Automated Calling System Context-Aware Response Generation
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Solution Overview
Problem
Users face challenges in collecting information from multiple businesses or organizations efficiently, as it often requires manual calls and human interaction, and existing automated systems struggle to understand and respond appropriately to human conversation in a context-dependent manner.
Innovation Solution
An automated calling system that uses machine learning to analyze audio data, context, and previous conversation intents to generate appropriate responses, allowing it to conduct telephone conversations with humans and complete tasks such as making reservations or hiring services without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calling is used to collect information from multiple businesses, then data accuracy can be maintained through human judgment, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent introduces an automated calling system as an intermediary between the user and multiple businesses. This system includes an audio processing module that captures and transcribes phone conversations, and an information extraction module that automatically identifies and structures relevant data from the transcriptions, replacing manual data collection while maintaining accuracy through automated analysis
Solution Approach 2:
The patent replaces the mechanical process of manual calling and data recording with an automated electronic system. The system uses audio processing technology to transcribe conversations and machine learning models to extract information automatically, substituting human operators with an automated processing pipeline that operates continuously without fatigue
2Productivity
If automated calling systems are implemented to reduce manual labor, then productivity increases, but the ability to understand and respond to context-dependent human conversation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the audio processing module continuously transcribes ongoing phone conversations and provides real-time input to the information extraction module. The system learns from the transcribed data and adjusts its information extraction patterns based on the actual conversation context, enabling adaptive response to varying business scenarios
Solution Approach 2:
The patent performs preliminary audio transcription and text processing before final information extraction. By converting speech to text in advance and pre-processing the linguistic data, the system creates a structured foundation that enables more accurate and context-aware information extraction in subsequent processing stages
3Measurement precision
If comprehensive audio analysis is performed to understand conversation context, then response accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the audio processing pipeline into distinct functional modules: an audio processing module that handles transcription, and a separate information extraction module that processes the transcribed text. This segmentation allows each module to specialize in its specific task, reducing overall system complexity while maintaining high response accuracy through coordinated processing
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.