公开(公告)号：US20240378373A1

公开(公告)日：2024-11-14

申请号：US18661195

申请日：2024-05-10

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： PAVAN BUDUGUPPA ,  RAMASUBRAMANIAN SUNDARAM ,  ALLWIN RAJU

IPC: G06F40/166 ,  G06F21/62 ,  G06F40/117 ,  G06F40/197 ,  G06F40/295

Abstract: A method of extending a tagging and masking of recognized entities in a conversation transcript to cover new entity types. The method is performed in relation to the conversation transcript in accordance with parameters defining a new entity type. The method may include the steps of: searching for one of the keyword lookup trigger or the entity lookup trigger and triggering a lookup process in response to a detection thereof; performing the triggered lookup process by searching the search range specified in the received parameters for the selected generic entity type and detecting the selected generic entity type therein; revising the conversation transcript so that the entity name of the detected selected generic entity type is replaced with the entity name for the new entity type; and outputting a revised version of the conversation transcript that includes the revision.

公开(公告)号：US20230315992A1

公开(公告)日：2023-10-05

申请号：US18208078

申请日：2023-06-09

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： FELIX IMMANUEL WYSS ,  ARAVIND GANAPATHIRAJU ,  PAVAN BUDUGUPPA

IPC: G06F40/295 ,  H04L51/02 ,  G06F40/253 ,  G06N3/044

CPC classification number: G06F40/295 ,  H04L51/02 ,  G06F40/253 ,  G06N3/044 ,  G06N3/08

Abstract: A method for deriving a model for a chatbot for predicting entities in a sentence. The sentence is input into a named-entity recognition module and features obtained. A LSTM RNN forward pass and backward pass is performed on the features to obtain a first and second set of results, respectively. A first concatenating is performed on the first set of results and the second set of results. A second concatenation is performed on the first concatenation using output target entities. A connected set of neurons from the second concatenation is obtained. An output is obtained, and a prediction is collected on a next output by summing the outputs previous to that output. The prediction is input into the performing of the second concatenation step, wherein the method is performed cyclically until all outputs have been processed with input predictions.

公开(公告)号：US20230196030A1

公开(公告)日：2023-06-22

申请号：US17557219

申请日：2021-12-21

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： PAVAN BUDUGUPPA ,  RAMASUBRAMANIAN SUNDARAM ,  VEERA RAGHAVENDRA ELLURU

IPC: G06F40/40 ,  G06N5/02 ,  G06F40/30 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F40/40 ,  G06F40/30 ,  G06N3/082 ,  G06N3/0454 ,  G06N5/022

Abstract: A method for creating a student model from a teacher model for knowledge distillation. The method including: providing a first model; using a first instance of the first model to create the teacher model by training the first instance of the first model on a training dataset; using a second instance of the first model to create the student model by training the second instance of the first model on a subset of the training dataset; identifying corresponding layers in the teacher model and the student model; for each of the corresponding layers, computing a weight similarity criterion; ranking the corresponding layers according to the weight similarity criterion; selecting, based on the ranking, one or more of the corresponding layers for designation as one or more discard layers; removing from the student model the one or more discard layers.

公开(公告)号：US20230196024A1

公开(公告)日：2023-06-22

申请号：US17557245

申请日：2021-12-21

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： PAVAN BUDUGUPPA ,  RAMASUBRAMANIAN SUNDARAM ,  VEERA RAGHAVENDRA ELLURU

IPC: G06F40/30 ,  G06N5/02 ,  G06N3/04

CPC classification number: G06F40/30 ,  G06N5/02 ,  G06N3/0454

Abstract: A method for creating a student model from a teacher model for knowledge distillation. The method may include: providing the teacher model trained on a first training dataset; generating candidate student models, wherein each of the candidate student models is a model having a unique permutation of layers derived by randomly selecting one or more layers of the plurality of layers of the teacher model for removing; generating a second training dataset; for each of the candidate student models: providing the second training dataset as inputs; recording outputs generated; and based on the recorded outputs, evaluating a performance according to a predetermined model evaluation criterion; determining which of the candidate student models performed best among the candidate student models based on the predetermined model evaluation criterion; identifying a preferred candidate student model.