, a respective discrete set of one or more parameter domains each of a respective continuous parameter, each parameter domain having respective parameters values of the respective continuous parameter, - a discrete set of primitive shape types, and - for each primitive shape type, a respective discrete set of one or more parameter domains each of a respective continuous parameter, each parameter domain having respective parameters values of the respective continuous parameter, each primitive shape type forming, with a respective parameter value for each of the respective discrete set of one or more parameter domains, a respective element of the candidate set, wherein, for each of one or more primitive shape types, the one or more respective continuous parameters comprise one or more dimensional parameters and one or more positioning parameters, wherein the discrete set of primitive shape types comprises a cuboid type, a sphere type, one or more cylinder types, and one or more prism types, wherein the neural network comprises recurrent neural network cells, each RNN cell outputting at a respective time step ( t), respective first data for inference of a respective primitive shape type and respective second data for inference of a respective parameter value for each of the respective discrete set of one or more parameter domains, wherein the neural network is adapted to be used as part of a process of designing a 3D modeled object, the process of designing the 3D modeled object being included in a process of manufacturing a product corresponding to the 3D modeled object.