Extracting non-textual data from documents via machine learning

    公开(公告)号:US11455812B2

    公开(公告)日:2022-09-27

    申请号:US16818311

    申请日:2020-03-13

    IPC分类号: G06V30/414 G06K9/62 G06N20/00

    摘要: An approach for extracting non-textual data from an electronic document is disclosed. The approach includes receiving a request to extract a file and converting the file into pixels. The approach creates a pixel map of the converted file and determines one or more density clusters of the pixel map based on image clustering method. Furthermore, the approach determines one or more coordinates of the one or more density clusters and determines one or more candidate information regions based on the one or more coordinates, density of the one or more density clusters. Finally, the approach extracts one or more textual data based on the one or more candidate information regions and outputs the extracted one or more textual data.

    Determining image defects using image comparisons

    公开(公告)号:US11321822B2

    公开(公告)日:2022-05-03

    申请号:US16947376

    申请日:2020-07-30

    摘要: A method, computer system, and a computer program product for analyzing visual defects is provided. The present invention may include generating a template image. The present invention may include capturing a test image. The present invention may include performing an image registration between the template image and the test image. The present invention may include generating a registered test image. The present invention may include performing an image difference analysis between the registered test image and the template image. The present invention may include generating a differential image. The present invention may include synthesizing the registered, differential image, and template image. The present invention may include generating a synthetic image. The present invention may include inputting the synthetic image into a multi-scale detection network. The present invention may include generating a defect map.

    ADAPTIVELY COMPRESSING A DEEP LEARNING MODEL

    公开(公告)号:US20230103149A1

    公开(公告)日:2023-03-30

    申请号:US17449652

    申请日:2021-09-30

    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.

    Environment monitoring and associated monitoring device

    公开(公告)号:US11351682B2

    公开(公告)日:2022-06-07

    申请号:US16445262

    申请日:2019-06-19

    摘要: Embodiments of the present disclosure relate to environment monitoring and associated monitoring device, in which region-level environmental conditions are obtained and are detected by a plurality of sensors installed in a plurality of regions of a space enclosing electronic devices. Additionally, respective region priorities of the plurality of regions are determined based at least in part on the region-level environmental conditions, a region priority indicating a probability of occurrence of a risk event within a corresponding region. Furthermore, a movement path of a moveable monitoring device through the plurality of regions is determined based on the region priorities for detecting device-level environmental conditions associated with the electronic devices, and a moveable monitoring device and a system are disclosed.

    DYNAMIC WORKFLOW WITH KNOWLEDGE GRAPHS

    公开(公告)号:US20210173873A1

    公开(公告)日:2021-06-10

    申请号:US16702684

    申请日:2019-12-04

    摘要: Disclosed embodiments provide techniques for computerized technical support. A knowledge graph for a computer application is established. An input query from a user is processed to extract entities used as action identifiers. One or more nodes within the knowledge graph are identified, along with corresponding relationship edges leading to the nodes. When multiple candidate nodes are found that contain information relevant to the input query, a custom clarification statement is created based on the one or more identified relationship edges. The user provides answers to the clarification statement to narrow down which nodes contain the most relevant information. This process may continue, eliminating nodes based on user responses, until a single node remains, corresponding to an action identifier. The action identifier includes action description information that provides technical assistance to a user.

    Dynamic workflow with knowledge graphs

    公开(公告)号:US11314812B2

    公开(公告)日:2022-04-26

    申请号:US16702684

    申请日:2019-12-04

    摘要: Disclosed embodiments provide techniques for computerized technical support. A knowledge graph for a computer application is established. An input query from a user is processed to extract entities used as action identifiers. One or more nodes within the knowledge graph are identified, along with corresponding relationship edges leading to the nodes. When multiple candidate nodes are found that contain information relevant to the input query, a custom clarification statement is created based on the one or more identified relationship edges. The user provides answers to the clarification statement to narrow down which nodes contain the most relevant information. This process may continue, eliminating nodes based on user responses, until a single node remains, corresponding to an action identifier. The action identifier includes action description information that provides technical assistance to a user.

    DETERMINING IMAGE DEFECTS USING IMAGE COMPARISONS

    公开(公告)号:US20220036525A1

    公开(公告)日:2022-02-03

    申请号:US16947376

    申请日:2020-07-30

    摘要: A method, computer system, and a computer program product for analyzing visual defects is provided. The present invention may include generating a template image. The present invention may include capturing a test image. The present invention may include performing an image registration between the template image and the test image. The present invention may include generating a registered test image. The present invention may include performing an image difference analysis between the registered test image and the template image. The present invention may include generating a differential image. The present invention may include synthesizing the registered, differential image, and template image. The present invention may include generating a synthetic image. The present invention may include inputting the synthetic image into a multi-scale detection network. The present invention may include generating a defect map.

    EXTRACTING NON-TEXTUAL DATA FROM DOCUMENTS VIA MACHINE LEARNING

    公开(公告)号:US20210286993A1

    公开(公告)日:2021-09-16

    申请号:US16818311

    申请日:2020-03-13

    IPC分类号: G06K9/00 G06N20/00 G06K9/62

    摘要: An approach for extracting non-textual data from an electronic document is disclosed. The approach includes receiving a request to extract a file and converting the file into pixels. The approach creates a pixel map of the converted file and determines one or more density clusters of the pixel map based on image clustering method. Furthermore, the approach determines one or more coordinates of the one or more density clusters and determines one or more candidate information regions based on the one or more coordinates, density of the one or more density clusters. Finally, the approach extracts one or more textual data based on the one or more candidate information regions and outputs the extracted one or more textual data.