Globalfoundries Inc.

Sachcontrol Gmbh

Solera Holdings, Inc.

Andrey Lutich

Lars Rietzschel

Mihran Shahinian

A method includes obtaining an image of a damaged vehicle and a three-dimensional (3D) vehicle model associated with the damaged vehicle. The method also includes identifying one or more damage markers that indicate damage to the damaged vehicle based on the image. The method further includes identifying a pose of the damaged vehicle within the image. In addition, the method includes mapping the one or more damage markers onto the 3D vehicle model based on the identified pose, where the one or more mapped damage markers identify one or more locations and dimensions of the damage to the damaged vehicle on the 3D vehicle model. In some cases, one or more trained machine learning models may be used to identify the one or more damage markers, identify the pose of the damaged vehicle within the image, and/or map the one or more damage markers onto the 3D vehicle model.

Transfer of damage markers from images to 3D vehicle models for damage assessment

In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device.

Repair estimation based on images

In one embodiment, a method includes receiving an invoice. The invoice includes invoice positions and each of the invoice positions includes a position text. The method also includes converting each word and number of the position text of each invoice position to a word embedding vector, summing the word embedding vectors for each invoice position to generate a word vector for each invoice position, and concatenating the word vector and a number vector of each invoice position to generate a position vector for each invoice position. The method further includes generating a first combined position vector for a first invoice position, generating a second combined position vector for a second invoice position, generating an invoice vector by summing the first and second combined position vectors, comparing the invoice vector to a fraud detection parameter, and determining whether the invoice is indicative of fraud based on the comparison.

Information security system for fraud detection

A known pattern area (of known shapes) is aligned with a first area of an IC design (by overlaying and centering the known pattern area on the first area). The first area is compared to the known pattern area to identify dissimilarities between shapes in the first area and the known shapes in the known pattern area as unweighted shapes. The unweighted shapes are weighted (based on the location of the unweighted shapes relative to a weighting zone pattern) to produce weighted shapes; and a dissimilarity measure between the first area and the known pattern area is calculated by summing the weighted shapes. A determination of whether the first area matches the known pattern area is made (based on whether the dissimilarity measure exceeds a threshold) and the same is output.

Matching IC design patterns using weighted XOR density

Embodiments of the disclosure provide a method including: identifying a target feature in an integrated circuit (IC) layout not represented in a library, the library including a plurality of sub-resolution assist feature (SRAF) usefulness maps corresponding to a plurality of features and SRAFs in the IC layout; generating a usefulness map for the target feature with an artificial neural network (ANN), the generating being based on the target feature and the plurality of SRAF usefulness maps in the library; adding the target feature and the generated usefulness map to the library; selecting an SRAF insertion site for the target feature based on the generated usefulness map; and inserting an SRAF for the target feature into the IC layout at the selected SRAF insertion site.

SRAF insertion with artificial neural network

An illustrative method includes providing a layout of at least a portion of a photomask, the layout comprising a plurality of target features, each target feature having a shape in accordance with a corresponding one of at least one target shape, for each of the target shapes, providing a local map specifying a respective value of a local sub-resolution assist feature (SRAF) usefulness for each of a plurality of positions relative to the target shape, generating a global usefulness map specifying a respective global SRAF usefulness for each of the plurality of positions relative to at least a portion of the photomask on the basis of the assignment of the values of the local SRAF usefulness.

Generating manufacturable sub-resolution assist feature shapes from a usefulness map

A method includes providing a layout of a portion of a photomask. The layout includes a plurality of target features having a shape in accordance with a corresponding one of a target shape. For each of the target shapes, a local map specifying a respective value of a local sub-resolution assist feature (SRAF) usefulness for each of a plurality of positions relative to the target shape is provided. For each of the target features, an assignment of a part of the values of the local SRAF usefulness of the local map for the target shape corresponding to a target feature to a position relative to the portion of the photomask is provided. A global map specifying a global SRAF usefulness for each of the positions relative to the portion of the photomask is provided on the basis of the assignment of the values of the local SRAF usefulness.

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Dresden, Germany

Andrey Lutich