Context-Based AI Query Processing for Precise Automated Actions

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Solution Overview

Problem

Conventional chatbot systems struggle to provide precise guidance in complex and dynamic environments with multiple interconnected devices, leading to latencies and resource-intensive errors.

Innovation Solution

A computer-implemented method using artificial intelligence techniques to classify user intentions, identify relevant data sources, and dynamically generate context-based outputs by integrating classified intentions and data, facilitating automated actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional chatbot systems are implemented in complex environments with multiple device types and diverse data streams, then they can handle various user queries, but they result in latencies and resource-intensive errors

Engineering Contradiction:

Improveability to handle various user queriesVSAvoidlatencies and errors

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system segments the complex query processing task into distinct components: an AI model that classifies user intentions and identifies relevant data sources, and a separate execution engine that performs automated actions based on classified intentions. This segmentation allows each component to specialize, improving overall reliability while maintaining versatility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary classification of user intentions and identification of relevant data sources before executing automated actions. By pre-processing and categorizing queries using AI techniques, the system prepares structured information in advance, reducing latencies and preventing resource-intensive errors during action execution.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If conventional chatbot systems process queries in complex environments, then they can provide responses, but they consume excessive resources and produce errors

Engineering Contradiction:

Improveresponse generation capabilityVSAvoidresource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential elements from user queries - specifically classifying the intention and identifying relevant data sources - before proceeding to execute automated actions. This extraction approach avoids processing unnecessary information, reducing resource consumption while maintaining productive response generation.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the parameter of query processing from comprehensive text analysis to structured classification and identification. By transforming unstructured user queries into classified intentions with associated data sources, the system optimizes resource usage while maintaining productivity in generating appropriate responses.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If conventional chatbot systems operate in dynamic environments with interconnected devices, then they can cover multiple scenarios, but they lack precise guidance capability

Engineering Contradiction:

Improvecoverage of multiple scenariosVSAvoidguidance precision

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system introduces an intermediary AI model that acts as a mediator between user queries and automated actions. This intermediary classifies intentions and identifies relevant data sources, providing precise guidance by translating diverse user requests into structured, actionable information that maintains precision across multiple scenarios.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements a feedback mechanism where the AI model continuously classifies user intentions and identifies data sources based on incoming queries. This feedback loop allows the system to adapt to different scenarios while maintaining precise guidance by constantly refining its understanding of user needs and relevant information sources.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250328546A1Automatically generating context-based dynamic outputs using artificial intelligence techniques

Publication Date: 2025.10.23 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for automatically generating context-based dynamic outputs using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query from at least one user device using at least one user interface; classifying at least one intention associated with the at least one query by processing the at least one query using one or more artificial intelligence techniques; identifying at least one data source related to the at least one query and/or the classified intention(s) by processing the at least one query using the artificial intelligence technique(s); dynamically generating at least one context-based version of the at least one query by integrating at least a portion of the classified intention(s) and data associated with the identified data source(s) into the at least one query; and performing automated action(s) based on the dynamically generated context-based version(s) of the at least one query.