利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

817 条记录 (耗时 0.03 秒)

    Call Tagging Using Machine Learning Model
    1.
    发明公开
    Call Tagging Using Machine Learning Model 审中-公开

    公开(公告)号：US20240346230A1

    公开(公告)日：2024-10-17

    申请号：US18752195

    申请日：2024-06-24

    申请人： Calabrio, Inc.

    发明人： Dylan Morgan ,  Boris Chaplin ,  Kyle Smaagard ,  Chris Vanciu ,  Laura Cattaneo ,  Matt Matsui ,  Catherine Bullock

    IPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/51

    CPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/5183

    摘要： Systems and methods disclosed relate to contextually tagging statements associated with calls. In particular, the contextual tagging is directed to training a call tagging model for predicting one or more categories associated with a statement for tagging. The disclosed technology generates training data for training the call tagging model based on a list of known phrases used in contacts in a contextual category and matching phrases and words in the list of known phrases against words and phrases used in statements in sample call transcripts. The call tagging model is fine-tuned by using sample statements that appear in contacts. Once trained, the call tagging model is used to determine a probability distribution of categories associated with statements in a contact and further determine contact-level category distributions using multi-dimensional vectors. The tagged contacts are used to determine contacts that are contextually similar to a given contact.

    PROVIDING TAGGING ASSISTANCE AND COMPLIANCE REVIEW IN CONNECTION WITH STANDARDIZED DOCUMENTS
    6.
    发明公开
    PROVIDING TAGGING ASSISTANCE AND COMPLIANCE REVIEW IN CONNECTION WITH STANDARDIZED DOCUMENTS 审中-公开

    公开(公告)号：US20240232515A1

    公开(公告)日：2024-07-11

    申请号：US12730209

    申请日：2010-03-23

    申请人： Michael L. Rohan ,  Kevin D. Berens

    发明人： Michael L. Rohan ,  Kevin D. Berens

    IPC分类号： G06F40/174 ,  G06F40/117

    CPC分类号： G06F40/174 ,  G06F40/117

    摘要： Systems and methods for facilitating the review of standardized documents and tagging such documents are provided. According to one embodiment, a method of communicating tag popularity is provided. In the context of a financial document processing application, which is configured to facilitate (i) tagging of financial information with taxonomy clements and (ii) creation of standardized documents in a form suitable for filing with a regulatory agency, tagged financial information is presented. Usage data is received by the financial document processing application relating to aggregate usage of taxonomy elements in standardized documents previously filed with the regulatory agency by filing companies. Visual feedback is then provided with respect to tagging conformance of the tagged financial information based on the usage data by displaying, within a user interface of the financial document processing application, an indication of prevalence of usage of the taxonomy clements among the filing companies.

    Digital asset and design component tracking in a collaborative environment
    7.
    发明授权
    Digital asset and design component tracking in a collaborative environment 有权

    公开(公告)号：US12020209B2

    公开(公告)日：2024-06-25

    申请号：US14726334

    申请日：2015-05-29

    申请人： ADOBE INC.

    发明人： Lavanya Raghunath ,  Gavin Murray Peacock ,  Michael James Andrew Smith

    IPC分类号： G06F40/117 ,  G06F3/04842 ,  G06F40/186 ,  G06F40/197 ,  G06Q10/10

    CPC分类号： G06Q10/103 ,  G06F3/04842 ,  G06F40/117 ,  G06F40/186 ,  G06F40/197

    摘要： As digital assets are created and shared, design components that comprise those digital assets are curated, organized, and tracked so as to allow meaningful relationships to be established between shared assets and design components. The tracking that underlies such relationships is provided by metadata associated with a given design component. This metadata may include information such as an asset identifier that identifies a source digital asset from which the design component was extracted; a version identifier that identifies a version of the source digital asset; an author identifier that identifies an author of the source digital asset; and a layer identifier that can be used to reveal the context in which the design component was derived from the source asset. This metadata allows relationships to be established between a design component and the digital assets that incorporate that design component, thus facilitating asset and component tracking and update notification broadcasting.

    Call tagging using machine learning model
    8.
    发明授权
    Call tagging using machine learning model 有权

    公开(公告)号：US12019976B1

    公开(公告)日：2024-06-25

    申请号：US18065597

    申请日：2022-12-13

    申请人： Calabrio, Inc.

    发明人： Dylan Morgan ,  Boris Chaplin ,  Kyle Smaagard ,  Chris Vanciu ,  Laura Cattaneo ,  Matt Matsui ,  Catherine Bullock

    IPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/51

    CPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/5183

    摘要： Systems and methods disclosed relate to contextually tagging statements associated with calls. In particular, the contextual tagging is directed to training a call tagging model for predicting one or more categories associated with a statement for tagging. The disclosed technology generates training data for training the call tagging model based on a list of known phrases used in contacts in a contextual category and matching phrases and words in the list of known phrases against words and phrases used in statements in sample call transcripts. The call tagging model is fine-tuned by using sample statements that appear in contacts. Once trained, the call tagging model is used to determine a probability distribution of categories associated with statements in a contact and further determine contact-level category distributions using multi-dimensional vectors. The tagged contacts are used to determine contacts that are contextually similar to a given contact.

    Call Tagging Using Machine Learning Model
    9.
    发明公开
    Call Tagging Using Machine Learning Model 审中-公开

    公开(公告)号：US20240193347A1

    公开(公告)日：2024-06-13

    申请号：US18065597

    申请日：2022-12-13

    申请人： Calabrio, Inc

    发明人： Dylan Morgan ,  Boris Chaplin ,  Kyle Smaagard ,  Chris Vanciu ,  Laura Cattaneo ,  Matt Matsui ,  Catherine Bullock

    IPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/51

    CPC分类号： G06F40/117 ,  G06F40/166 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00 ,  H04M3/5183

    摘要： Systems and methods disclosed relate to contextually tagging statements associated with calls. In particular, the contextual tagging is directed to training a call tagging model for predicting one or more categories associated with a statement for tagging. The disclosed technology generates training data for training the call tagging model based on a list of known phrases used in contacts in a contextual category and matching phrases and words in the list of known phrases against words and phrases used in statements in sample call transcripts. The call tagging model is fine-tuned by using sample statements that appear in contacts. Once trained, the call tagging model is used to determine a probability distribution of categories associated with statements in a contact and further determine contact-level category distributions using multi-dimensional vectors. The tagged contacts are used to determine contacts that are contextually similar to a given contact.