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    UNSUPERVISED DIALOGUE TOPIC EXTRACTION
    11.
    发明申请
    UNSUPERVISED DIALOGUE TOPIC EXTRACTION 有权

    公开(公告)号：US20210149949A1

    公开(公告)日：2021-05-20

    申请号：US16685933

    申请日：2019-11-15

    Applicant: salesforce.com, inc.

    Inventor： Zachary Alexander ,  Na Cheng

    IPC: G06F16/61 ,  G06F7/08 ,  G06F40/30

    Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for extracting topics 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 each exchange label sequence corresponds to a sequence of utterances generated by the entity. The system processes the exchange label sequences to generate one or more subsets of the utterances, where each of the subsets corresponds to a particular topic.

    DYNAMIC FIELD VALUE RECOMMENDATION METHODS AND SYSTEMS
    12.
    发明申请
    DYNAMIC FIELD VALUE RECOMMENDATION METHODS AND SYSTEMS 有权

    公开(公告)号：US20210149933A1

    公开(公告)日：2021-05-20

    申请号：US15929364

    申请日：2020-04-28

    Applicant: salesforce.com, Inc.

    Inventor： Son Thanh Chang ,  Weiping Peng ,  Na Cheng ,  Feifei Jiang ,  Jacob Nathaniel Huffman ,  Nandini Suresh Kumar ,  Khoa Le ,  Christopher Larry

    IPC: G06F16/31 ,  H04L12/58 ,  G06F16/34 ,  G06F16/35

    Abstract: Computing systems, database systems, and related methods are provided for recommending values for fields of database objects and dynamically updating a recommended value for a field of a database record in response to updated auxiliary data associated with the database record. One method involves obtaining associated conversational data, segmenting the conversational data, converting each respective segment of conversational data into a numerical representation, generating a combined numerical representation of the conversational data based on the sequence of numerical representations using an aggregation model, generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and autopopulating the field of the case database object with the recommended value.

    Question answering using dynamic question-answer database
    13.
    发明授权
    Question answering using dynamic question-answer database 有权

    公开(公告)号：US12001801B2

    公开(公告)日：2024-06-04

    申请号：US16685909

    申请日：2019-11-15

    Applicant: salesforce.com, inc.

    Inventor： Yuanxin Wang ,  Anuprit Kale ,  Zachary Alexander ,  Na Cheng

    IPC: G06F40/30 ,  G06F16/9032 ,  G06F18/22 ,  G06F40/205 ,  G06N20/00

    CPC classification number: G06F40/30 ,  G06F16/90332 ,  G06F18/22 ,  G06F40/205 ,  G06N20/00

    Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for integrating question generation and answer retrieval in a question answer system. The system generates a question using a set of documents and determines whether it is semantically distinct from questions in a question-answer repository. After determining that the question is semantically distinct from questions in the question-answer repository, the system adds the question to the question-answer repository. Upon receipt of a user-submitted question, the system uses the question-answer repository to identify a semantically similar question. The system retrieves an answer corresponding to the identified question from the question-answer repository and provides the answer in response to the user-submitted question.

    MULTI-LINGUAL INTENT MODEL WITH OUT-OF-DOMAIN DETECTION
    14.
    发明申请
    MULTI-LINGUAL INTENT MODEL WITH OUT-OF-DOMAIN DETECTION 有权

    公开(公告)号：US20230086302A1

    公开(公告)日：2023-03-23

    申请号：US17479748

    申请日：2021-09-20

    Applicant: salesforce.com, inc.

    Inventor： Shilpa Bhagavath ,  Shubham Mehrotra ,  Abhishek Sharma ,  Shashank Harinath ,  Na Cheng ,  Zineb Laraki

    IPC: G06F40/263 ,  G06F40/58 ,  G06K9/62

    Abstract: A method that includes receiving an input at an interactive conversation service that uses an intent classification model. The method may further include generating, using an encoder model of the intent classification model, a set of output vectors corresponding to the input, where the encoder model is configured to determine a set of metrics corresponding to intent classifications. The method may further include determining, using an outlier detection model of the intent classification model, whether the input is in-domain or out-of-domain (OOD) based on a first vector of the set of output vectors satisfying a domain threshold relative to one or more of the intent classifications. The method may further include outputting, by the intent classification model, a second vector of the set of output vectors that indicates the set of metrics corresponding to the intent classifications or an indication that the input is OOD.

    TRAINING A MACHINE LEARNING MODEL USING STRUCTURED DATA
    15.
    发明申请
    TRAINING A MACHINE LEARNING MODEL USING STRUCTURED DATA 有权

    公开(公告)号：US20220318669A1

    公开(公告)日：2022-10-06

    申请号：US17220567

    申请日：2021-04-01

    Applicant: salesforce.com, inc.

    Inventor： Zachary Alexander ,  Na Cheng ,  Jayesh Govindarajan

    IPC: G06N20/00 ,  G06K9/62

    Abstract: A computing system may receive a corpus of training data including a plurality of data entity schemas. A first data entity of a first set of data entities corresponding to a first data entity schema is associated with a topic characteristic based on a first set of attributes defined by the first data entity schema, and a first attribute of the first set of attributes is associated with a structural characteristic that is common across each of the first set of data entities. The system may identify a respective attribute type identifier for each attribute of the first set, generate an attribute embedding for each attribute using the attribute value and the identifier, generate an entity embedding based on each attribute embedding and parameterize the topic characteristic for each data entity and the structural characteristic for each attribute.

    Identification of response list
    16.
    发明授权
    Identification of response list 有权

    公开(公告)号：US11379671B2

    公开(公告)日：2022-07-05

    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 ,  G06N20/00 ,  G06F16/332

    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.

    TECHNIQUES AND ARCHITECTURES FOR RECOMMENDING PRODUCTS BASED ON WORK ORDERS
    18.
    发明申请
    TECHNIQUES AND ARCHITECTURES FOR RECOMMENDING PRODUCTS BASED ON WORK ORDERS 有权

    公开(公告)号：US20210150610A1

    公开(公告)日：2021-05-20

    申请号：US16773727

    申请日：2020-01-27

    Applicant: salesforce.com, inc.

    Inventor： Yixin Mao ,  Sitaram Asur ,  Na Cheng ,  Gary Brandeleer ,  Kavya Murali ,  Nicholas Beng Tek Geh

    IPC: G06Q30/06 ,  G06Q10/06 ,  G06Q10/08 ,  G06F17/16

    Abstract: A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.

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