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    SYSTEM AND METHOD FOR A SINGLE, UNIFIED COMMUNITY AND LEARNING EXPERIENCE
    502.
    发明申请
    SYSTEM AND METHOD FOR A SINGLE, UNIFIED COMMUNITY AND LEARNING EXPERIENCE 有权

    公开(公告)号：US20210150928A1

    公开(公告)日：2021-05-20

    申请号：US15929822

    申请日：2020-05-22

    Applicant: salesforce.com, inc.

    Inventor： Emily Hudson ,  Chris Fox ,  Qingqing Liu ,  Asha Camper Singh ,  Dan Chen ,  Matt Rigdon ,  Nicholas Stuart ,  Jiahan Ericsson ,  Joyce Yeh ,  Samantha Ready ,  Adam Torman

    IPC: G09B7/02 ,  G06F16/9535 ,  G06F16/9538

    Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for implementing an electronic learning (eLearrning) system. In some implementations, an indication of a user selection by a first user of electronic learning content of an electronic learning (eLearning) system is processed, where the electronic learning content includes a learning module. Responsive to processing the indication of the user selection, the learning module is initiated or resumed by providing one or more of a plurality of content items of the learning module for presentation by the client device. The progress of the first user in relation to completion of the learning module is tracked. An unanswered question that pertains to the learning module and that has been submitted by a second user is identified based, at least in part, on the progress of the first user in relation to the completion of the learning module. The unanswered question is then provided via a web page of the learning module for presentation via a client device of the first user.

    SYSTEM AND METHOD FOR AUTOMATICALLY CREATING PERSONALIZED COURSES AND TRACKABLE ACHIEVEMENTS
    503.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATICALLY CREATING PERSONALIZED COURSES AND TRACKABLE ACHIEVEMENTS 有权

    公开(公告)号：US20210150483A1

    公开(公告)日：2021-05-20

    申请号：US15929823

    申请日：2020-05-22

    Applicant: salesforce.com, inc.

    Inventor： Reid Scott Carlberg ,  Adam Torman ,  Emily Hudson ,  Samantha Ready ,  Cameron Reynolds-Flatt ,  Chris Fox ,  Mark Geyer ,  Joseph Cheng ,  Alex Warshavsky

    IPC: G06Q10/10 ,  G06F16/901 ,  G06F16/958 ,  G09B5/02

    Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for generating custom courses for users. A goal of a user of a database system is ascertained and users of a database system that have reached the goal are identified. A list of content items including educational content is generated. The list can be generated using artificial intelligence or computer-generated models. In addition, dependency trees can be applied during the generation of a computer-generated list of content items. The content items can represent a variety of resources including educational learning modules or trails, as well as other actions that the user can take to reach their goal. The list can be provided via a web page for display by a client device, and the user can interact with the list. The user's achievements with respect to the list are tracked and a record of the user's achievements can be updated within the web page, as well as other web pages for access by other users.

    Systems and Methods for Out-of-Distribution Classification
    504.
    发明申请
    Systems and Methods for Out-of-Distribution Classification 有权

    公开(公告)号：US20210150365A1

    公开(公告)日：2021-05-20

    申请号：US16877325

    申请日：2020-05-18

    Applicant: salesforce.com, inc.

    Inventor： Govardana Sachithanandam Ramachandran ,  Ka Chun Au ,  Shashank Harinath ,  Wenhao Liu ,  Alexis Roos ,  Caiming Xiong

    IPC: G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06F17/18

    Abstract: An embodiment provided herein preprocesses the input samples to the classification neural network, e.g., by adding Gaussian noise to word/sentence representations to make the function of the neural network satisfy Lipschitz property such that a small change in the input does not cause much change to the output if the input sample is in-distribution. Method to induce properties in the feature representation of neural network such that for out-of-distribution examples the feature representation magnitude is either close to zero or the feature representation is orthogonal to all class representations. Method to generate examples that are structurally similar to in-domain and semantically out-of domain for use in out-of-domain classification training. Method to prune feature representation dimension to mitigate long tail error of unused dimension in out-of-domain classification. Using these techniques, the accuracy of both in-domain and out-of-distribution identification can be improved.

    IDENTIFICATION OF RESPONSE LIST
    505.
    发明申请
    IDENTIFICATION OF RESPONSE LIST 有权

    公开(公告)号：US20210150146A1

    公开(公告)日：2021-05-20

    申请号：US16687626

    申请日：2019-11-18

    Applicant: salesforce.com, inc.

    Inventor： Zachary Alexander ,  Edgar Gerardo Velasco ,  Victor Winslow Yee ,  Na Cheng ,  Khoa Le

    IPC: G06F40/30 ,  G06F16/33 ,  G06F16/332 ,  G06N20/00

    Abstract: A system is configured to analyze a corpus of historical chat data to identify the list of “best” responses. As such, the user is not required to identify a list of canned responses for input into the system. The described system uses a context word embedding function and response word embedding function to generate context vectors and response vectors corresponding to the corpus of conversation data, and the vectors are represented by a respective context matrix and a response matrix. The system processes these matrices to generate scores for responses, clusters the responses, and identifies the responses corresponding to the best scores for each cluster.

    UNSUPERVISED DIALOGUE STRUCTURE EXTRACTION
    506.
    发明申请
    UNSUPERVISED DIALOGUE STRUCTURE EXTRACTION 有权

    公开(公告)号：US20210149921A1

    公开(公告)日：2021-05-20

    申请号：US16685926

    申请日：2019-11-15

    Applicant: salesforce.com, inc.

    Inventor： Zachary Alexander ,  Na Cheng

    IPC: G06F16/28 ,  G06F16/21 ,  G06N7/00 ,  G06N3/00 ,  G06F17/27

    Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for extracting state flow structures from a corpus of exchanges. The system generates vector representations of utterances of an entity common to the exchanges and uses the vector representations to cluster the utterances.
    The system labels the clusters and uses the labeled clusters to generate an exchange label sequence for each of the exchanges, where the exchange label sequence corresponds to a sequence of utterances generated by the entity. The system processes the exchange label sequences to generate a state flow structure, where each of the states is represented by a corresponding set of utterances.

    METHOD AND USER INTERFACE FOR DATA MAPPING
    508.
    发明申请
    METHOD AND USER INTERFACE FOR DATA MAPPING 有权

    公开(公告)号：US20210149695A1

    公开(公告)日：2021-05-20

    申请号：US17162562

    申请日：2021-01-29

    Applicant: salesforce.com, inc.

    Inventor： Christopher BILL ,  Steven Kostrezewski ,  Sara Flamion

    IPC: G06F9/451 ,  G06F3/0482 ,  G06F3/0484

    Abstract: Embodiments of apparatus, systems, and methods are described for creating, arranging, and displaying data mappings between two different data schemas in a graphical user interface (GUI). The GUI allows scaling of a data schema, automatic data sorting and grouping of objects in a schema, dynamic spacing of data mappings in the GUI, and customizable data map transformations to entities of a canonical data model. The GUI can limit the display of objects and fields to those that have been mapped into entity groups. The GUI can display mapped or unmapped fields to facilitate the mapping of additional fields or objects. The GUI displays visual logic connectors between objects and entities to summarize the relationship and number of mappings between the objects and entities. Objects and entities can be expanded and collapsed to show more granular relationship information. Instance-enabled canonical entities can be created to conceptually group fields.

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