公开(公告)号：US20220366295A1

公开(公告)日：2022-11-17

申请号：US17319579

申请日：2021-05-13

Applicant: INTUIT INC.

Inventor： Prarit LAMBA ,  Steven Hidetaka KAWASUMI ,  Clifford GREEN

IPC: G06N20/00 ,  G06F16/2457

Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include providing features of a plurality of content items as inputs to an embedding model and receiving embeddings of the plurality of content items as outputs from the embedding model. Embodiments include receiving a data set comprising features of a plurality of users associated with content items of the plurality of content items that correspond to the plurality of users. Embodiments include generating a training data set for a machine learning model, wherein the training data set comprises the features of the plurality of users associated with respective labels indicating which respective embeddings of the embeddings correspond to each respective user of the plurality of users. Embodiments include training the machine learning model, using the training data set, to output corresponding embeddings of relevant content items for users based on features of the users.

公开(公告)号：US20230281399A1

公开(公告)日：2023-09-07

申请号：US17653426

申请日：2022-03-03

Applicant: INTUIT INC.

Inventor： Prarit LAMBA ,  Clifford GREEN ,  Tomer TAL ,  Andrew MATTARELLA-MICKE

IPC: G06F40/58 ,  G06F40/56 ,  G06K9/62

CPC classification number: G06F40/58 ,  G06F40/56 ,  G06K9/6257

Abstract: Embodiments disclosed herein provide language-agnostic routing prediction models. The routing prediction models input text queries in any language and generate a routing prediction for the text queries. For a language that may have sparse training text data, the models, which are machine learning models, are trained using a machine translation to a prevalent language (e.g., English) to the language having sparse training text data -with the original text corpus and the translated text corpus being an input to multi-language embedding layers. The trained machine learning model makes routing predictions for text queries for the language having sparse training text data.

公开(公告)号：US20230208975A1

公开(公告)日：2023-06-29

申请号：US18178425

申请日：2023-03-03

Applicant: INTUIT INC.

Inventor： Prarit LAMBA ,  Clifford GREEN

IPC: H04M3/523 ,  G06F40/20 ,  H04M3/51 ,  G06V30/40 ,  G06F18/214 ,  G06F18/21

CPC classification number: H04M3/5233 ,  G06F40/20 ,  H04M3/5191 ,  H04M3/5235 ,  H04M3/5238 ,  G06V30/40 ,  G06F18/214 ,  G06F18/2178

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.