Conversational Recommendation Models Using Multimodal Context

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing artificial intelligence models face challenges in generating human-like conversational recommendations due to reliance on high-quality data acquisition, specialized knowledge requirements, and potential biases, leading to ambiguous and biased suggestions, particularly in specialized domains.

Innovation Solution

The system expands training data by incorporating multi-modal inputs such as textual, audio, and biometric data, using separate models to predict and modify recommendations based on both textual and supplemental inputs, and employs context-aware processing to refine interpretations over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If artificial intelligence models are used to generate conversational recommendations, then the system can process data and perform real-time determinations, but the models rely on large amounts of high-quality data that is complex and time-consuming to obtain and categorize

Engineering Contradiction:

Improvereal-time determination capabilityVSAvoiddata acquisition and labeling time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and pre-labeling data in advance, creating a structured database of user interactions, contexts, and recommendations before actual conversational moments occur. This allows the AI model to query pre-organized data during real-time interactions without undergoing time-consuming data acquisition and labeling at the moment of need.

Inventive Principle:

Principle #10Preliminary action

2Extent of automation

If artificial intelligence models are used to generate recommendations, then the system can analyze input data to predict and suggest completions, but specialized knowledge is required to design, program, and integrate the solutions

Engineering Contradiction:

Improveautomatic suggestion capabilityVSAvoidspecialized knowledge requirements

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer of pre-defined interaction patterns, context templates, and recommendation frameworks that bridge the gap between raw user input and AI-generated suggestions. This intermediary structure guides the AI model through standardized decision pathways, reducing the need for complex specialized programming while maintaining automated intelligent suggestions.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If artificial intelligence models are used to generate recommendations, then the system can process data and find patterns, but the results are difficult to review as the process by which results are made may be unknown or obscured

Engineering Contradiction:

Improvepattern recognition accuracyVSAvoiddecision process transparency

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system implements feedback mechanisms that track and record the decision-making pathways of the AI model, storing which data patterns were considered, which recommendations were generated, and how user interactions evolved. This feedback loop makes the previously opaque decision process transparent and reviewable, allowing users and developers to examine the reasoning behind recommendations without sacrificing pattern recognition accuracy.

Inventive Principle:

Principle #23Feedback

4Speed

If the system generates recommendations based on initial user inputs, then the system can provide timely suggestions, but the model may struggle when there are multiple possible outputs for a given input

Engineering Contradiction:

Improverecommendation generation speedVSAvoidaccuracy in ambiguous contexts

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system employs dynamic recommendation generation that adapts to user interactions in real-time. Instead of providing static predictions based solely on initial inputs, the system continuously updates its suggestions based on subsequent user actions, contextual cues, and interaction patterns. This dynamic approach allows the system to resolve ambiguities by observing how users respond to different possibilities, maintaining both speed and accuracy even in complex situations.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260017454A1Systems and methods for alternative content recommendations based on analyzing potential interpretations using supplemental inputs

Publication Date: 2026.01.15 CAPITAL ONE SERVICES LLC

AI summary

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and/or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and/or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).