18674479. ENABLING NATURAL CONVERSATIONS WITH SOFT ENDPOINTING FOR AN AUTOMATED ASSISTANT simplified abstract (Google LLC)
Contents
ENABLING NATURAL CONVERSATIONS WITH SOFT ENDPOINTING FOR AN AUTOMATED ASSISTANT
Organization Name
Inventor(s)
Jaclyn Konzelmann of Mountain View CA (US)
Trevor Strohman of Sunnyvale CA (US)
Jonathan Bloom of Maplewood NJ (US)
Johan Schalkwyk of Scarsdale NY (US)
Joseph Smarr of Half Moon Bay CA (US)
ENABLING NATURAL CONVERSATIONS WITH SOFT ENDPOINTING FOR AN AUTOMATED ASSISTANT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18674479 titled 'ENABLING NATURAL CONVERSATIONS WITH SOFT ENDPOINTING FOR AN AUTOMATED ASSISTANT
- Simplified Explanation:**
The patent application describes a system where an automated assistant can process audio data in real-time to generate text output, analyze the text using a natural language understanding model, and provide appropriate responses based on the user's input.
- Key Features and Innovation:**
- Real-time processing of audio data using a streaming ASR model
- Analysis of text output using an NLU model to understand user input
- Generation of fulfillment data based on the NLU output
- Detection of audio-based characteristics to determine user pauses or completion of speech
- Provision of natural conversation output based on user input
- Potential Applications:**
This technology can be applied in automated customer service systems, virtual assistants, language translation services, and transcription tools.
- Problems Solved:**
The system addresses the challenges of processing real-time audio data, understanding user input accurately, and providing relevant responses in a conversational manner.
- Benefits:**
- Improved user experience in interacting with automated systems
- Enhanced accuracy in understanding spoken language
- Efficient processing of audio data for various applications
- Commercial Applications:**
The technology can be utilized in customer service chatbots, virtual assistants for smart devices, transcription services, and language learning platforms.
- Prior Art:**
Prior research in the field of natural language processing, speech recognition, and conversational AI can provide insights into similar technologies and approaches.
- Frequently Updated Research:**
Stay updated on advancements in streaming ASR models, NLU algorithms, and audio processing techniques to enhance the performance of the system.
- Questions about the Technology:**
1. How does the system differentiate between user pauses and completion of speech? 2. What are the potential limitations of real-time audio processing in this context?
By incorporating real-time audio processing, natural language understanding, and audio-based characteristics analysis, this technology revolutionizes the way automated assistants interact with users, providing a more seamless and intuitive experience.
Original Abstract Submitted
As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data that captures a portion of a spoken utterance to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and cause, based on the NLU output, a stream of fulfillment data to be generated. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with the portion of the spoken utterance captured in the stream of audio data. Based on the audio-based characteristics and/the stream of NLU output, implementations can determine whether the user has paused in providing the spoken utterance or has completed providing of the spoken utterance. If the user has paused, implementations can cause natural conversation output to be provided for presentation to the user.
