Context-Aware Chatbot Guidance for Topic-Relevant Responses

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Solution Overview

Problem

Chatbots often struggle with addressing complex questions or maintaining user satisfaction due to insufficient or unrelated responses, leading to a preference for live agents despite their convenience.

Innovation Solution

Utilizing multiple AI models, including a primary generative AI model and secondary tone and topic detection models, to generate and adjust chatbot responses based on conversation context, tone, and topic relevance, ensuring consistent and relevant interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a chatbot is used to provide automated customer support, then operational costs are reduced and availability is improved, but user satisfaction deteriorates due to insufficient or unrelated responses

Engineering Contradiction:

ImproveavailabilityVSAvoidresponse relevance

Core Design Contradiction:

ReliabilityVSManufacturing precision

Solution Approach 1:

The system implements feedback loops where user responses are analyzed to detect dissatisfaction signals. When users express frustration or indicate the chatbot is unhelpful, the system adjusts its response strategy in real-time, escalating to more appropriate resolution paths such as human agent transfer or alternative support channels.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The chatbot system dynamically adapts its behavior based on conversation context and user sentiment. It transitions from rigid scripted responses to more flexible, context-aware responses using AI models that can recognize when standard protocols fail and adjust the interaction approach accordingly.

Inventive Principle:

Principle #15Dynamics

2Stability of the object's composition

If a chatbot follows strict conversation protocols to maintain structure, then conversation organization is improved, but user satisfaction deteriorates due to rigid and unnatural interactions

Engineering Contradiction:

Improveconversation structureVSAvoidinteraction naturalness

Core Design Contradiction:

Stability of the object's compositionVSEase of operation

Solution Approach 1:

The system employs dynamic conversation management where the level of protocol adherence adjusts based on user needs. AI models analyze conversation flow and user intent to determine when to follow strict protocols and when to allow more natural, flexible interactions, creating a balanced approach that maintains structure while enabling naturalness.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The chatbot system incorporates self-correction mechanisms where AI models automatically identify when protocol adherence is causing user frustration and autonomously adjust the conversation approach, such as breaking protocol rules to transfer to human agents or change response styles without external intervention.

Inventive Principle:

Principle #25Self-service

3Speed

If AI models are used to generate chatbot responses, then response speed is improved, but response quality deteriorates due to generic or tone-mismatched outputs

Engineering Contradiction:

Improveresponse speedVSAvoidresponse quality

Core Design Contradiction:

SpeedVSManufacturing precision

Solution Approach 1:

Multiple AI models provide feedback on different aspects of potential responses (tone, context relevance, appropriateness). The system synthesizes this feedback to select or generate responses that meet multiple quality criteria simultaneously, ensuring both speed and precision in response generation.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system uses a composite approach combining multiple AI models with different specialized functions. Rather than relying on a single model, it integrates outputs from models trained on different aspects (tone detection, context understanding, response generation) to create responses that are both fast and high-quality.

Inventive Principle:

Principle #40Composite materials

4Adaptability or versatility

If the chatbot handles diverse user inputs to increase versatility, then adaptability is improved, but response relevance deteriorates due to topic drift from the original inquiry

Engineering Contradiction:

Improveinput handling capabilityVSAvoidtopic focus

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The system continuously monitors conversation topic drift using AI analysis of user inputs and chatbot responses. When topic deviation is detected, the system provides feedback to realign the conversation with the original user intent, maintaining versatility in handling diverse inputs while preserving focus on the core issue.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250298988A1Chatbot to guide user back to topic of interest

Publication Date: 2025.09.25 THE TORONTO DOMINION BANK

AI summary

An example operation may include one or more of executing an interaction with an account device about a first topic of interest and a chatbot within a chat element, wherein the interaction comprises an exchange of content between the account device and the chatbot within the chat element, receiving an interaction data from the account device within the chat element; determining that the interaction data is a second topic of interest based on an execution of an artificial intelligence model on the interaction and the first topic of interest, generating a chatbot response to the interaction data based on the execution of the artificial intelligence model on a state of the interaction prior to receipt of the interaction data, and outputting the chatbot response within the chat element.