公开(公告)号：US12249314B2

公开(公告)日：2025-03-11

申请号：US18136745

申请日：2023-04-19

Applicant: Oracle International Corporation

Inventor： Vishal Vishnoi ,  Xin Xu ,  Srinivasa Phani Kumar Gadde ,  Fen Wang ,  Muruganantham Chinnananchi ,  Manish Parekh ,  Stephen Andrew McRitchie ,  Jae Min John ,  Crystal C. Pan ,  Gautam Singaraju ,  Saba Amsalu Teserra

IPC: G06F17/00 ,  G06N5/04 ,  G06N20/00 ,  G10L13/08

Abstract: 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.

公开(公告)号：US12210842B2

公开(公告)日：2025-01-28

申请号：US18545621

申请日：2023-12-19

Applicant: Oracle International Corporation

Inventor： Ying Xu ,  Poorya Zaremoodi ,  Thanh Tien Vu ,  Cong Duy Vu Hoang ,  Vladislav Blinov ,  Yu-Heng Hong ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Vishal Vishnoi ,  Elias Luqman Jalaluddin ,  Manish Parekh ,  Thanh Long Duong ,  Mark Edward Johnson

IPC: G10L15/16 ,  G06F40/35 ,  G06N20/00 ,  H04L51/02 ,  G06F40/205 ,  G06F40/253

Abstract: 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.

公开(公告)号：US12106055B2

公开(公告)日：2024-10-01

申请号：US17407692

申请日：2021-08-20

Applicant: Oracle International Corporation

Inventor： Gautam Singaraju ,  Vishal Vishnoi ,  Manish Parekh ,  Alexander Wang

IPC: G06F40/35 ,  G06F40/205 ,  G06F40/40 ,  G10L15/18 ,  G10L15/22

CPC classification number: G06F40/35 ,  G06F40/40 ,  G06F40/205 ,  G10L15/1815 ,  G10L15/22

Abstract: 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.

公开(公告)号：US11283737B2

公开(公告)日：2022-03-22

申请号：US17234620

申请日：2021-04-19

Applicant: Oracle International Corporation

Inventor： Manish Parekh ,  King-Hwa Lee

IPC: H04L12/58 ,  H04L51/02 ,  H04L51/046 ,  H04L51/00 ,  H04L51/224

Abstract: 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.

公开(公告)号：US20200344186A1

公开(公告)日：2020-10-29

申请号：US16857766

申请日：2020-04-24

Applicant: Oracle International Corporation

Inventor： Manish Parekh ,  King-Hwa Lee

IPC: H04L12/58

Abstract: 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.

公开(公告)号：US20200342175A1

公开(公告)日：2020-10-29

申请号：US16857512

申请日：2020-04-24

Applicant: Oracle International Corporation

Inventor： Srinivasa Phani Kumar Gadde ,  Manish Parekh ,  Steven Martijn Davelaar ,  Manmohit Rekhi

IPC: G06F40/30 ,  G06F16/23 ,  H04L12/58 ,  G06F40/295

Abstract: 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.

公开(公告)号：US12019994B2

公开(公告)日：2024-06-25

申请号：US17456916

申请日：2021-11-30

Applicant: Oracle International Corporation

Inventor： Ying Xu ,  Poorya Zaremoodi ,  Thanh Tien Vu ,  Cong Duy Vu Hoang ,  Vladislav Blinov ,  Yu-Heng Hong ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Vishal Vishnoi ,  Elias Luqman Jalaluddin ,  Manish Parekh ,  Thanh Long Duong ,  Mark Edward Johnson

IPC: G06F17/18 ,  G06F40/35 ,  G06N20/00 ,  H04L51/02 ,  G06F40/205 ,  G06F40/253

CPC classification number: G06F40/35 ,  G06N20/00 ,  H04L51/02 ,  G06F40/205 ,  G06F40/253

Abstract: 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.

公开(公告)号：US11972220B2

公开(公告)日：2024-04-30

申请号：US17456687

申请日：2021-11-29

Applicant: Oracle International Corporation

Inventor： Ying Xu ,  Poorya Zaremoodi ,  Thanh Tien Vu ,  Cong Duy Vu Hoang ,  Vladislav Blinov ,  Yu-Heng Hong ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Vishal Vishnoi ,  Elias Luqman Jalaluddin ,  Manish Parekh ,  Thanh Long Duong ,  Mark Edward Johnson

IPC: G06F40/35 ,  G06F40/205 ,  G06F40/253 ,  G06N3/08 ,  H04L51/02

CPC classification number: G06F40/35 ,  G06N3/08 ,  H04L51/02 ,  G06F40/205 ,  G06F40/253

Abstract: 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.

公开(公告)号：US20230206004A1

公开(公告)日：2023-06-29

申请号：US18113594

申请日：2023-02-23

Applicant: Oracle International Corporation

Inventor： Srinivasa Phani Kumar Gadde ,  Manish Parekh ,  Steven Martjin Davelaar ,  Manmohit Rekhi

IPC: G06F40/30 ,  G06F40/295 ,  H04L67/10

CPC classification number: G06F40/30 ,  G06F40/295 ,  H04L67/10

Abstract: 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.

公开(公告)号：US20210304074A1

公开(公告)日：2021-09-30

申请号：US17216498

申请日：2021-03-29

Applicant: Oracle International Corporation

Inventor： Poorya Zaremoodi ,  Ying Xu ,  Thanh Tien Vu ,  Vladislav Blinov ,  Yu-Heng Hong ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Vishal Vishnoi ,  Elias Luqman Jalaluddin ,  Manish Parekh ,  Thanh Long Duong ,  Mark Edward Johnson ,  Xin Xu ,  Cong Duy Vu Hoang

IPC: G06N20/00

Abstract: Techniques are disclosed for tuning hyperparameters of a machine-learning model. A plurality of metrics are selected for which hyperparameters of the machine-learning model are to be tuned. Each metric is associated with a plurality of specification parameters including a target score, a penalty factor, and a bonus factor. The plurality of specification parameters are configured for each metric in accordance with a first criterion. The machine-learning model is evaluated using one or more validation datasets to obtain a metric score. A weighted loss function is formulated based on a difference between the metric score and the target score of each metric, the penalty factor or the bonus factor. The hyperparameters associated with the machine-learning model are tuned in order to optimize the weighted loss function. In response to the weighted loss function being optimized, the machine-learning model is provided as a validated machine-learning model.