Abstract: A method for machine-learning a function configured constraints prediction for a sketch representing at least a portion of a mechanical part. The method includes providing a training dataset of training examples, each including a graph representing a sketch. The graph includes object nodes each representing a geometric object, geometry nodes each representing a geometric property, topological edges each representing a topological link, geometrical edges each connecting a geometry node to one object node, and constraint edges each representing a constraint and labeled with a label of a predefined set of labels each representing a constraint of a predefined constraints set. The method includes training the function to take as input a graph representing a sketch and to output constraints each of a constraint edge of the graph and each belonging to the predefined constraints set.

Abstract: A computer-implemented method for forming a dataset of medical images. The dataset is configured to be used for training a model. The trained model is thereby configured to output a prediction from an input medical image. The dataset-forming method comprises obtaining a first set of medical images. The first set consists of annotated medical images. The first set is unbalanced with respect to a meta-property relative to each medical image. The dataset-forming method further comprises obtaining a second set of medical images. The second set comprises unannotated medical images. The dataset-forming method further comprises outputting a third set of medical images. The third set comprises the first set and unannotated medical images of the second set. The third set is balanced with respect to the meta-property. The dataset-forming method is an improved solution for forming a dataset of medical images.

Abstract: A stamping part design method including obtaining a first B-rep model having a 3D boundary curve formed by boundary-curve edges and obtaining a first and second value representing a g1-continuity and a g2-continuity extrapolation length requirement. The method includes determining a second B-Rep model comprising an extrapolation of the first B-Rep model and presenting at least a g2-continuity over a length of the extrapolation patches equal to the second value and then at least a g1-continuity over a length of the extrapolation patches equal to the first value. The determination of the second B-Rep model includes partitioning the boundary-curve edges into first groups of consecutive first boundary-curve edges, and second groups of consecutive second boundary-curve edges and applying a surface-extension operator to each first group and filling the gaps. This forms an improved solution of stamping part design.

Abstract: A computer-implemented method for generating, from one or more medical images each of an organ of a patient, a medical report compliant with a predefined set of medical report rules. The method includes, automatically, determining positions and diameters of one or more lesions in the one or more medical images. The method further including, automatically, determining, based on determined positions and diameters of the lesions, a list of target lesions in accordance with the predefined set of medical report rules. The method further includes, automatically, generating the medical report based on the determined target lesions and their positions and diameters.

Abstract: A computer-implemented method for processing a medical image of a body of a patient on a horizontal support. The method includes obtaining the medical image. The medical image includes voxels. Each voxel is associated with a value. The value represents tissue density at the position represented by the voxel. The method further includes discarding each voxel of the medical image associated with a value smaller than a predefined threshold. The discarding splits the image into connected components separated by discarded voxels. The method further includes identifying, among the connected components, a connected component having a largest number of voxels. The method further includes computing a curve separating the identified connected component from the remaining part of the medical image. The voxels bounded by the curve correspond to the body of the patient.

Abstract: A computer-implemented method for material extrusion detection in a portion of a mechanical part having a distribution of material. The method includes obtaining a CAD 3D model of the mechanical part including a skin portion representing an outer surface of the portion of the mechanical part and an extrusion axis. The method further includes computing a ratio of an area of an orthogonal projection of the skin portion over an area of the skin portion. The method further includes determining whether or not the distribution of material is arranged as an extrusion based on the ratio and a ratio threshold. The outer surface is determined to be an extrusion surface when the ratio is lower than the ratio threshold. This forms an improved solution for processing a CAD 3D model of a mechanical part.

Abstract: A computer-implemented method for computing a score representing a percentage of occurrence of a clinical event. The method includes obtaining a database including a list of P characteristics within a vector X, associated with the clinical event. The method includes obtaining, for a given patient, the modalities corresponding to a subset of the characteristics in the list. The method includes extracting, from the database, the modality weight associated with each modality obtained for the patient. The method includes computing the score for the given patient by summing the extracted modalities weights, using the formula Scorepatient=?pj?X*?(pj). The method includes computing a dispersion range associated with the computed score. Such a method forms an improved solution for computing a score representing a percentage of occurrence of a clinical event for a patient.

Abstract: A computer-implemented method for machine-learning a neural network having a Spike Neural Network for predicting a time occurrence of a clinical event. The method includes providing a training dataset of training examples. Each training example includes one or more time series of values of respective physiological measures of a patient over a past time period and a corresponding ground truth of a time occurrence of the clinical event. The method further includes training the neural network based on the training dataset. The neural network is trained for taking as input one or more time series of values of the respective physiological measures of a patient over a past time period and for outputting a prediction of probabilities over a time period of the occurrence of the clinical event. The method is an improved solution for predicting a time occurrence of a clinical event.

