Oracle International Corporation

Oracle International Corportion

Jae Min John

Vishal Vishnoi

Srinivasa Phani Kumar Gadde

Saba Amsalu Teserra

Gautam Singaraju

Manish Parekh

Crystal C Pan

Muruganantham Chinnananchi

Stephen Andrew McRitchie

Fen Wang

Mark Edward Johnson

Thanh Long Duong

Xin Xu

Cong Duy Vu Hoang

Balakota Srinivas Vinnakota

Yakupitiyage Don Thanuja Samodhye Dharmasiri

Aashna Devang Kanuga

Shivashankar Subramanian

Gioacchino Tangari

Nitika Mathur

Philip Arthur

Steve Wai-Chun Siu

Techniques for preprocessing data assets to be used in a natural language to logical form model based on scalable search and content-based schema linking. In one particular aspect, a method includes accessing an utterance, classifying named entities within the utterance into predefined classes, searching value lists within the database schema using tokens from the utterance to identify and output value matches including: (i) any value within the value lists that matches a token from the utterance and (ii) any attribute associated with a matching value, generating a data structure by organizing and storing: (i) each of the named entities and an assigned class for each of the named entities, (ii) each of the value matches and the token matching each of the value matches, and (iii) the utterance, in a predefined format for the data structure, and outputting the data structure.

Transforming natural language to structured query language based on scalable search and content-based schema linking

Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Routing for chatbots

The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an explicit invocation of a chatbot and determining an input for the chatbot being invoked. In certain embodiments, explicit invocation analysis involves detecting an invocation name in an utterance. The invocation name is an identifier assigned to a particular chatbot. In response to detection of the invocation name, the utterance is refined for input to the particular chatbot by determining which parts of the utterance, if any, contain relevant information for the particular chatbot and generating a new utterance, using the relevant parts of the utterance, for processing by the particular chatbot. The refining can involve removal of a portion of the utterance associated with the invocation name.

Handling explicit invocation of chatbots

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Redwood City, CA, United States of America

Jae Min John