AI Voice Assistant Command Prediction

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Solution Overview

Problem

Voice assistants require significant computing resources to perform various functions, leading to inefficiencies in processing and resource utilization.

Innovation Solution

A server computer system equipped with an AI engine that analyzes voice data to identify commands, generates recommendations, and sends signals to devices to output messages or perform operations, reducing the need for extensive input and output processes by leveraging historical voice data to automate tasks and activate shortcuts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If voice assistants process all user inputs through complete analysis and output cycles, then comprehensive functionality is achieved, but computational resource consumption increases significantly

Engineering Contradiction:

ImprovefunctionalityVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis by examining historical voice data to identify frequently executed command sequences before users actually request them. This advance preparation allows the system to recognize patterns and predict user intentions, enabling it to respond with pre-processed recommendations rather than performing complete analysis cycles for every input, thus reducing real-time computational resource consumption while maintaining comprehensive functionality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system serves itself by automatically analyzing its own historical operation data to generate recommendations for optimizing future interactions. The AI engine autonomously identifies patterns in voice commands and outputs, then uses this self-generated knowledge to improve efficiency without requiring external intervention or additional computational overhead from users, thereby reducing overall resource consumption while preserving full functionality

Inventive Principle:

Principle #25Self-service

2Reliability

If the system requires extensive user input and confirmation for each operation, then accuracy is improved, but interaction time and complexity increase

Engineering Contradiction:

Improveoperation accuracyVSAvoidinteraction time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements feedback by continuously monitoring user responses to recommendations and using this information to refine its prediction accuracy. When the AI engine correctly predicts user intent based on historical patterns, it can execute operations with minimal confirmation, reducing interaction time. The feedback loop ensures that accuracy is maintained or improved over time as the system learns from actual user behavior rather than relying solely on extensive initial confirmations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies partial action by requiring full user confirmation only when prediction confidence is low or when operations involve sensitive actions. For high-confidence predictions based on clear historical patterns, the system executes with reduced confirmation steps, thereby decreasing interaction time while maintaining sufficient accuracy through the partial verification approach

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If the AI engine analyzes all historical voice data for every recommendation, then recommendation quality improves, but processing speed decreases

Engineering Contradiction:

Improverecommendation qualityVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSSpeed

Solution Approach 1:

The system segments the historical voice data analysis by dividing it into distinct time periods, command types, or usage patterns. Instead of analyzing all historical data uniformly for each recommendation, the AI engine selectively examines relevant segments based on the current context and prediction needs. This segmentation maintains high recommendation quality by focusing on pertinent data while significantly improving processing speed by avoiding unnecessary analysis of unrelated historical records

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary organization and indexing of historical voice data before it is needed for generating recommendations. By pre-processing and structuring the data in advance, the AI engine can quickly retrieve and analyze only the necessary portions when making recommendations, rather than scanning through all historical data each time. This preliminary action preserves recommendation quality through thorough analysis of relevant data while achieving faster processing speeds through efficient data access

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240304182A1System and method for activating an artificial intelligence-based recommendation

Publication Date: 2024.09.12 THE TORONTO DOMINION BANK

AI summary

A server computer system comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to engage an artificial intelligence (AI) engine to analyze voice data to identify a string of commands; and generate at least one recommendation based on the string of commands; send, via the communications module to a first device, a signal causing the first device to output a message that includes the at least one recommendation; receive, via the communications module and from the first device, a signal that includes voice data for activating the at least one recommendation; and activate the at least one recommendation.