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公开(公告)号：US11809454B2
公开(公告)日：2023-11-07
申请号：US17100864
申请日：2020-11-21
发明人： Zhong Fang Yuan , Tong Liu , Li Juan Gao , Ming Jin Chen , Ke Yong Zhang
IPC分类号： G06F16/00 , G06F16/28 , G06F40/30 , G06F40/117 , G06N20/00
CPC分类号： G06F16/285 , G06F40/117 , G06F40/30 , G06N20/00
摘要： Label-based document classification using artificial intelligence includes collecting, by one or more processors, a plurality of pre-trained classification models into a model pool and a plurality of documents into a document pool. The collected plurality of pre-trained classification models are applied in parallel to the plurality of documents in the document pool to generate a list of labels. Based on the list of labels, a final label result is generated according to which a baseline algorithm for document classification is generated by the one or more processors.
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公开(公告)号：US20180239575A1
公开(公告)日：2018-08-23
申请号：US15439995
申请日：2017-02-23
发明人： Ming Jin Chen , Liang Liang Tang , Zu Long Wang , Yin Jun Zhang , Hong Yin Zhu
CPC分类号： G06F3/1454 , G09G5/006 , G09G2350/00 , G09G2354/00 , G09G2370/16 , H04W4/80
摘要： The disclosure is directed to mobile application function sharing. A system according to embodiments includes: a plurality of mobile devices; and an application on each of the mobile devices; wherein the application on each of the mobile devices includes a share context framework, including: a view interface for generating a share context including a sequence of page views of content of the application; a sender interface for transmitting the generated share context directly to the application on another of the plurality of mobile devices; a receiver interface for receiving, directly from the application on another of the plurality of mobile devices, a share context including a sequence of page views of content of the application on the other mobile device; and an executor for rendering in the application the sequence of page views included in the received share context.
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公开(公告)号：US11854287B2
公开(公告)日：2023-12-26
申请号：US17456197
申请日：2021-11-23
发明人： Li Juan Gao , Zhong Fang Yuan , Tong Liu , Ming Xia Shi , Ming Jin Chen
IPC分类号： G06V30/418 , G06V30/148 , G06V30/413 , G06V30/19
CPC分类号： G06V30/418 , G06V30/153 , G06V30/19013 , G06V30/413
摘要： A method, a computer program product, and a computer system compare images for content consistency. The method includes receiving a first image including a first document and a second image including a second document. The method includes performing a visual classification analysis on the first image and the second image. The visual classification analysis generates an overlap of the first image with the second image. The method includes determining whether a region of the overlap is indicative of a content inconsistency. As a result of the region of the overlap being indicative of a content inconsistency, the method includes performing a character recognition analysis on a first area of the first image and a second area of the second image corresponding to the region of the overlap to verify the content inconsistency.
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公开(公告)号：US20230179410A1
公开(公告)日：2023-06-08
申请号：US17457717
申请日：2021-12-06
发明人： Li Juan Gao , Zhong Fang Yuan , Ming Jin Chen , Tong Liu
CPC分类号： H04L9/0869 , H04L9/0819 , G06V30/19147 , G06N3/0454 , G06F40/40
摘要： A method, computer system, and a computer program product for data protection is provided. The present invention may include, generating an encoder network. The present invention may also include, encoding a training data using the generated encoder network, wherein the training data includes natural language data. The present invention may further include, training a deep learning model using the encoded training data.
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公开(公告)号：US20230162521A1
公开(公告)日：2023-05-25
申请号：US17456197
申请日：2021-11-23
发明人： Li Juan Gao , Zhong Fang Yuan , Tong Liu , Ming Xia Shi , Ming Jin Chen
CPC分类号： G06K9/00483 , G06K9/00456 , G06K9/344 , G06K9/6202 , G06K2209/01
摘要： A method, a computer program product, and a computer system compare images for content consistency. The method includes receiving a first image including a first document and a second image including a second document. The method includes performing a visual classification analysis on the first image and the second image. The visual classification analysis generates an overlap of the first image with the second image. The method includes determining whether a region of the overlap is indicative of a content inconsistency. As a result of the region of the overlap being indicative of a content inconsistency, the method includes performing a character recognition analysis on a first area of the first image and a second area of the second image corresponding to the region of the overlap to verify the content inconsistency.
