Oracle International Corporation

Manish Parekh

Vishal Vishnoi

Srinivasa Phani Kumar Gadde

Mark Edward Johnson

Thanh Long Duong

Elias Luqman Jalaluddin

Crystal C Pan

Cong Duy Vu Hoang

Gautam Singaraju

Poorya Zaremoodi

Vladislav Blinov

Yu-Heng Hong

Ying Xu

Xin Xu

Yakupitiyage Don Thanuja Samodhye Dharmasiri

Thanh Tien Vu

Manmohit Rekhi

Steven Martijn Davelaar

Balakota Srinivas Vinnakota

King-Hwa Lee

Muruganantham Chinnananchi

Saba Amsalu Teserra

Jae Min John

Stephen Andrew McRitchie

Fen Wang

Alexander Wang

The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Composite entity for rule driven acquisition of input data to chatbots

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

Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.

Automatic out of scope transition for chatbot

Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Distance-based logit value for natural language processing

A chatbot system is configured to execute code to perform determining, by the chatbot system, a classification result for an utterance and one or more anchors each anchor of the one or more anchors corresponding to one or more anchor words of the utterance. For each anchor of the one or more anchors, one or more synthetic utterances are generated, and one or more classification results for the one or more synthetic utterances are determined. A report is generated by the chatbot system comprising a representation of a particular anchor of the one or more anchors, the particular anchor corresponding to a highest confidence value among the one or more anchors. The one or more synthetic utterances may be used to generate a new training dataset for training a machine-learning model. The training dataset may be refined according to a threshold confidence values to filter out datasets for training.

Techniques for providing explanations for text classification

Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Enhanced logits for natural language processing

Techniques for described for invoking a chatbot in a chatbot system, in response to an event notification from a software application. The event notification can be sent to the chatbot system based on the software application determining that one or more conditions associated with an event are satisfied. In certain embodiments, the event notification contains information indicating a dialog flow state for starting a new conversation between a particular chatbot and a user. The event notification can also identify the user and/or the particular chatbot. In some instances, a prompt is output to the user requesting the user to confirm a start of the new conversation. Whether the prompt is output or not can depend on whether there is an existing conversation between the user and a chatbot in the chatbot system.

Application initiated conversations for chatbots

Growing community of inventors

San Jose, CA, United States of America

Manish Parekh