Abstract: A computer-implemented method for encoding a time series of values of a physiological measure of a patient. The method includes obtaining the time series of values of the physiological measure of the patient. The method further includes, for each time point of the time series of values, obtaining a slope coefficient of the time series of values at the time point. The obtaining a slope coefficient is based on the time point, a predetermined number of previous time points, and a predetermined number of next time points. The method further includes, for each time point of the time series of values, comparing the slope coefficient to a reference slope coefficient. The method is an improved solution for encoding a time series of values of a physiological measure of a patient.

Abstract: A computer implemented method for designing a 3D modeled object representing a manufacturing product. The method includes obtaining a base mesh representing the 3D modeled object, selecting one or more connected edges of the base mesh, subdividing the base mesh based on the selected edges by obtaining a bevel pattern area over the selected path. The method obtains, for at least one of the two endpoints of the path, a transition area by grouping all faces sharing the at least one of the two endpoints of the path, except those of the computed bevel pattern area. The method re-meshes the transition area by obtaining a transition vertex located in the transition area and computing an edge connecting each vertex of the pair of vertices with the obtained transition vertex. The method outputs the subdivided base mesh. This improves the design of a 3D modeled object.

Abstract: A method for generation of a sequence of synthetic ultrasound images of an organ. The method includes providing a video diffusion model trained to take as input a sequence of semantically labelled 2D representations of the organ and a corresponding sequence of noise images, to generate a corresponding denoised sequence of ultrasound images respecting the labels. The method includes providing several consecutive sequences of labelled 2D representations of the organ and corresponding consecutive sequences of noise images, iteratively applying the video diffusion model to each couple formed by one said consecutive sequence and one corresponding noise images sequence, including, at each iteration, using, for each denoising step of the backward process of the video diffusion model, a part of the denoised synthetic ultrasound images generated at the previous iteration for that denoising step in replacement of a part of images of the given couple for that denoising step.

Abstract: A computer-implemented method for predicting new occurrences of an event of a physical system. The method includes providing a first set of past events of the physical system, each past event comprising several attributes, providing a signature for each past event of the first set, providing a new event comprising several attributes, computing a signature of the new event, computing a similarity measure between the signature of the new event and each signature of each past event of the first set, determining the past events closest to the new event according to the similarity measures thereby forming a second set of past events, computing a score of relevance for each attribute of the second set, providing a set of attributes by selecting the attributes having the greater scores of relevance.

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

Abstract: A computer-implemented method for designing at least one 3D model in a 3D scene including receiving a user's 2D sketch and displaying it on a plane, the 2D sketch representing a view of the 3D model to be designed, inferring a 3D primitive based on the 2D sketch, the 3D primitive being oriented and positioned in the 3D scene to match the view, performing a 2D projection of the 3D primitive on the plane, and fitting the 2D projection onto the 2D sketch.

Abstract: A computer-implemented method for updating a virtual RDF graph database, the virtual database comprising tuples including a file storage having a durability property of ACID property and guarantying consistent write, providing a virtual RDF graph database including a first RDF graph database updatable by streams and a second read-only RDF graph database stored on the file storage and updatable by batches, a catalog for storing metadata describing the second read-only RDF graph database on the file storage, the catalog being compliant with ACID and obtaining a stream of tuples and a batch of tuples, applying the stream of tuples on the first RDF graph database, applying the batch of tuples on the second RDF graph database by computing a snapshot of the second RDF graph database, including the batch of tuples, the computed snapshot and registering the computed snapshot in the catalog.

Abstract: 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 includes a vertex neural network, an edge neural network, and a face neural network. The edge and face neural network each further include a topological model and a geometrical model. Each of the neural networks 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.

Abstract: A computer-implemented method estimates a mass distribution of a physical product comprising a plurality of product parts. The method: a) creates a data structure comprising, for each part, data representing position and orientation of the part, its mass of the part, preferably its tolerance associated to each mass of the part, and its geometry in voxel format; b) decomposes a digital model of the physical product into a plurality of parallel slices (SL1); c) for each slice, uses the data structure for identifying a set of product parts of the data structure overlapping with the slice and determining a respective overlap rate; and d) attributes to each slice a mass value corresponding to a sum of the masses of all product parts overlapping with the slice, weighted by the respective overlap rates. A computer program product, non-transitory computer-readable data-storage medium, a computer system and a Computer Aided Design (CAD) Systems carry out such a method.

Abstract: 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 includes by a user graphically interacting with the 3D representation 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 includes, 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 includes, 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.

Abstract: A machine-learning method for learning a function for predicting a next feature in a feature tree. The method includes obtaining a dataset of examples each including a graph representing at least a part of a feature tree. The graph includes 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 includes edges each connecting nodes and represents 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 includes 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.

Abstract: 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 includes providing specifications of the gap. The specifications include a radius of a connection between the surface and flanges of the gap. The specifications further include a profile of the gap. The specifications further include a shape defining the gap on the surface. The method further includes sweeping the profile along the shape. The method further includes connecting the result of the sweep to the surface by applying a fillet operation. The fillet operation has as radius the provided radius.