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    HIERARCHICAL NEURAL NETWORK BASED IMPLEMENTATION FOR PREDICTING OUT OF STOCK PRODUCTS
    1.
    发明公开
    HIERARCHICAL NEURAL NETWORK BASED IMPLEMENTATION FOR PREDICTING OUT OF STOCK PRODUCTS 审中-公开

    公开(公告)号：US20230267481A1

    公开(公告)日：2023-08-24

    申请号：US17675817

    申请日：2022-02-18

    Applicant: salesforce.com, inc.

    Inventor： Akash Singh ,  Rajdeep Dua

    IPC: G06Q30/02 ,  G06N3/04 ,  G06Q10/08

    CPC classification number: G06Q30/0202 ,  G06N3/0445 ,  G06Q10/087

    Abstract: A hierarchical neural network for predicting out of stock products comprises an input layer that receives data from data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in stores of a retailer. A first level of neural networks processes the data from the data sources into respective learned intermediate vector representations. A second level comprises a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation. A third level comprises a feed forward network that receives the combined vector representation and outputs to the retailer an out of stock probability indicating which store and product combinations are likely to have out of stock products over a predetermined timeframe.

    Selection of object recognition models for computer vision
    2.
    发明授权
    Selection of object recognition models for computer vision 有权

    公开(公告)号：US11625929B2

    公开(公告)日：2023-04-11

    申请号：US16884233

    申请日：2020-05-27

    Applicant: salesforce.com, inc.

    Inventor： Rajdeep Dua ,  Mani Kandar Madduri ,  Nutana Sukumar Reddy Muramreddy ,  Piyush Singh

    IPC: G06V20/64 ,  G06T7/00 ,  G06V10/44

    Abstract: Disclosed herein are system, method, and computer program product embodiments for compliance auditing using cloud based computer vision. In one aspect, a system is configured to receive, from a mobile device, a compliance audit request to at least recognize one or more products within the audit image. The system is further configured to select a first object recognition model having a first associated object recognition model identifier from a model selection list based at least on a required object recognition list, wherein the first object recognition model is configured to recognize a first set of object names within the required object recognition list. The system is further configured to request the computer vision system to perform object recognition using the first object recognition model to recognize the first set of object names within the audit image, and transmit audit result information to the mobile device.

    Machine learning based models for object recognition
    3.
    发明授权
    Machine learning based models for object recognition 有权

    公开(公告)号：US11210562B2

    公开(公告)日：2021-12-28

    申请号：US16751078

    申请日：2020-01-23

    Applicant: salesforce.com, inc.

    Inventor： Joy Mustafi ,  Lakshya Kumar ,  Rajdeep Dua ,  Machiraju Pakasasana Rama Rao

    IPC: G06K9/62 ,  G06K9/20 ,  G06K9/00 ,  G06K9/46

    Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

    Automatic rule generation for recommendation engine using hybrid machine learning
    4.
    发明授权
    Automatic rule generation for recommendation engine using hybrid machine learning 有权

    公开(公告)号：US10922725B2

    公开(公告)日：2021-02-16

    申请号：US16264148

    申请日：2019-01-31

    Applicant: salesforce.com, inc.

    Inventor： Joy Mustafi ,  Rajdeep Dua

    IPC: G06Q30/02 ,  G06K9/62 ,  G06N20/00

    Abstract: The system and methods of the disclosed subject matter provide a hybrid machine learning approach for recommending items that a consumer should be shown as a next best offer. The recommendation may be based on the consumer's previous behavior, other consumers' previous behavior, and the consumer's profile. The system and methods may cluster an input dataset using an unsupervised clustering engine. The dataset output from the unsupervised clustering engine may be subsequently provided to the input of a supervised machine learning engine to generate a rules-based model. The system and methods may use the rules-based model to subsequently cluster new user data and generate recommendations based on the user's assigned cluster.

    MACHINE LEARNING BASED MODELS FOR OBJECT RECOGNITION
    6.
    发明申请
    MACHINE LEARNING BASED MODELS FOR OBJECT RECOGNITION 有权

    公开(公告)号：US20210150273A1

    公开(公告)日：2021-05-20

    申请号：US16751078

    申请日：2020-01-23

    Applicant: salesforce.com, inc.

    Inventor： Joy Mustafi ,  Lakshya Kumar ,  Rajdeep Dua ,  Machiraju Pakasasana Rama Rao

    IPC: G06K9/62 ,  G06K9/20 ,  G06K9/00 ,  G06K9/46

    Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

    MACHINE LEARNING BASED MODELS FOR OBJECT RECOGNITION
    8.
    发明申请
    MACHINE LEARNING BASED MODELS FOR OBJECT RECOGNITION 有权

    公开(公告)号：US20220114394A1

    公开(公告)日：2022-04-14

    申请号：US17558416

    申请日：2021-12-21

    Applicant: salesforce.com, inc.

    Inventor： Joy Mustafi ,  Lakshya Kumar ,  Rajdeep Dua ,  Machiraju Pakasasana Rama Rao

    IPC: G06K9/62 ,  G06V10/22 ,  G06V10/40 ,  G06V20/30

    Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

    AUTOMATIC RULE GENERATION FOR RECOMMENDATION ENGINE USING HYBRID MACHINE LEARNING
    9.
    发明申请
    AUTOMATIC RULE GENERATION FOR RECOMMENDATION ENGINE USING HYBRID MACHINE LEARNING 审中-公开

    公开(公告)号：US20200250715A1

    公开(公告)日：2020-08-06

    申请号：US16264148

    申请日：2019-01-31

    Applicant: salesforce.com, inc.

    Inventor： Joy Mustafi ,  Rajdeep Dua

    IPC: G06Q30/02 ,  G06N20/00 ,  G06K9/62

    Abstract: The system and methods of the disclosed subject matter provide a hybrid machine learning approach for recommending items that a consumer should be shown as a next best offer. The recommendation may be based on the consumer's previous behavior, other consumers' previous behavior, and the consumer's profile. The system and methods may cluster an input dataset using an unsupervised clustering engine. The dataset output from the unsupervised clustering engine may be subsequently provided to the input of a supervised machine learning engine to generate a rules-based model. The system and methods may use the rules-based model to subsequently cluster new user data and generate recommendations based on the user's assigned cluster.

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