Ebay, Incorporated

Paypal, Inc.

Aixplain, Inc.

Amazon Technologies, Inc.

Apptek, Inc.

Other

A9.com, Inc.

Hassan Sawaf

Jean-David Ruvini

Sanjika Hewavitharana

Robinson Piramuthu

Sunil Mohan

Evgeny Matusov

Mudar Yaghi

Smruthi Mukund

Selcuk Kopru

Derek Barnes

Marc Delingat

Yoram Vardi

Alex Cozzi

Mohamed Elbadrashiny

Thiago Castro Ferreira

Mohammad Shihadah

Kiran Reddy Nagarur

Peng Zhang

Rashmi Gangadharaiah

Ritwik Giri

Andrew Paul Breen

Fady El-Rukby

Katrin Kirchhoff

Arvindh Krishnaswamy

Yaser Al-Onaizan

Mehmet Umut Isik

Roger Scott Jenke

Roberto Barra-Chicote

Kalpesh N Sutaria

Marcello Federico

Jintao Jiang

Robert Enyedi

Arshit Gupta

Garima Lalwani

Mona Diab

Adel A Youssef

Marios Anapliotis

Kamer Ali Yuksel

Amro Shihadah

Lucas Aguiar Pavanelli

Nur Al-huda Anwer Hamdan

Jinato Jiang

The present disclosure relates to a system and method for generating an outcome in response to a user query using an AI agent controller equipped with a large language model. The AI agent controller receives the user query, determines a sequence of tasks required to achieve the outcome, and communicates the sequence to a Central Agent Name and Attribute Registry (CANAR) containing a repository of registered AI agents. The CANAR identifies and obtains responses from AI agents capable of executing the tasks. The AI agent controller then coordinates the execution of these tasks by the selected AI agents to generate the specified outcome. The system further includes functionalities for validating user queries, cataloging AI agents, and dynamically prioritizing tasks based on user preferences or urgency. Additionally, the system supports real-time updates of the AI agent repository and task distribution based on geographical proximity, ensuring efficient and accurate task execution.

System and method for managing AI agents using central agent name and attribute registry

A method for facilitating performing of tasks optimally using software applications. The method includes receiving, using a communication device, task data from a user device, analyzing, using a processing device, the task data, extracting, using the processing device, a feature associated with the task data, analyzing, using the processing device, the feature using a machine learning model, training the machine learning model for predicting a performance score associated with each of software applications capable of performing a task, selecting, using the processing device, a software application from the software applications based on the performance score, generating, using the processing device, a recommendation of the software application based on the selecting, and transmitting, using the communication device, the recommendation and the output of the chosen software application based on the task input data, to the user device.

System and method for facilitating performing of tasks optimally using software applications

A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method further includes receiving information associated with a requested operator, and generating, by a processing device executing a first machine learning model, a skeleton architecture of an AI-based solution to the operator based on the information.

Machine learning model generator

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

Techniques are described for a contextual natural language understanding (cNLU) framework that is able to incorporate contextual signals of variable history length to perform joint intent classification (IC) and slot labeling (SL) tasks. A user utterance provided by a user within a multi-turn chat dialog between the user and a conversational agent is received. The user utterance and contextual information associated with one or more previous turns of the multi-turn chat dialog is provided to a machine learning (ML) model. An intent classification and one or more slot labels for the user utterance are then obtained from the ML model. The cNLU framework described herein thus uses, in addition to a current utterance itself, various contextual signals as input to a model to generate IC and SL predictions for each utterance of a multi-turn chat dialog.

Contextual natural language understanding for conversational agents

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

A method of propagating annotations of content items to a search query is disclosed. A strength of a correspondence between a search query and a listing of an item on a network-based publication system is determined. The strength of the correspondence is based on an analysis of a set of actions by a set of users who submitted the search query. A set of annotations is generated. The set of annotations is propagated to a search engine and used to enhance search results.

Method for performing sequence labelling on queries

Techniques for the generation of dubbed audio for an audio/video are described. An exemplary approach is to receive a request to generate dubbed speech for an audio/visual file; and in response to the request to: extract speech segments from an audio track of the audio/visual file associated with identified speakers; translate the extracted speech segments into a target language; determine a machine learning model per identified speaker, the trained machine learning models to be used to generate a spoken version of the translated, extracted speech segments based on the identified speaker; generate, per translated, extracted speech segment, a spoken version of the translated, extracted speech segments using a trained machine learning model that corresponds to the identified speaker of the translated, extracted speech segment and prosody information for the extracted speech segments; and replace the extracted speech segments from the audio track of the audio/visual file with the spoken versions spoken version of the translated, extracted speech segments to generate a modified audio track.

Multilingual speech translation with adaptive speech synthesis and adaptive physiognomy

In a flow of computer actions, a computer system () receives a request involving a machine translation. In performing the translation (), or in using the translation in subsequent computer operations (), the computer system takes into account known statistical relationships (), obtained from previously accumulated click-through data (), between a machine translation performed in a flow, the flow's portions preceding the translation, and success indicators pertaining to the flow's portion following the translation. The statistical relationships are derived by data mining of the click-through data. Further, normal actions can be suspended to use a random option to accumulate the click-through data and/or perform statistical AB testing. Other features are also provided.

Use of statistical flow data for machine translations between different languages

A semantic analysis can be performed to determine an intent of a received query. The intent can relate to a primary object of the query, which can be identified through the semantic analysis. Other attributes can be determined from the query that help to focus the object of the intent. A query vector is generated, based on the intent and primary object, and used to search a multi-dimensional semantic space including semantic representations of possible matches. The attributes are used to adjust the query vector in the semantic space. Objects having vectors ending proximate the query vector are identified as potential search results, with the distance from the query vector being used as a ranking mechanism. If refinement is needed, a dialog is used to obtain additional information from the user. Once results are obtained with sufficient confidence, results can be returned as search results for the query.

Semantic modeling for search

Information is provided a user of a mobile device. Media content is sensed by the mobile device and sent to an information processing server. The information processing server also obtains media content from another source and associates at least a portion of the obtained media content with a product or service offer. The sensed media content is correlated to the obtained media content such that an associated buy or service offer is selected. The buy or service offer is sent to the mobile device for display to the user.

Systems and methods for presentation and analysis of media content

A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

Machine translation optimization for queries executed in a different language

A method of forming parallel corpora comprises receiving sets of items in first language and second languages, each of the sets having one or more associated descriptions and metadata. The metadata is collected from the two sets of items and are aligned using the metadata. The aligned metadata are mapped from the first language to the second language for each of the sets. The descriptions of two items are fetched and the structural similarity of the descriptions is measured to assess whether two items are likely to be translations of each other. For mapped items with structurally similar descriptions, the mapped item descriptions are formed into respective sentences in first language and in the second language. The sentences are parallel corpora which may be used to translate an item from the first language to the second language, and also to train a machine translation system.

Methods for automatic generation of parallel corpora

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Los Gatos, CA, United States of America

Hassan Sawaf