Neural Sentence Generation for Ambiguous Virtual Assistant Intents

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Solution Overview

Problem

Conventional virtual assistants struggle to recognize and handle user requests described in varied and ambiguous natural language, often failing to understand slight deviations from standard phrasing.

Innovation Solution

A neural network language model is trained via unsupervised learning to generate phrases and sentences that align with user intents, using a classifier model to compute correctness scores and select sentences with high probability of matching the intended action, even for ambiguous queries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional virtual assistants use standard pattern matching to recognize user requests, then they can reliably handle standard phrasing, but they fail to recognize varied and ambiguous natural language expressions

Engineering Contradiction:

Improveability to handle varied natural language phrasingVSAvoidaccuracy in recognizing user intent

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent transforms the rigid parameter-matching approach into a flexible semantic representation system. By converting user queries and candidate sentences into vector embeddings that capture semantic meaning rather than exact word matches, the system can reliably recognize varied phrasing while maintaining accuracy in intent recognition.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical pattern-matching system with a neural network-based semantic similarity system. Instead of relying on predefined templates and keyword matching, the system uses trained neural networks to compute semantic representations and measure similarity, enabling reliable interpretation of ambiguous and varied natural language expressions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If virtual assistants manually define every possible user phrasing pattern, then they can accurately recognize standard requests, but the system complexity and development effort increase significantly

Engineering Contradiction:

Improveaccuracy in request recognitionVSAvoidsystem configuration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent enables the virtual assistant system to automatically learn and adapt to user phrasing patterns through unsupervised training on domain-specific data. The neural networks are trained to recognize semantic patterns without requiring manual annotation of every possible user expression, significantly reducing system configuration complexity while maintaining high recognition accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary training of neural network models on domain-specific corpora before deployment. This pre-training phase allows the system to learn common phrasing patterns and semantic relationships in advance, so that when deployed, the virtual assistant can reliably recognize user requests without requiring complex real-time configuration or manual pattern definition.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If virtual assistants use exact matching algorithms, then they provide fast and simple processing, but they cannot handle slight deviations from standard phrasing

Engineering Contradiction:

Improvequery processing speedVSAvoidhandling of ambiguous queries

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent implements a two-stage processing approach where the system first performs rapid filtering of candidate sentences based on basic criteria, then applies computationally intensive semantic similarity calculations only to the most promising candidates. This allows the system to maintain high productivity for clear queries while providing adaptability for ambiguous cases without sacrificing overall processing speed.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250356849A1Domain specific neural sentence generator for multi-domain virtual assistants

Publication Date: 2025.11.20 SOUNDHOUND AI IP LLC

AI summary

Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.