Audio Intent Evaluation for Low-Resource Communication Operations

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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 inefficiencies.

Innovation Solution

A system and method that evaluates audio data exchanged between devices, dynamically generates action item suggestions based on machine learning algorithms, and reduces resource consumption by efficiently determining communication intents in real time, allowing for more efficient conclusion of operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If communication operations exchange larger data packets over multiple minutes, then information completeness is improved, but network resource consumption (power, memory, processing) increases significantly

Engineering Contradiction:

Improveinformation completenessVSAvoidnetwork resource consumption

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent segments communication operations into distinct phases: audio data collection phase and data exchange phase. The system determines communication intent from audio data before initiating full data exchange, allowing early termination if intent is clear. This segmentation prevents unnecessary prolonged communication operations that would consume excessive network resources while ensuring complete information exchange when needed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary intent determination by analyzing audio data before committing to full data exchange operations. Machine learning models process audio inputs to predict communication intent, allowing the system to prepare appropriately and avoid unnecessary resource-intensive operations. This preliminary action filters out low-value communications before they consume significant network resources.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If communication operations last multiple minutes, then more complete information exchange is achieved, but processing time and resource usage increase

Engineering Contradiction:

Improvedata exchange completenessVSAvoidcommunication operation duration

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses self-service mechanisms by automatically determining communication intent from audio data without requiring manual intervention or prolonged interaction. The machine learning models autonomously analyze audio patterns, speaker characteristics, and contextual information to classify intent, enabling the system to make rapid decisions about whether to proceed with data exchange and how long the operation should last.

Inventive Principle:

Principle #25Self-service

3Speed

If machine learning algorithms process audio data in real time, then intent determination speed is improved, but processor and memory usage increase

Engineering Contradiction:

Improveintent determination speedVSAvoidprocessor and memory usage

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent segments the audio processing pipeline into distinct functional modules: audio data reception, feature extraction, intent classification, and decision execution. Each module processes only the necessary data for its specific function, reducing overall computational complexity. The system processes audio features rather than raw audio data, significantly reducing memory requirements while maintaining real-time processing capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes processing parameters by converting raw audio data into extracted features (such as spectral characteristics, temporal patterns, and speaker attributes) before feeding them to the machine learning model. This parameter transformation reduces the dimensionality and complexity of the input data, allowing real-time processing with reduced processor and memory usage while maintaining accurate intent determination.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250391405A1System and method to evaluate audio data

Publication Date: 2025.12.25 BANK OF AMERICA CORP

AI summary

A system comprises a memory communicatively coupled to at least one processor. The memory is operable to store a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models. The at least one processor is configured to obtain audio data from a user device. In response to receiving the audio data, the processor is configured to execute the machine learning algorithm to transcribe the audio data into text data and summarize the text data into a request summary. Further, the processor is configured to determine a target operation based on the request summary in response to summarizing the text data. The target operation is a determined intent to perform a communication operation. The processor is configured to map the target operation to a suggestion and presenting the suggestion to a workspace device.