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公开(公告)号：US20220365834A1
公开(公告)日：2022-11-17
申请号：US17241675
申请日：2021-04-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Usama Khurshid HAQ , Muskan KUKREJA
IPC: G06F11/07
Abstract: 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.
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公开(公告)号：US20220156486A1
公开(公告)日：2022-05-19
申请号：US17098285
申请日：2020-11-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Gowtham GANESAN , Biyi FANG , Sheng YI
Abstract: 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.
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公开(公告)号：US20200293770A1
公开(公告)日：2020-09-17
申请号：US16355702
申请日：2019-03-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Yixin SHI , Sheng YI
Abstract: 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.
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公开(公告)号：US20240403643A1
公开(公告)日：2024-12-05
申请号：US18325379
申请日：2023-05-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Tianyu DING , Luming LIANG , Ilya Dmitriyevich ZHARKOV
IPC: G06N3/082 , G06N3/0464
Abstract: Technologies described herein relate to training and compressing a computer-implemented model. To that end, an untrained computer-implemented model is obtained, where the untrained computer-implemented model is to be trained and compressed. The untrained computer-implemented model includes an operator that comprises a structure. Further, training data is obtained, where the training data is to be employed to train the computer-implemented model. Upon receipt of a request from a user, the untrained computer-implemented model is trained and compressed based upon the training data. The untrained computer-implemented model is trained and compressed without further input from the user, such that a trained and compressed computer-implemented model is generated. The trained and compressed model does not include the structure.
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公开(公告)号：US20230143969A1
公开(公告)日：2023-05-11
申请号：US17522608
申请日：2021-11-09
Applicant: Microsoft Technology Licensing, LLC
Inventor： Biyi FANG , Sheng YI , Tianyi CHEN
CPC classification number: G06K9/00416 , G06K9/00422 , G06K9/00442 , G06K9/4638
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20200302163A1
公开(公告)日：2020-09-24
申请号：US16360006
申请日：2019-03-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yixin SHI , Aman ORAZAEV , Tianyi CHEN , Sheng YI
Abstract: 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.
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公开(公告)号：US20250166126A1
公开(公告)日：2025-05-22
申请号：US18513081
申请日：2023-11-17
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyu DING , Jinxin ZHOU , Tianyi CHEN , Ilya Dmitriyevich ZHARKOV , Luming LIANG
Abstract: The technology described herein provides an improved training framework for a diffusion model used for a super resolution (SR) task. In particular, the technology provides diffusion rectification to correct a training-sampling discrepancy inherent in current training methods. The technology also provides estimation-adaptation. The diffusion rectification portion of the technology uses an estimated HR image, rather than a ground truth HR image as the seed to the forward process. This improves model performance issues caused by a training-sampling discrepancy. The training-sampling discrepancy occurs because the training and sampling processes do not use the same data. The estimation adaption strategy injects ground truth to the plurality of noisy images to reduce the training-estimation error in the images. In an aspect, a different amount of ground truth is injected into training images based on the training image's location in the Markov chain.
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公开(公告)号：US20240428431A1
公开(公告)日：2024-12-26
申请号：US18339211
申请日：2023-06-21
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Tianyu DING , Luming LIANG , Ilya Dmitriyevich ZHARKOV
Abstract: A method performed by a processor of a computing system is described herein, where the method includes obtaining an image that includes an object having a shape, where a boundary of the shape of the object in the digital image is labeled in the digital image. The method also includes computing an encoding for the shape, where computing the encoding for the shape includes partitioning the shape into multiple partitions. Computing the encoding for the shape further includes, for the multiple partitions, computing angle-based contour descriptors that represent boundaries of the partitions, where the encoding for the shape of the object is based upon the angle-based contour descriptors.
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公开(公告)号：US20230419111A1
公开(公告)日：2023-12-28
申请号：US18458709
申请日：2023-08-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Sheng YI , Yixin SHI , Xiao TU
Abstract: 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.
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公开(公告)号：US20220392265A1
公开(公告)日：2022-12-08
申请号：US17820036
申请日：2022-08-16
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tianyi CHEN , Sheng YI
Abstract: 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).
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