公开(公告)号：US12143343B1

公开(公告)日：2024-11-12

申请号：US17532958

申请日：2021-11-22

Applicant: Amazon Technologies, Inc.

Inventor： Swaminathan Sivasubramanian ,  Vasanth Philomin ,  Ganesh Kumar Gella ,  Santosh Kumar Ameti ,  Meghana Puvvadi ,  Manikya Pavan Kiran Pothukuchi ,  Harshal Pimpalkhute ,  Rama Krishna Sandeep Pokkunuri ,  Yahor Pushkin ,  Roger Scott Jenke ,  Yaser Al-Onaizan ,  Yi Zhang ,  Saab Mansour ,  Salvatore Romeo

IPC: H04L51/02 ,  G06F9/54

Abstract: A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

公开(公告)号：US12175966B1

公开(公告)日：2024-12-24

申请号：US17361003

申请日：2021-06-28

Applicant: Amazon Technologies, Inc.

Inventor： Yi-An Lai ,  Yi Zhang ,  Roger Scott Jenke ,  Meghana Puvvadi ,  Shang-Wen Daniel Li ,  Peng Zhang ,  Jason P. Krone ,  Garima Lalwani ,  Niranjhana Nayar ,  Kartik Natarajan

IPC: G10L15/22 ,  G06N5/04 ,  G06N20/00 ,  G10L15/06 ,  G10L15/18 ,  H04L51/02 ,  G10L15/16

Abstract: Techniques for updating a machine learning model based on user interactions are described. In particular, in some examples, user interactions with a chatbot provide aspects of a data set to be used to train or fine-tune a ML model. In some examples, this is accomplished by collecting data from a first plurality of interactions with a machine learning (ML) model; generating a variant of the ML model using the collected data by: filtering the collected data to create a first data set, training the ML model based on the first data set to generate an adapted ML model, and fine-tuning the adapted ML model on a second data set, different than the first data set to generate the variant of the ML model.

公开(公告)号：US11341339B1

公开(公告)日：2022-05-24

申请号：US16874582

申请日：2020-05-14

Applicant: Amazon Technologies, Inc.

Inventor： Shang-Wen Daniel Li ,  Meghana Puvvadi ,  Trevor Andrew Morse ,  Roger Scott Jenke ,  Yi Zhang ,  Rama Krishna Sandeep Pokkunuri

IPC: G06F15/00 ,  G10L15/00 ,  G10L13/00 ,  G10L13/06 ,  G06F40/40

Abstract: Techniques for creating and calibrating natural-language understanding (NLU) machine learning models are described. In certain embodiments, a training service tunes parameters of a function, taking the output from an NLU machine learning model as an input of the function, to calibrate the NLU machine learning model's output to optimize the interpretability of the resulting output, e.g., confidence score(s). Embodiments herein include generating, by the NLU machine learning model, an output based at least in part on an input (e.g., utterance) from a user, and applying a tuned, output modifying function to the output from the NLU machine learning model to generate a modified output. An inference may be generated based at least in part on the modified output.

公开(公告)号：US11568145B1

公开(公告)日：2023-01-31

申请号：US17038506

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Salvatore Romeo ,  Yi Zhang ,  Garima Lalwani ,  Meghana Puvvadi ,  Rama Krishna Sandeep Pokkunuri

IPC: G06F40/289 ,  G06F40/40 ,  H04L51/04

Abstract: Systems, methods, and apparatuses for contextual natural language understanding are detailed. An exemplary method includes receiving a user utterance provided by a user within a multi-turn chat dialog between the user and a conversational agent; providing to a contextual natural language understanding framework: the user utterance, and contextual information associated with one or more previous turns of the multi-turn chat dialog, the contextual information associated with each turn of the one or more previous turns including a previous intent, a previous dialog act, and an elicited slot; and obtaining, from the contextual natural language understanding framework, an intent classification and one or more slot labels.

公开(公告)号：US11562735B1

公开(公告)日：2023-01-24

申请号：US16836130

申请日：2020-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Arshit Gupta ,  Julian E. S. Salazar ,  Peng Zhang ,  Katrin Kirchhoff ,  Yi Zhang

IPC: G10L15/18 ,  G10L15/197 ,  G10L15/26

Abstract: A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.