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    Hybrid-input predictive data analysis
    71.
    发明授权
    Hybrid-input predictive data analysis 有权

    公开(公告)号：US11954602B1

    公开(公告)日：2024-04-09

    申请号：US16792635

    申请日：2020-02-17

    Applicant: Optum, Inc.

    Inventor： Daniel J. Mulcahy ,  Subhash Seelam ,  Damian Kelly ,  Vijay S. Nori ,  Adam Russell

    IPC: G06N5/02 ,  G06F40/284 ,  G06N20/00

    CPC classification number: G06N5/02 ,  G06F40/284 ,  G06N20/00

    Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing hybrid input predictive data analysis. In one example, a method includes identifying a vocabulary data object associated with one or more prediction input text data objects; determining, based at least in part on the vocabulary data object, a per-input-entity tokenized representation for the one or more prediction input text data objects; determining, based at least in part on the per-input-entity tokenized representation and one or more prediction input structured data objects and using a hybrid-input predictive model, a prediction score; determining, based at least in part on one or more threshold determination configuration criteria, a predictive threshold for the hybrid-input predictive model; generating, based at least in part on the prediction score and the predictive threshold, a predictive output; and performing one or more prediction-based actions based at least in part on the predictive output.

    TRAINING MACHINE LEARNING BASED NATURAL LANGUAGE PROCESSING FOR SPECIALTY JARGON
    72.
    发明公开
    TRAINING MACHINE LEARNING BASED NATURAL LANGUAGE PROCESSING FOR SPECIALTY JARGON 审中-公开

    公开(公告)号：US20240111795A1

    公开(公告)日：2024-04-04

    申请号：US17936873

    申请日：2022-09-30

    Applicant: Florida Power & Light Company

    Inventor： Kevin Ogden ,  Lissette Carrasco ,  Fred Meyler ,  Camille Zerfas ,  Joseph Prusa ,  Yihong Ge ,  Mahdi Esfahanian

    IPC: G06F16/332 ,  G06F11/34 ,  G06N5/02 ,  G06N5/04

    CPC classification number: G06F16/3329 ,  G06F11/3409 ,  G06N5/02 ,  G06N5/04

    Abstract: Systems and methods for training a machine learning based natural language processor to process specialized language terms. A training corpus of labeled specialized language terms associated with a specialized subject area is assembled. A generated database query corresponding to a question comprising at least one specialized language term within the set of labeled specialized language terms is created with a machine learning based natural language processing process. A cost function is determined based on differences between the generated database query and contents in a knowledge graph mapping terms describing characteristics of entities associated with the specialized subject area. The natural language processing process is refined based on feeding back the cost function to create a refined natural language process. Received queries are processed with the refined natural language process to create database queries directed to answering questions associated with the received query, and the answer is presented.

    SYMBOLIC STATE ESTIMATION FOR CONTACT-RICH MANIPULATION TASKS
    73.
    发明公开
    SYMBOLIC STATE ESTIMATION FOR CONTACT-RICH MANIPULATION TASKS 审中-公开

    公开(公告)号：US20240111571A1

    公开(公告)日：2024-04-04

    申请号：US17934459

    申请日：2022-09-22

    Applicant: Intrinsic Innovation LLC

    Inventor： Wenzhao Lian ,  Stefan Schaal ,  Takatoki Migimatsu

    IPC: G06F9/48 ,  G06N5/02

    CPC classification number: G06F9/4881 ,  G06N5/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot using symbolic states. One of the methods includes receiving a definition of a task having multiple task states, wherein each task state is associated with a different respective control policy; executing the task using an initial control policy associated with an initial task state; during execution of the task, continually generating, from sensor data, a prediction of the task state of the task; and upon determining that a transition to a different task state has occurred, transitioning the robot to a different control policy associated with the different task state.

    Dashboard for review and management of pre-paid professional services
    74.
    发明授权
    Dashboard for review and management of pre-paid professional services 有权

    公开(公告)号：US11948137B2

    公开(公告)日：2024-04-02

    申请号：US16911255

    申请日：2020-06-24

    Applicant: Kenneth L Sherman

    Inventor： Kenneth L Sherman

    IPC: G06Q20/14 ,  G06Q20/12 ,  G06Q20/28 ,  G06Q50/18 ,  H04N7/15 ,  G06F3/04842 ,  G06N5/02

    CPC classification number: G06Q20/145 ,  G06Q20/12 ,  G06Q20/28 ,  G06Q50/184 ,  H04N7/15 ,  G06F3/04842 ,  G06N5/02

    Abstract: One embodiment provides a method comprising generating a first graphical user interface (GUI) for display on an electronic device. The first GUI comprises data indicative of at least one professional service available for selection. The method comprises receiving, from the electronic device, a request to initiate a professional service selected from the first GUI. The method further comprises generating a second GUI for display on the electronic device. The second GUI comprises a customized questionnaire for collecting information relevant to the professional service selected. The method further comprises receiving, from the electronic device, user responses to the customized questionnaire, and initiating an analysis of the user responses. A communication indicative of the analysis is forwarded to the electronic device.

    Generating and providing team member recommendations for content collaboration
    75.
    发明授权
    Generating and providing team member recommendations for content collaboration 有权

    公开(公告)号：US11948104B2

    公开(公告)日：2024-04-02

    申请号：US17354187

    申请日：2021-06-22

    Applicant: Dropbox, Inc.

    Inventor： Jiarui Ding

    IPC: G06Q10/0631 ,  G06N5/02 ,  G06N20/00

    CPC classification number: G06Q10/063112 ,  G06N5/02 ,  G06N20/00

    Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for identifying and recommending team members for target users from a content management system utilizing a machine learning approach. In particular, the disclosed systems can generate a set of candidate team members from among users of the content management system based on various factors such as access to a common digital content item. In some embodiments, the disclosed systems further determine recommended team members from among the set of candidate team members. For example, the disclosed systems can utilize a machine learning approach to generate or predict recommended team members based on particular features extracted or determined for, or with respect to, the various candidate team members. In certain implementations, the disclosed systems further provide a recommended-team-member notification to notify a target user of a recommended team member.

    LANGUAGE AGNOSTIC ARCHITECTURE FOR A SEARCH ENGINE
    76.
    发明公开
    LANGUAGE AGNOSTIC ARCHITECTURE FOR A SEARCH ENGINE 审中-公开

    公开(公告)号：US20240095812A1

    公开(公告)日：2024-03-21

    申请号：US18157697

    申请日：2023-01-20

    Applicant: Shutterstock, Inc.

    Inventor： Patrick Nicholson

    IPC: G06Q30/0601 ,  G06F16/901 ,  G06F16/953 ,  G06N5/02

    CPC classification number: G06Q30/0641 ,  G06F16/9027 ,  G06F16/953 ,  G06N5/02

    Abstract: A data structure to facilitate language agnostic searches in an online marketplace is provided. The data structure includes multiple nodes, each node having one or more labels associated to a network resource; and multiple links joining a parent node to a child node. A first label is associated with one or more words in a first language, the link indicates a navigation route for a search engine to move from the parent node to the child node in search of a pre-selected media file, and a label in a parent node is associated with an unambiguous concept in a knowledge base, the network resource includes one or more media files associated with a license weight, and the license weight for the parent node is higher than the license weight for the child node.

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