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    Techniques and architectures for recommending products based on work orders
    1.
    发明授权
    Techniques and architectures for recommending products based on work orders 有权

    公开(公告)号：US11544762B2

    公开(公告)日：2023-01-03

    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 ,  G06F17/16 ,  G06Q10/08 ,  G06Q10/06

    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.

    Method and system for capturing data of actions
    2.
    发明授权
    Method and system for capturing data of actions 有权

    公开(公告)号：US11425245B2

    公开(公告)日：2022-08-23

    申请号：US16678530

    申请日：2019-11-08

    Applicant: salesforce.com, inc.

    Inventor： Molly Mahar ,  Nicholas Beng Tek Geh

    IPC: H04M3/42 ,  H04L51/02 ,  H04L51/046 ,  H04M3/53 ,  H04M3/523 ,  H04L51/234

    Abstract: Described herein is a system and method for capturing data associated with actions attempted by an automated agent. The system described herein captures data associated with the actions attempted by an automated agent during the messaging session between an automated agent and the user and present a summary of the actions in a messaging platform. In an embodiment, the automated agent uploads data associated with actions attempted during the messaging session to a server. The server captures the data associated with the actions and generates a description of each action that was attempted. The server generates a summary including the description of each action. The summary of the actions are rendered in the messaging platform.

    TECHNIQUES AND ARCHITECTURES FOR RECOMMENDING PRODUCTS BASED ON WORK ORDERS
    3.
    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.

    Re-indexing query-independent document features for processing search queries
    4.
    发明授权
    Re-indexing query-independent document features for processing search queries 有权

    公开(公告)号：US10733241B2

    公开(公告)日：2020-08-04

    申请号：US15730574

    申请日：2017-10-11

    Applicant: salesforce.com, inc.

    Inventor： Jayesh Govindarajan ,  Ammar Haris ,  Nicholas Beng Tek Geh ,  Francisco Borges

    IPC: G06F17/00 ,  G06F16/93 ,  G06F16/248 ,  G06F16/958 ,  G06F16/332

    Abstract: An online system stores documents for access by users. The online system also stores query independent information about the documents. Query independent features include data that can be used to score or rank a document independent of any terms entered as a search query. The online system periodically determines whether the values of query independent features have changed, such as by checking activity logs. The online system updates records of query independent features accordingly, and sends information about the updated records to an enterprise search platform for re-indexing. When a user sends a search query to the online system, the enterprise search platform determines whether documents are relevant to the query based on the document contents and the query independent features associated with the documents.

    Ranking search results using machine learning based models
    5.
    发明授权
    Ranking search results using machine learning based models 有权

    公开(公告)号：US10606910B2

    公开(公告)日：2020-03-31

    申请号：US15730591

    申请日：2017-10-11

    Applicant: salesforce.com, inc.

    Inventor： Jayesh Govindarajan ,  Nicholas Beng Tek Geh ,  Francisco Borges ,  Ammar Haris

    IPC: G06F17/30 ,  G06F16/9535 ,  H04L29/08 ,  G06N20/00

    Abstract: An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.

    Method and System for Capturing Data of Actions
    7.
    发明申请
    Method and System for Capturing Data of Actions 有权

    公开(公告)号：US20230056392A1

    公开(公告)日：2023-02-23

    申请号：US17893889

    申请日：2022-08-23

    Applicant: salesforce.com, inc.

    Inventor： Molly Mahar ,  Nicholas Beng Tek Geh

    IPC: H04M3/42 ,  H04L51/02 ,  H04L51/046 ,  H04M3/53 ,  H04M3/523 ,  H04L51/234

    Abstract: Described herein is a system and method for capturing data associated with actions attempted by an automated agent. The system described herein captures data associated with the actions attempted by an automated agent during the messaging session between an automated agent and the user and present a summary of the actions in a messaging platform. In an embodiment, the automated agent uploads data associated with actions attempted during the messaging session to a server. The server captures the data associated with the actions and generates a description of each action that was attempted. The server generates a summary including the description of each action. The summary of the actions are rendered in the messaging platform.

    Ranking Search Results using Machine Learning Based Models
    8.
    发明申请
    Ranking Search Results using Machine Learning Based Models 审中-公开

    公开(公告)号：US20180101617A1

    公开(公告)日：2018-04-12

    申请号：US15730591

    申请日：2017-10-11

    Applicant: salesforce.com, inc.

    Inventor： Jayesh Govindarajan ,  Nicholas Beng Tek Geh ,  Francisco Borges ,  Ammar Haris

    IPC: G06F17/30 ,  G06N99/00 ,  H04L29/08

    CPC classification number: G06F16/9535 ,  G06N20/00 ,  H04L67/02 ,  H04L67/306

    Abstract: An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.

    RANKING SEARCH RESULTS USING HIERARCHICALLY ORGANIZED COEFFICIENTS FOR DETERMINING RELEVANCE
    9.
    发明申请
    RANKING SEARCH RESULTS USING HIERARCHICALLY ORGANIZED COEFFICIENTS FOR DETERMINING RELEVANCE 审中-公开

    公开(公告)号：US20180101536A1

    公开(公告)日：2018-04-12

    申请号：US15728938

    申请日：2017-10-10

    Applicant: salesforce.com, inc.

    Inventor： Jayesh Govindarajan ,  Nicholas Beng Tek Geh ,  Ammar Haris

    IPC: G06F17/30

    CPC classification number: G06F16/24578 ,  G06F16/2228 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/9535

    Abstract: An online system receives a search query from a user. In response to the request, the online system obtains search results matching the search query and identifies a set of attributes describing a context of the search query. The online system generates a data structure that includes a plurality of search coefficients. The search coefficients are selected based on the identified set of attributes. Some of the search coefficients have conflicting values. The online system traverses the data structure to identify the search coefficients having conflicting values. For each search coefficient having conflicting values, the online system resolves conflicts and determines a value of the search coefficient. The online system ranks search results based on the resolved values of the search coefficients.

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