公开(公告)号：US20240070734A1

公开(公告)日：2024-02-29

申请号：US18388013

申请日：2023-11-08

Applicant: eBAY Inc.

Inventor： Tomer Lancewicki ,  Ramesh Periyathambi

IPC: G06Q30/0283 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0601

CPC classification number: G06Q30/0283 ,  G06N20/00 ,  G06Q30/0206 ,  G06Q30/0619

Abstract: A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

公开(公告)号：US11875241B2

公开(公告)日：2024-01-16

申请号：US17462465

申请日：2021-08-31

Applicant: eBay Inc.

Inventor： Farah Abdallah ,  Robert Enyedi ,  Amit Srivastava ,  Elaine Lee ,  Braddock Craig Gaskill ,  Tomer Lancewicki ,  Xinyu Zhang ,  Jayanth Vasudevan ,  Dominique Jean Bouchon

IPC: G06N3/006 ,  G06F16/50 ,  G06Q30/0601 ,  G06Q30/0251 ,  G06F16/9032 ,  G06Q10/10 ,  G06F40/30 ,  G06N20/00 ,  G06F16/248

CPC classification number: G06N3/006 ,  G06F16/248 ,  G06F16/50 ,  G06F16/90332 ,  G06F40/30 ,  G06N20/00 ,  G06Q10/10 ,  G06Q30/0256 ,  G06Q30/0601 ,  G06Q30/0625

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

公开(公告)号：US20220207562A1

公开(公告)日：2022-06-30

申请号：US17700011

申请日：2022-03-21

Applicant: eBay Inc.

Inventor： Ramesh Periyathambi ,  Manojkumar Rangasamy Kannadasan ,  Lakshimi Duraivenkatesh ,  Vineet Bindal ,  Selcuk Kopru ,  Tomer Lancewicki

IPC: G06Q30/02 ,  G06N5/04 ,  G06N20/00

Abstract: Techniques for prefetching operation cost based digital content and digital content with emphasis that overcome the challenges of conventional systems are described. In one example, a computing device may receive digital content representations of digital content from a service provider system, which are displayed on a user interface of the computing device. Thereafter, the computing device may also receive digital content as prefetches having a changed display characteristic as emphasizing a portion of the digital content based on a model trained using machine learning. Alternatively, the computing device may receive digital content as a prefetch based on a model trained using machine learning in which the model addresses a likelihood of conversion of a good or service and an operation cost of providing the digital content. Upon receiving a user input selecting one of the digital content representations, digital content is rendered in the user interface of the computing device.

公开(公告)号：US20220191267A1

公开(公告)日：2022-06-16

申请号：US17513793

申请日：2021-10-28

Applicant: eBay Inc.

Inventor： Vineet Bindal ,  Naga Sita Raghuram Nimishakavi Venkata ,  Ramesh Periyathambi ,  Lakshimi Duraivenkatesh ,  Tomer Lancewicki ,  Selcuk Kopru

IPC: H04L29/08 ,  H04L29/06 ,  G06F16/95

Abstract: Systems and methods for processing webpage calls via multiple module responses are described. A system may receive, from a client device, a first call for module data associated with a set of webpage modules for presentation in a webpage. The system may subsequently transmit, to the client device based on receiving the first call, a first response including first module data associated with a first subset of the set of webpage modules. The first response may additionally include a token identifying the webpage. The server may additionally transmit, to the client device based on transmitting the first response, a second response including the token identifying the webpage and second module data associated with a second subset of the set of webpage modules that differs from the first subset of the set of webpage modules.

公开(公告)号：US20210390365A1

公开(公告)日：2021-12-16

申请号：US17462465

申请日：2021-08-31

Applicant: eBay Inc.

Inventor： Farah Abdallah ,  Robert Enyedi ,  Amit Srivastava ,  Elaine Lee ,  Braddock Craig Gaskill ,  Tomer Lancewicki ,  Xinyu Zhang ,  Jayanth Vasudevan ,  Dominique Jean Bouchon

IPC: G06N3/00 ,  G06F16/50 ,  G06Q30/06 ,  G06Q30/02 ,  G06F16/9032 ,  G06Q10/10 ,  G06F40/30 ,  G06N20/00 ,  G06F16/248

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

公开(公告)号：US20240332292A1

公开(公告)日：2024-10-03

申请号：US18744331

申请日：2024-06-14

Applicant: eBay Inc.

