Paypal, Inc.

Van Hoang Nguyen

Quan Jin Ferdinand Tang

Yuzhen Zhuo

Sandro Cavallari

Hewen Wang

Kim Dung Bui

Francesco Gelli

Gautam Vasappanavara

George Chen Kaidi

Chun Kiat Ho

Rushik Navinbhai Upadhyay

Zhe Chen

Li Hua Lim

Rey Neo

Harsha Singalreddy

Quan Anh Nguyen

Amit Nahata

Lei Xu

Phoram Kirtikumar Mehta

Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.

Automated database query generation and analysis

Systems/techniques for facilitating context-enhanced category classification are provided. In various embodiments, a system can access a first textual description of a product or service. In various aspects, the system can identify, via execution of named entity recognition, one or more keywords in the first textual description. In various instances, the system can access, from a set of queryable databases, one or more second textual descriptions that respectively correspond to the one or more keywords. In various cases, the system can generate, via execution of word embedding, a first numerical representation of the first textual description and one or more second numerical representations of the one or more second textual descriptions. In various aspects, the system can identify, via execution of a machine learning classifier, a category label for the product or service, based on the first numerical representation and the one or more second numerical representations.

Context-enhanced category classification to generate product category labels used to conduct electronic transactions based on regulatory restrictions

Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Systems and methods for formatting informal utterances

Methods and systems are presented for generating and using a machine learning model configured to perform cross-domain named entity recognition. The machine learning model is generated to accept a sentence associated with a target domain and to predict, for at least a word in the sentence, a corresponding entity associated with the target domain, without having been trained using training data associated with the target domain. In particular, the machine learning model is trained using only training data associated with a source domain. Based on derived relationships between entities associated with the source domain and entities associated with the target domain, the machine learning model is configured to transfer knowledge associated with the source domain to the target domain such that the machine learning model can map words within a sentence to entities associated with the target domain.

Cross-domain named entity recognition using knowledge graph

Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Systems and methods for determining a response to a user query

Growing community of inventors

Singapore, Singapore

Van Hoang Nguyen