Dassault Systèmes Patente

The disclosure notably relates to a computer-implemented method for measuring an anatomical target from a set of medical images representing the anatomical target. The method comprises: obtaining at least one medical image of the anatomical target; obtaining an annotation associated with the obtained at least one medical image of the anatomical target, the annotation comprising a measurement of the anatomical target. The method further comprises obtaining a trained model configured for outputting, based on the obtained annotation, a segmentation mask of the anatomical target represented on the obtained at least one medical image. The method comprises applying the obtained trained model to the obtained at least one medical image and the obtained annotation. Thereby, the method identifies a segmentation mask of the anatomical target represented on the obtained at least one medical image. The method comprises computing a measurement of the anatomical target on the identified segmentation mask.

The disclosure notably relates to a computer-implemented method for annotating a set of medical images of at least one anatomical target of a same patient. The method comprises: obtaining at least one query from the set of medical images, each query being an unannotated 2D slice of a medical image; obtaining a support from the set of medical images, the support comprising a set of pairs, each pair comprising a 2D slice of the medical image and a respective 2D annotation; obtaining a pre-trained few-shot model being configured: for taking as input at least one query and a support; and for outputting, based on the inputted support, a 2D annotation associated with the inputted at least one query. The method also comprises predicting, by using the pre-trained few-shot model, a 2D annotation associated with the obtained at least one query.

The invention relates to a method for updating an exploded path (25) within a 3D scene (11), comprising:- displaying a 3D scene (11) provided with a three-axis system (15) and including an exploded path (25) extending from a starting point (SP) to a finishing point (FP), the exploded path (25) being a connected series of at least three line segments (25A, 25B, 25C, 25D) each aligned with one of the axes (X, Y, Z) of the three-axis system (15) according to a periodic direction pattern;and, upon instruction to apply a translation to a line segment (25C):- applying the translation to this line segment (25C) and to the following or preceding line segment (25B, 25D) to preserve the periodic direction pattern, and, if necessary,- adding a linking line segment to relink the exploded path (25) to the starting or finishing point (SP, FP).

The disclosure notably relates to a computer-implemented method of machine-learning, for learning a generative function configured to generate a B-rep given a conditioning signal representing a geometry. The generative function comprises a vertex neural network, an edge neural network, and a face neural network. The edge and face neural network each further comprise a topological model and a geometrical model. Each of the neural network is configured to generate the respective parts of the B-rep (vertices, edges, and faces) respecting the conditioning signal. This provides an improved solution for designing B-reps given a conditioning signal representing a geometry.

The disclosure notably relates to a computer-implemented method for forming a gap on a surface. The surface represents a surface portion of a product to be manufactured. The method comprises providing specifications of the gap. The specifications comprise a radius of a connection between the surface and flanges of the gap. The specifications further comprise a profile of the gap. The specifications further comprise a shape defining the gap on the surface. The method further comprises sweeping the profile along the shape. The method further comprises connecting the result of the sweep to the surface by applying a fillet operation. The fillet operation has as radius the provided radius.

The disclosure relates to a machine-learning method for learning a function configured for predicting a next feature in a feature tree. The method comprises providing a dataset of examples each comprising a graph representing at least a part of a feature tree. The graph comprises nodes each representing a feature and each labeled with a label of a set of labels each indicating a feature type of a predetermined set. The graph comprises edges each connecting nodes and representing a parent-child relationship between the features represented by the nodes. The graph comprises ground truth data indicating a next feature according to one or more selected features each corresponding to a graph node. The method further comprises training the function to take as input a graph representing at least a part of a feature tree and to output a prediction of one or more next features in the feature tree.

