Audio Intent Summarization for Low-Resource Interface Controls
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Solution Overview
Problem
Communication operations in existing systems consume significant network resources, including power, memory, and processing resources, particularly in lengthy or large data exchanges, leading to inefficient use of resources.
Innovation Solution
A system and method that dynamically generates interface controls based on audio data exchanged between devices, using machine learning algorithms to transcribe and summarize the data, determining intent, and generating visual representations in real time to reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional communication operations are used for data exchange between devices, then complete data transmission is achieved, but network resources (power, memory, processing) are significantly consumed
Solution Approach 1:
The patent extracts only the essential information from audio data by performing transcription and summarization to determine intent, rather than transmitting or processing the complete audio data. This extraction approach reduces network resource consumption while preserving the critical information needed for communication operations.
Solution Approach 2:
The system performs preliminary transcription and summarization of audio data to determine intent before proceeding with full communication operations. This preliminary action allows the system to identify and process only the necessary information, reducing subsequent resource consumption during actual data exchange operations.
2Measurement precision
If real-time transcription and summarization of audio data is performed using machine learning algorithms, then intent determination accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing transcription and summarization only when necessary for intent determination, rather than processing all audio data in real-time. The system selectively applies machine learning algorithms to extract meaningful intent information, reducing overall processing time while maintaining accuracy for critical communication operations.
3Adaptability or versatility
If interface controls are dynamically generated based on transcribed and summarized audio data, then user interaction capability is enhanced, but device complexity increases
Solution Approach 1:
The patent implements a universal intent determination mechanism that processes various audio data types (conversations, instructions, requests) through a common machine learning pipeline. This multi-functional approach allows the system to handle diverse communication scenarios with a unified interface control generation process, managing complexity while enhancing adaptability.
Data Source
AI summary
A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to obtain audio data from a user device. Further, in response to receiving the audio data, the processor is configured to execute a machine learning algorithm to transcribe the audio data into text data and summarize the text data into a data summary. The data summary is representative of a predicted intent associated with the audio data. The processor is configured to determine an interface property based on the data summary in response to summarizing the text data. The interface property is one or more communication commands to interact with the data summary. The processor is configured to determine an interface control based on the data summary and the interface property, bind the interface property to a rendered interface control, and present the rendered interface control to a workspace device.