知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

12 records (took 0.014 seconds)

    Image augmentation and object detection
    2.
    发明授权
    Image augmentation and object detection 有权

    公开(公告)号：US11238314B2

    公开(公告)日：2022-02-01

    申请号：US16686051

    申请日：2019-11-15

    Applicant: salesforce.com, inc.

    Inventor： Ankit Chadha ,  Caiming Xiong ,  Ran Xu

    IPC: G06K9/62 ,  G06T3/40 ,  G06T3/60 ,  G06N3/04 ,  G06N3/08 ,  G06T3/20

    Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

    Intelligent training set augmentation for natural language processing tasks
    4.
    发明授权
    Intelligent training set augmentation for natural language processing tasks 有权

    公开(公告)号：US11599721B2

    公开(公告)日：2023-03-07

    申请号：US17002562

    申请日：2020-08-25

    Applicant: salesforce.com, inc.

    Inventor： Shiva Kumar Pentyala ,  Mridul Gupta ,  Ankit Chadha ,  Indira Iyer ,  Richard Socher

    IPC: G06F40/253 ,  G10L15/19 ,  G06F40/30

    Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

    SYNTHETIC CRAFTING OF TRAINING AND TEST DATA FOR NAMED ENTITY RECOGNITION
    5.
    发明申请
    SYNTHETIC CRAFTING OF TRAINING AND TEST DATA FOR NAMED ENTITY RECOGNITION 有权

    公开(公告)号：US20220245346A1

    公开(公告)日：2022-08-04

    申请号：US17248583

    申请日：2021-01-29

    Applicant: salesforce.com, inc.

    Inventor： Shubham Mehrotra ,  Ankit Chadha

    IPC: G06F40/295 ,  G06F40/47 ,  G06F40/30

    Abstract: A method and system for extracting and labeling Named-Entity Recognition (NER) data in a target language for use in a multi-lingual software module has been developed. First, a textual sentence is translated to the target language using a translation module. A named entity is identified and extracted within the translated sentence. The named entity is identified by either: exact mapping; a semantically similar translated named entity that meets a predetermined minimum threshold of similarity; or utilizing a rule-based library for the target language. Once identified, the named entity is labeled with a pre-determined category and stored in a retrievable electronic database.

    Intelligent Training Set Augmentation for Natural Language Processing Tasks
    6.
    发明申请
    Intelligent Training Set Augmentation for Natural Language Processing Tasks 有权

    公开(公告)号：US20220067277A1

    公开(公告)日：2022-03-03

    申请号：US17002562

    申请日：2020-08-25

    Applicant: salesforce.com, inc.

    Inventor： Shiva Kumar Pentyala ,  Mridul Gupta ,  Ankit Chadha ,  Indira Iyer ,  Richard Socher

    IPC: G06F40/253 ,  G06F40/30 ,  G10L15/19

    Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

    System and method for unsupervised density based table structure identification
    7.
    发明授权
    System and method for unsupervised density based table structure identification 有权

    公开(公告)号：US11347708B2

    公开(公告)日：2022-05-31

    申请号：US16680302

    申请日：2019-11-11

    Applicant: salesforce.com, inc.

    Inventor： Ankit Chadha ,  Zeyuan Chen ,  Caiming Xiong ,  Ran Xu ,  Richard Socher

    IPC: G06F16/30 ,  G06F16/22 ,  G06F16/28

    Abstract: Embodiments described herein provide unsupervised density-based clustering to infer table structure from document. Specifically, a number of words are identified from a block of text in an noneditable document, and the spatial coordinates of each word relative to the rectangular region are identified. Based on the word density of the rectangular region, the words are grouped into clusters using a heuristic radius search method. Words that are grouped into the same cluster are determined to be the element that belong to the same cell. In this way, the cells of the table structure can be identified. Once the cells are identified based on the word density of the block of text, the identified cells can be expanded horizontally or grouped vertically to identify rows or columns of the table structure.

    IMAGE AUGMENTATION AND OBJECT DETECTION
    8.
    发明申请
    IMAGE AUGMENTATION AND OBJECT DETECTION 有权

    公开(公告)号：US20210150282A1

    公开(公告)日：2021-05-20

    申请号：US16686051

    申请日：2019-11-15

    Applicant: salesforce.com, inc.

    Inventor： Ankit Chadha ,  Caiming Xiong ,  Ran Xu

    IPC: G06K9/62 ,  G06T3/40 ,  G06T3/60 ,  G06T3/20 ,  G06N3/08 ,  G06N3/04

    Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

    HIERARCHICAL NATURAL LANGUAGE UNDERSTANDING SYSTEMS
    9.
    发明申请
    HIERARCHICAL NATURAL LANGUAGE UNDERSTANDING SYSTEMS 有权

    公开(公告)号：US20220245349A1

    公开(公告)日：2022-08-04

    申请号：US17162318

    申请日：2021-01-29

    Applicant: salesforce.com, inc.

    Inventor： Shiva Kumar Pentyala ,  Jean-Marc Soumet ,  Shashank Harinath ,  Shilpa Bhagavath ,  Johnson Liu ,  Ankit Chadha

    IPC: G06F40/30 ,  G06N20/00 ,  G06F40/295

    Abstract: Methods and systems for hierarchical natural language understanding are described. A representation of an utterance is inputted to a first machine learning model to obtain information on the first utterance. According to the information on the utterance a determination that the representation of the utterance is to be inputted to a second machine learning model that performs a dedicated natural language task is performed. In response to determining that the representation of the utterance is to be inputted to a second machine learning model, the utterance is inputted to the second machine learning model to obtain an output of the dedicated natural language task.

Patent Agency Ranking