The invention relates to a method for previewing a 3D scene video having duration and frame rate comprising: a) receiving (200) at least one set of rendering parameter values of the 3D scene video, each value being associated with a respective timestamp, a respective interpolation function being associated with each pair of values having consecutive timestamps; b) determining (210) a number N of timecodes, where N is an integer based on the product of the duration and frame rate. The method further comprises iteratively, in a non-linear rendering order that is not chronological for at least some of the N timecodes: c) computing (250) a respective value for each set of rendering parameter values by using the interpolation function associated with the pair of values whose respective timestamps bound the current timecode, and d) rendering (280) a frame based on each rendering parameter value determined in operation c).

The disclosure notably relates to a computer-implemented method of designing a 3D modeled object representing a mechanical product. The 3D modeled object has a feature tree and an original value of a set of semantic parameters. The method comprises by a user graphically interacting with the 3D representation, and in particular, graphically selecting a semantic point of an outer surface of the 3D modeled object and graphically selecting one or more target 3D positions of the semantic point. The method further comprises, automatically, iteratively modifying the value of the set of semantic parameters. Thereby the method obtains an updated value of the set of semantic parameters. The method further comprises, automatically by the CAD system, updating the 3D representation of the 3D modeled object based on the updated value of the set of semantic parameters. This provides an improved solution for updating 3D modeled objects based on graphical interactions.

The disclosure notably relates to a computer-implemented data structure for storing RDF dataset comprising a set of RDF quads. The data structure comprises a first data structure. The first data structure comprises a first non-graph database with ACID properties for temporarily storing one or more modifications to be applied to the RDF dataset. The data structure also comprises a second data structure. The second data structure comprises a second non-graph database with ACID properties for storing a state of the RDF dataset as a binary representation. The binary representation is stored as binary data in a native structure of the second non-graph database.

The disclosure relates to a method for executing linear recursive queries. The method comprises obtaining a first part of the query that defines as input one or more initial conditions of query elements in a second part of the query on the RDF graph database. The method also comprises obtaining at least two clauses specifying that a second part of the query will be executed recursively. The first clause defines how an output of an execution of the second part of the query is used as an input of a next execution of the second part of the query. The second clause defines the second part of the query and comprises the query elements describing how the output of an execution is queried using (i) the input of the one or more initial conditions for an initial execution or (ii) the output of a previous execution for the next execution.

The disclosure notably relates to a computer-implemented data structure representing an adjacency matrix associated with a predicate of a set of quads of an RDF graph database, wherein the adjacency matrix being partitioned into a grid of rectangular partitions, the partitions having a same dimension; the adjacency matrix comprising a set of adjacency sub-matrices, each adjacency sub-matrix corresponding to a respective partition of the adjacency matrix; each adjacency sub-matrix being identified by its respective position in the grid.

The disclosure notably relates to a computer-implemented method for predicting a cardiovascular behavior of a newborn to birth from his/her fetus physiological parameters. The method comprises obtaining physiological parameters of the fetus, obtaining a non-calibrated surrogate cardiovascular model modeling a cardiovascular system of the fetus and modelling at least one physiological change triggered by the birth, calibrating the non-calibrated surrogate cardiovascular model with a data assimilation algorithm using the obtained physiological parameters of the fetus on the non-calibrated surrogate cardiovascular model, thereby obtaining a calibrated surrogate cardiovascular model of the fetus, and predicting the cardiovascular behavior of the newborn by triggering the at least one physiological change of the calibrated surrogate cardiovascular model of the fetus, thereby obtaining a calibrated surrogate cardiovascular model of the newborn.

The disclosure notably relates to a computer-implemented method of machine-learning. The method comprises obtaining a dataset of ground truth 3D layouts. The machine-learning method further comprises obtaining a probability distribution of noise levels. The machine-learning method also comprises, for each ground truth 3D layout, obtaining a respective perturbed 3D layout. The machine-learning method moreover comprises training a function. The function is configured for taking an input 3D layout and a given noise level, and for predicting an output 3D layout. The training is performed over the dataset based on a loss which penalizes a dissimilarity metric between each ground truth 3D layout and a respective predicted 3D layout.

