Adaptive Conversation State Filtering for Search Thread Boundaries

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Solution Overview

Problem

Conversational interfaces face challenges in managing conversation states and filters, particularly in speech recognition environments with accent variations and mobile settings, where users struggle with explicit filter management and thread boundaries, leading to suboptimal user experiences.

Innovation Solution

A method for adaptive conversation state management that dynamically applies filtering operators, recognizes conversation thread boundaries, and automatically resets filters without explicit user intervention, using a relationship database and graph model to measure relatedness and adjust the conversation state space based on user inputs and preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If filtering operators are explicitly managed by users in non-conversational interfaces, then information retrieval precision is improved, but user operation complexity increases

Engineering Contradiction:

Improveinformation retrieval precisionVSAvoiduser operation complexity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system automatically manages filtering operators by inferring them from conversation exchanges and dynamically applying them to modify conversation state, eliminating the need for explicit user management while maintaining retrieval precision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical filter management with automated natural language processing, where filtering operators are inferred from speech recognition and conversation context, substituting user actions with intelligent system behavior

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If filters are reset on each session boundary in session-based systems, then system reliability is improved, but loss of information increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidfilter state information

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system dynamically adapts conversation state space to filtering operators, expanding or pruning state and adjusting weights based on operators, allowing filters to persist and evolve across session boundaries when contextually appropriate

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system monitors conversation exchanges to detect thread boundaries and automatically resets filters only when appropriate, using feedback from conversation context to determine when filter persistence or reset is desired

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If conversation state space is dynamically adapted to filtering operators, then adaptability is improved, but device complexity increases

Engineering Contradiction:

Improveconversation state adaptabilityVSAvoidstate management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments conversation state management into distinct components including filtering operators, conversation exchanges, and state space elements, allowing independent management and processing of each component

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20260079987A1Method for adaptive conversation state management with filtering operators applied dynamically as part of a conversational interface

Publication Date: 2026.03.19 ADEIA GUIDES INC

AI summary

A system and method of processing a search request is provided. Identification of a desired content item is based on comparing a topic of the search request to previous user input. The method includes providing access to a set of content items with metadata that describes the corresponding content items and providing information about previous searches. The method further includes receiving a present input from the user and determining a relatedness measure between the information about the previous searches and an element of the present input. If the relatedness measure is high, the method also includes selecting a subset of content items based on comparing the present input and information about the previous searches with the metadata that describes the subset of content items. Otherwise, the method includes selecting a subset of content items based on comparing the present input with the metadata that describes the subset of content items.