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公开(公告)号：US20220351020A1
公开(公告)日：2022-11-03
申请号：US17245541
申请日：2021-04-30
发明人： Kun Yan Yin , Chao Yu , Ming Jin Chen , Teng Sun , Xiao Ye Li
IPC分类号： G06N3/04
摘要： In an approach to deploying parallelizable deep learning models by adapting to the computing devices, a deep learning model is split into a plurality of slices, where each slice can exchange data with related slices. Virtual models are created from the plurality of slices, where the virtual models are based on capabilities of a plurality of devices on which the one or more virtual models are to be deployed, and further where each virtual model contains each slice of the plurality of slices. The one or more virtual models are stored in a cache. Responsive to determining that the deep learning model is to be deployed on one or more devices, a candidate model is selected from the virtual models in the cache, where the selection is based on information from a device monitor about the devices.
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公开(公告)号：US20230103149A1
公开(公告)日：2023-03-30
申请号：US17449652
申请日：2021-09-30
发明人： Chao Yu , Kun Yan Yin , Ming Jin Chen , Teng Sun , Guang Qing Zhong
IPC分类号： G06N3/04
摘要： An approach is provided for adaptively compressing a deep learning model. An original deep learning model for different Internet of Things (IoT) devices is determined. Device information is collected from the IoT devices. Based on the device information, multiple recommendation engines are selected from a set of recommendation engines. Compression factor combinations are determined by using the multiple recommendation engines. Compression ratios and model accuracies for the compression factor combinations are determined. Based on the compression ratios and the model accuracies, an optimal compression factor combination is selected from the compression factor combinations. A compressed deep learning model is generated by compressing the original deep learning model by using the optimal compression factor. The compressed deep learning model is deployed to the IoT devices.
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公开(公告)号：US11615618B2
公开(公告)日：2023-03-28
申请号：US17225165
申请日：2021-04-08
发明人： Kun Yan Yin , Chao Yu , Ming Jin Chen , Teng Sun , Hong Bing Zhang
摘要： A computer-implemented method for annotating images is disclosed. The computer-implemented method includes generating a saliency map corresponding to an input image, wherein the input image is an image that requires annotation, generating a behavior saliency map, wherein the behavior saliency map is a saliency map formed from an average of a plurality of objects contained within respective bounding boxes of a plurality of sample images, generating a historical saliency map, wherein the historical saliency map is a saliency map formed from an average of a plurality of tagged objects in the plurality of sample images, fusing the saliency map corresponding to the input image, the behavior saliency map, and the historical saliency map to form a fused saliency map, and generating, based on the fused saliency map, a bounding box around an object in the input image.
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公开(公告)号：US20220327312A1
公开(公告)日：2022-10-13
申请号：US17225165
申请日：2021-04-08
发明人： Kun Yan Yin , Chao Yu , Ming Jin Chen , Teng Sun , Hong Bing Zhang
摘要： A computer-implemented method for annotating images is disclosed. The computer-implemented method includes generating a saliency map corresponding to an input image, wherein the input image is an image that requires annotation, generating a behavior saliency map, wherein the behavior saliency map is a saliency map formed from an average of a plurality of objects contained within respective bounding boxes of a plurality of sample images, generating a historical saliency map, wherein the historical saliency map is a saliency map formed from an average of a plurality of tagged objects in the plurality of sample images, fusing the saliency map corresponding to the input image, the behavior saliency map, and the historical saliency map to form a fused saliency map, and generating, based on the fused saliency map, a bounding box around an object in the input image.
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公开(公告)号：US20220164370A1
公开(公告)日：2022-05-26
申请号：US17100864
申请日：2020-11-21
发明人： Zhong Fang Yuan , Tong Liu , Li Juan Gao , Ming Jin Chen , Ke Yong Zhang
IPC分类号： G06F16/28 , G06N20/00 , G06F40/117 , G06F40/30
摘要： Label-based document classification using artificial intelligence includes collecting, by one or more processors, a plurality of pre-trained classification models into a model pool and a plurality of documents into a document pool. The collected plurality of pre-trained classification models are applied in parallel to the plurality of documents in the document pool to generate a list of labels. Based on the list of labels, a final label result is generated according to which a baseline algorithm for document classification is generated by the one or more processors.
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