The disclosure notably relates to a computer-implemented method of using a machine-learning function preconfigured for taking an input 3D layout and a given noise level, and for predicting an output 3D layout. The function is preconfigured with a conditioning drop-out with respect to at least one layout parameter. The method further comprises obtaining a set of conditioning inputs and, for each conditioning input, determining one or more conditioning candidate 3D layouts and determining a plurality of perturbed conditioning candidate 3D layouts. The method further comprises, applying the preconfigured function to each perturbed conditioning candidate, in which the one layout parameter is dropped out, computing reconstruction errors, and averaging the reconstruction errors, thereby obtaining a score. This forms an improved solution for predicting 3D layouts.

The disclosure notably relates to a computer-implemented method of machine-learning for assembling mechanical parts, based on a mating score and a mating axis. The method comprises providing a dataset of pairs of B-Reps, each pair comprising at least one B-Rep representing an assembly of mechanical parts, being labelled with mating compatibility data and, if the B-Reps of the pair are compatible according to the mating compatibility data, mating axis compatibility data. The method also comprises training a neural network based on the dataset, configured for taking as input a pair of B-Reps. The neural network also outputs a mating score of a pair of single embeddings, each single embedding representing a B-Rep, the mating score representing a score of mating compatibility between the mechanical parts represented by the pair, and if the B-Reps are compatible according to the mating score, data defining a mating axis.

The invention notably relates to a computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method comprises generating one or more solid CAD feature includes each representing a respective 3D shape. The method also comprises, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

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<div notation="docdba" type="abstract" xml:base="/api/emi/section/d225e46b-e181-4fb3-a1b0-64f6dd337976/image" xml:lang="en"><p>The invention notably relates to a computer-implemented method for simulating evolution of a tumor associated to an oncogene. The method comprises providing (S10) a plurality of pieces of data, each corresponding to a given cell of the tumor, and includes a degree of activation of the oncogene in the given cell. The method further comprises providing (S20) a model configured to take an input piece of data and to output an information on proliferation of the respective given cell corresponding to the input piece of data. The information on proliferation depends on the degree of activation of the oncogene. The method further comprises running (S30) the model on one or more pieces of data of the plurality of pieces of data and updating (S40) the plurality of pieces of data based on the result of the running (S30). Such a method improves the simulation of the evolution of a tumor.</p></div>

The disclosure relates to a method for generating a CAD feature tree from a discrete geometrical representation of apart. The method comprises providing the representation. The method comprises providing a set of CAD features. The method comprises optimizing an objective function rewarding a fitting of the discrete geometrical representation by a candidate sequence of CAD features and penalizing a complexity of the candidate sequence. The complexity of the candidate sequence is a function of the candidate sequence that increases when adding a feature to the candidate sequence. The optimization comprises determining one or more sequences of CAD features from the set of CAD features, including performing a depth-first search in a tree of candidate sequences of CAD features based on rewarding a fitting of the discrete geometrical representation by a candidate sequence and on penalizing a complexity of a candidate sequence.

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The invention notably relates to a computer-implemented method for designing a three-dimensional (3D) finite element mesh of a 3D part that comprises a lattice structure. The method comprises providing a solid representation of the 3D part, providing a regular tiling of cells, each cell comprising a finite element mesh, superposing the regular tiling of cells with the solid representation of the 3D part, partitioning (S40) the cells into two groups. A first group of cells, each in contact with the solid representation of the 3D part, and a second group of cells, none in contact with the solid representation of the 3D part. The method also comprises finite element meshing a boundary of the solid representation of the 3D part, extracting a boundary finite element mesh of the first group of cells, computing a Boolean union of the finite element mesh of the boundary of the solid representation of the 3D part and of the extracted boundary finite element mesh of the first group of cells, finite element meshing a volume of the computed Boolean union and merging the finite element meshes of meshed volume of the computed Boolean union and the cells of the second group of cells.

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