Microsoft Technology Licensing, LLC

Tianyi Chen

Sheng Yi

Yixin Shi

Xiao Tu

Biyi Fang

Reed L Townsend

Muskan Kukreja

Kyle Thomas Beck

Aman Orazaev

Megan L Fu

Gowtham Ganesan

Usama Khurshid Haq

Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).

Signature verification

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Model compression by sparsity-inducing regularization optimization

Model compression by sparsity&#x2014;inducing regularization optimization

Non-limiting examples of systems, methods, and devices for automatic triaging of diagnostic failures for heterogeneous groups of tenants of a Software-as-a-Service, multi-tenant environment are disclosed herein. In an implementation, telemetry data for the heterogeneous groups of tenants is analyzed to classify individual tenant failures and detect the health status of the individual tenant. Tenant failures and/or tenant health statuses are filtered according to a threshold level. Anomalies having metrics that meet or exceed the threshold level are further analyzed to determine their priority (e.g., to a specific tenant). If the anomalies are known, then an existing entry for the anomaly is tagged and its priority may be changed. If the anomalies are unknown, then an entry is generated for the anomaly and prioritized. Tenants may be notified of a detected anomaly and may provide feedback. The feedback may be used to update triaging models.

Automatic triaging of diagnostics failures

Systems and methods for performing spatial line grouping on digital ink stokes. The system includes an electronic processor configured to access a set of hypothetical lines in an electronic document and determine a set of hypothetical line pairings. The electronic processor is also configured to determine, via a gradient boosting tree model, a merge confidence score for each hypothetical line pairing and compare a first merge confidence score with a merge threshold. The first merge confidence score is associated with a first hypothetical line and a first neighboring hypothetical line. The electronic processor is also configured to, in response to the first merge confidence score satisfying the merge threshold, merge the first hypothetical line and the first neighboring hypothetical line to form a first line grouping. The electronic processor is also configured to perform a digital ink stroke analysis on the electronic document based on the first line grouping.

Gradient boosting tree-based spatial line grouping on digital ink strokes

A data processing system for performing a semantic analysis of digital ink stroke data implements obtaining the digital ink stroke data representing handwritten text, drawings, or both; analyzing the digital ink stroke data to extract path signature feature information from the digital ink stroke data; analyzing the path signature feature information using a convolutional neural network (CNN) trained to perform a pixel-level sematic analysis of the digital ink stroke data and to output a pixel segmentation map with semantic prediction information for each pixel of digital ink stroke data; analyzing the pixel segmentation map to generate stroke-level semantic information using a pixel-to-stroke conversion model; and processing the digital ink stroke data based on the stroke-level semantic information.

Semantic segmentation for stroke classification in inking application

A spatially sparse convolutional neural network (CNN) framework is introduced to that leverages high sparsity of input data to significantly reduce the computational cost of applications that employ CNNs (e.g., inking applications and others) by avoiding unnecessary floating point mathematical operations. The framework, which is compatible with parallelized operations, includes (1) a data structure for sparse tensors that both (a) reduces storage burden and (b) speeds computations; (2) a set of sparse tensor operations that accelerate convolution computations; and (3) the merging of pooling and convolutional layers. Practical applications involving handwriting recognition and/or stroke analysis demonstrate a notable reduction in storage and computational burdens.

Spatially sparse convolutional neural networks for inking applications

An ink parsing system receives ink strokes at an inking device input and render the received ink strokes into an image in a pixel space. Writing strokes are detected in the image and labeled. Pixels corresponding to the labeled writing strokes are removed from the image. Drawing strokes in the image having the removed pixels are detected using and labeled. Writing objects and drawing objects corresponding, respectively, to the labeled writing strokes and the labeled drawing strokes are output. A digital ink parsing pipeline is thereby provided having accurate ink stroke detection and segmentation.

Object detection and segmentation for inking applications

Methods, systems, and apparatuses for beautifying raw drawing data include a beautification engine having a raw data processor that receives the drawing data and identifies a set of segments in the data. A beautification director constructs a structural representation of the set of segments data that specifies a topological relationship between the segments. A single primitive beautifier identifies a segment in the structural representation to be beautified and a single primitive beautification action is applied to the segment to generate beautified raw drawing data. A multiple primitive beautifier identifies adjacent segments in the structural representation to be beautified and a multiple beautification action is applied to the adjacent segments to generate further beautified drawing data.

Growing community of inventors

Redmond, WA, United States of America

Tianyi Chen