公开(公告)号：US20210365450A1

公开(公告)日：2021-11-25

申请号：US17398755

申请日：2021-08-10

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi

IPC: G06F16/2453 ,  G06F16/25 ,  G06K9/72 ,  G06F16/28

Abstract: Embodiments described herein transforms a complex and usually unstructured table to a relational table based on the header pattern. Specifically, the original complex table is expanded into a single dimensional relational database format, in which each cell corresponds to one or more corresponding categories or subcategories from the original header. The transformed one-dimensional relational table is then populated with the corresponding cell values from the original table. In this way, data from the original complex and unstructured data table can be stored at a relational database.

公开(公告)号：US20200349736A1

公开(公告)日：2020-11-05

申请号：US16401695

申请日：2019-05-02

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi ,  Lakshya Kumar ,  Rajdeep Singh Dua

IPC: G06T7/73 ,  G06N3/04 ,  G06T11/20

Abstract: A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

公开(公告)号：US11347733B2

公开(公告)日：2022-05-31

申请号：US16536169

申请日：2019-08-08

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi

IPC: G06F17/00 ,  G06F16/2452 ,  G06F16/28 ,  G06N3/02 ,  G06N5/04 ,  G06F40/205 ,  G06F40/289 ,  G06V30/413

Abstract: Embodiments described herein automatically classifies numerical expressions from a textual document and designates a context to understand each numerical expression. Specifically, numerical expressions from a textual context are classified as nominal or cardinal. For cardinal numerical expressions that carry a quantitative meaning, inference terms are determined from the textual context to associate with the cardinal numerical expressions. The numerical expressions are then translated to a format of a numerical value and stored with metadata indicating the unit scale or the meaning of the numerical value.

公开(公告)号：US11243948B2

公开(公告)日：2022-02-08

申请号：US16536172

申请日：2019-08-08

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi

IPC: G06F16/2452 ,  G06F16/242 ,  G06F16/22 ,  G06F7/14 ,  G06F16/28

Abstract: Embodiments described herein provide a mechanism that translates a natural language question to a database query format that may be applied to a data table to generate an answer to the natural language question. The system may identify key terms from a natural language question and classify the key terms as variable names or operation names. The natural language question is than translated into a format of question template containing variable names and operation names. In this way, the system may map the template question to a database query which can be applied to operate on a relational database to identify a cell value that represents an answer to the natural language question.

公开(公告)号：US11037099B2

公开(公告)日：2021-06-15

申请号：US16436518

申请日：2019-06-10

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi ,  Lakshya Kumar ,  Rajdeep Singh Dua

IPC: G06K9/34 ,  G06Q10/08 ,  G06F16/583 ,  G06F16/953 ,  G06K9/62 ,  G06T7/70 ,  G06K9/03 ,  G06K9/32

Abstract: Embodiments described herein provide a method for obtaining information on product inventory and placement in a retail setting. An image including unannotated image data indicative of the retail setting is received. One or more shelves in the retail setting are determined from the unannotated image data, and the image is segmented into one or more sub-images corresponding to the one or more detected shelves. For each sub-image corresponding to a respective detected shelf, a product name is then and a number of appearances of the product name are detected using text recognition on the respective sub-image. Product inventory information and first placement information are derived based at least in part on the detected number of appearances and a shelf level corresponding to the sub-image.

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.

公开(公告)号：US20210042307A1

公开(公告)日：2021-02-11

申请号：US16536169

申请日：2019-08-08

Applicant: salesforce.com, inc.

Inventor： Joy Mustafi

IPC: G06F16/2452 ,  G06F16/28 ,  G06K9/00 ,  G06F17/27 ,  G06N5/04 ,  G06N3/02

Abstract: Embodiments described herein automatically classifies numerical expressions from a textual document and designates a context to understand each numerical expression. Specifically, numerical expressions from a textual context are classified as nominal or cardinal. For cardinal numerical expressions that carry a quantitative meaning, inference terms are determined from the textual context to associate with the cardinal numerical expressions. The numerical expressions are then translated to a format of a numerical value and stored with metadata indicating the unit scale or the meaning of the numerical value.