Inventor： Vineet BINDAL ,  Naga Sita Raghuram Nimishakavi Venkata ,  Ramesh Periyathambi ,  Lakshimi Duraivenkatesh ,  Tomer Lancewicki ,  Selcuk Kopru

IPC: H01L27/088 ,  H01L21/8234 ,  H01L21/84 ,  H01L23/528 ,  H01L27/02 ,  H01L27/118 ,  H01L27/12

CPC classification number: H01L27/0886 ,  H01L21/823431 ,  H01L21/845 ,  H01L23/528 ,  H01L27/0207 ,  H01L27/11807 ,  H01L27/1211 ,  H01L2924/0002

Abstract: Systems and methods for processing webpage calls via multiple module responses are described. A system may receive a first call and a second call for module data associated with a plurality of webpage modules for presentation of a webpage at a client device; categorize the plurality of webpage modules according to historical data associated with the client device; responsive to the first call, transmit to the client device a first response comprising a first webpage module corresponding to a first category; and responsive to the second call, transmit to the client device a second response comprising a second webpage module corresponding to a second category, where the first webpage module is transmitted in the first response and the second webpage module is transmitted in the second response based on the categorization.

公开(公告)号：US20240095490A1

公开(公告)日：2024-03-21

申请号：US18523674

申请日：2023-11-29

Applicant: eBay Inc.

Inventor： Farah Abdallah ,  Robert Enyedi ,  Amit Srivastava ,  Elaine Lee ,  Braddock Craig Gaskill ,  Tomer Lancewicki ,  Xinyu Zhang ,  Jayanth Vasudevan ,  Dominique Jean Bouchon

IPC: G06N3/006 ,  G06F16/248 ,  G06F16/50 ,  G06F16/9032 ,  G06F40/30 ,  G06N20/00 ,  G06Q10/10 ,  G06Q30/0251 ,  G06Q30/0601

CPC classification number: G06N3/006 ,  G06F16/248 ,  G06F16/50 ,  G06F16/90332 ,  G06F40/30 ,  G06N20/00 ,  G06Q10/10 ,  G06Q30/0256 ,  G06Q30/0601 ,  G06Q30/0625

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data is then received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

公开(公告)号：US11803896B2

公开(公告)日：2023-10-31

申请号：US16821583

申请日：2020-03-17

Applicant: eBay Inc.

Inventor： Ramesh Periyathambi ,  Tomer Lancewicki ,  Kishore Kumar Mohan ,  Lakshimi Duraivenkatesh ,  Selcuk Kopru

IPC: G06Q30/0601 ,  G06N20/00 ,  G06N5/04 ,  G06F16/9535 ,  H04L67/60 ,  G06F18/22 ,  G06F18/24 ,  G06F18/2113 ,  G06V10/75 ,  G06V10/764 ,  G06F3/0482

CPC classification number: G06Q30/0643 ,  G06F16/9535 ,  G06F18/2113 ,  G06F18/22 ,  G06F18/24 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0629 ,  G06V10/75 ,  G06V10/764 ,  H04L67/60 ,  G06F3/0482

Abstract: Methods for determining which image of a set of images to present in a search results page for a product are described. Components of a server system may receive a set of images for a set of items associated with a product. Components of the server system may perform image ranking to rank the set of images to identify a representative image of the set of images for the product, based on a user interaction metric of each image of the set of images. The components of the server system may then receive, from a user device, a search query that may be mapped to the product, and the component of the server system may transmit, to the user device, the search results page that includes at least one item of the set of items and the representative image based on the interaction metric of the representative image.

公开(公告)号：US11436655B2

公开(公告)日：2022-09-06

申请号：US16590018

申请日：2019-10-01

Applicant: eBay Inc.

Inventor： Ramesh Periyathambi ,  Tomer Lancewicki ,  Sai Vipin Siripurapu ,  Lakshimi Duraivenkatesh ,  Selcuk Kopru

IPC: G06Q30/06 ,  G06N20/00 ,  G06Q30/08

Abstract: Different action user-interface components in a comparison view are described. Initially, a selection is received to display a comparison view via a user interface of a listing platform. Multiple listings of the listing platform are selected for inclusion in the comparison view. A comparison view system determines which action of a plurality of actions, used by the listing platform, to associate with each of the listings. A display device displays the multiple listings concurrently in a comparison view via a user interface of the listing platform and also displays an action user-interface component (e.g., a button) in each of the plurality of listings. The action user-interface component is selectable to initiate the action associated with the respective listing. In accordance with the described techniques, the action user-interface component displayed in at least two of the multiple listings is selectable to initiate different actions in relation to the respective listing.

公开(公告)号：US11144811B2

公开(公告)日：2021-10-12

申请号：US15859239

申请日：2017-12-29

Applicant: eBay Inc.

Inventor： Farah Abdallah ,  Robert Enyedi ,  Amit Srivastava ,  Elaine Lee ,  Braddock Craig Gaskill ,  Tomer Lancewicki ,  Xinyu Zhang ,  Jayanth Vasudevan ,  Dominique Jean Bouchon

IPC: G06F3/048 ,  G06N3/00 ,  G06F16/50 ,  G06Q30/06 ,  G06Q30/02 ,  G06F16/9032 ,  G06Q10/10 ,  G06F40/30 ,  G06N20/00 ,  G06F16/248

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.