-
公开(公告)号：US11514265B2
公开(公告)日：2022-11-29
申请号：US16584619
申请日：2019-09-26
Applicant: Microsoft Technology Licensing, LLC
Inventor： Parag Agrawal , Yan Wang , Aastha Jain , Hema Raghavan
Abstract: The disclosed embodiments provide a system for performing inference. During operation, the system obtains a graph containing nodes representing members of an online system, edges between pairs of nodes, and edge scores representing confidences in a type of relationship between the pairs of nodes. Next, the system performs a set of iterations that propagate a label for the type of relationship from a first subset of edges to remaining edges in the graph, with each iteration updating a probability of the label for an edge between a pair of nodes based on a subset of edge scores for a second subset of edges connected to one or both nodes in the pair and probabilities of the label for the second subset of edges. The system then performs one or more tasks in the online system based on the probability of the label for the edge.
-
公开(公告)号：US11010421B2
公开(公告)日：2021-05-18
申请号：US16408192
申请日：2019-05-09
Applicant: Microsoft Technology Licensing, LLC
Inventor： Ravi Theja Yada , Yan Wang , Nikita Astrakhantsev , Arun Sacheti
IPC: G06F16/532 , G06N20/20 , G06F16/54 , G06F16/56 , G06N3/08 , G06F16/583 , G06F3/0485
Abstract: A computer-implemented technique is described herein for performing an image-based search that allows a user to create a custom query image that expresses the user's search intent. The technique generates the query image based on one or more input images and/or one or more information items that describe at least one desired characteristic of the query image. The technique then submits the query image to a search engine, and, in response, receives a set of candidate images that match the query image. In one implementation, the technique constructs the query image using a decoder neural network that operates on a mixed latent variable vector. In one approach, the technique uses a generative adversarial network (GAN) to produce the decoder neural network.
-
Patent Agency Ranking
公开(公告)号：US20200285878A1
公开(公告)日：2020-09-10
申请号：US16297388
申请日：2019-03-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yan Wang , Ye Wu , Arun Sacheti
Abstract: Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.
-
公开(公告)号：US20200210743A1
公开(公告)日：2020-07-02
申请号：US16234148
申请日：2018-12-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yan Wang , Arun Sacheti , Vishal Chhabilbhai Thakkar , Surendra Srinivas Ulabala , Shloak Jain
Abstract: Representative embodiments disclose mechanisms to create a text stream from raw OCR outputs. The raw OCR output comprises a plurality of bounding boxes, each bounding box defining a region containing text which has been recognized by the OCR system. A weight matrix is calculated that comprises a weight for each pair of bounding boxes. The weight representing the probability that a pair of bounding boxes belongs to the same cluster. The bounding boxes are then clustered along the weights. The resulting clusters are first ordered using an ordering criteria. The bounding boxes within each cluster are then ordered according to a second ordering criteria. The ordered clusters and bounding boxes are then arranged into a text stream.
-
公开(公告)号：US20190180146A1
公开(公告)日：2019-06-13
申请号：US15840823
申请日：2017-12-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Arun Sacheti , FNU Yokesh Kumar , Saurajit Mukherjee , Nikesh Srivastava , Yan Wang , Kuang-Huei Lee , Surendra Ulabala
Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.
-
公开(公告)号：US11769048B2
公开(公告)日：2023-09-26
申请号：US17021779
申请日：2020-09-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Parag Agrawal , Ankan Saha , Yafei Wang , Yan Wang , Eric Lawrence , Ashwin Narasimha Murthy , Aastha Nigam , Bohong Zhao , Albert Lingfeng Cui , David Sung , Aastha Jain , Abdulla Mohammad Al-Qawasmeh
IPC: G06N3/08 , G06N3/04 , G06F18/214
CPC classification number: G06N3/08 , G06F18/2148 , G06N3/04
Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.
-
公开(公告)号：US10607118B2
公开(公告)日：2020-03-31
申请号：US15840823
申请日：2017-12-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Arun Sacheti , FNU Yokesh Kumar , Saurajit Mukherjee , Nikesh Srivastava , Yan Wang , Kuang-Huei Lee , Surendra Ulabala
IPC: G06K9/62 , G06F16/532 , G06F16/583 , G06K9/00
Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.
-
-
-
-
-
-
Search Results
17
records
(took 0.02 seconds)
