Ebay, Incorporated

Sanjika Hewavitharana

Hassan Sawaf

Ajinkya Gorakhnath Kale

Robinson Piramuthu

Amit Srivastava

Evgeny Matusov

Selcuk Kopru

Stefan Schoenmackers

Lawrence William Colagiovanni

Brandon An Lai

Don Kumudu Janaka Ranatunga

Canran Xu

Jason Diran

Marie Michelle Rhea Foster

Vinh Khuc

Ramesh Periyathambi

Tomer Lancewicki

Senthil Kumar Padmanabhan

Ryan Reeves

Thrivikrama Taula

Baohao Liao

Daniel Stein

Sivan Elkis

Simon Alexander Becker

Saral Bharathi Sukumar Jeyaseelan

A system for assisting users in listing items for sale in an electronic marketplace is disclosed. A video is received from a user device associated with a user, the video including a video stream depicting a plurality of items to be listed for sale in the electronic marketplace. Respective images depicting respective items among the plurality of items are obtained from the video stream, and respective attributes of the respective items among the plurality of items are extracted from the video. Respective listings for sale of the respective items are generated based at least in part on the respective attributes of the respective items among the plurality of items, and the respective listings for sale of the respective items are displayed to the user.

Systems, method, and computer storage medium for creating listing for items for sale in an electronic marketplace based on video analysis

Methods, systems, and computer programs are presented for detecting a mission changes in a conversation. A user utterance from a user device is received. The user utterance is part of a conversation with an intelligent assistant. The conversation includes preceding user utterances in pursuit of a first mission. It is determined that the user utterance indicates a mission change from the first mission to a second mission based on an application of a machine-learned model to the user utterance and the preceding user utterances. The machine-learned model has been trained repeatedly with past utterances of other users over a time period, the determining based on a certainty of the indication satisfying a certainty threshold. Responsive to the determining that the user utterance indicates the mission change from the first mission to a second mission, a reply to the user utterance is generated to further the second mission rather than the first mission.

Detection of mission change in conversation

Disclosed are systems and methods for receiving a plurality of comments at a particular phase of a transaction with a member of a networked system, classifying one or more of the plurality of comments into one of a set of predetermined sentiment classifications, applying a trained machine learning system to select a category from a set of predefined categories for each of the one or more comments, applying a natural language processing module to generate a sub-category for each of the one or more comments, associating the generated sub-categories with their respective categories for the one or more comments, and generating a display of the determined categories for the particular transaction with the generated sub-categories, each generated sub-category being graphically connected to their respective categories.

Processing transactional feedback

In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature. Based on a similarity score of the first and second feature vectors at least meeting a similarity score threshold, the Listing Engine generates a pairing of the first listing in the first language with the second listing in the second language for inclusion in training data of a machine translation system.

Detecting cross-lingual comparable listings

A query segmentation system segments a search query into a query segment that identifies search results from items stored on a database. The query segmentation system receives a query string, and the query string comprises a plurality of tokens. A first token and a second token are identified from the plurality of tokens, and a first vector and a second vector associated with the first token and the second token respectively are determined. The query segmentation system determines whether to include the first and second tokens in a single query segment based on the first and second vectors. The single query segment is processed to identify the search results from the database.

Methods and systems for query segmentation

According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.

Dynamic topic adaptation for machine translation using user session context

In various example embodiments, a system and method for applying a quantitative range for qualitative terms. In one example, a method includes gathering user interactions, identifying a qualitative term included in the user interactions, building an electronic knowledge graph that associates the qualitative term with a quantitative range for the product identified using the qualitative term according to values in the user interactions that include the qualitative term, receiving a query for the product that includes the qualitative term, and performing, in response to receiving the query, a search that limits results according to the quantitative range stored in the knowledge graph.

Applying a quantitative range for qualitative terms

Method, medium, and system for detecting cross-lingual comparable listings for machine translation using image similarity

Growing community of inventors

Milpitas, CA, United States of America

Sanjika Hewavitharana