Chat System Dialog Recommendations via Context-Aware Event Detection

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Solution Overview

Problem

Users of chat information systems face difficulties in understanding the capabilities and effectively utilizing the features of these systems, leading to a need for improved human-computer interfaces that provide intelligent and proactive dialog recommendations based on multiple criteria.

Innovation Solution

A method and system for delivering dialog recommendations in chat information systems, which involves receiving and recognizing speech-based user inputs, identifying triggering events such as user behavior, geographical location, and operating modes, and generating proactive recommendations to enhance user interaction and experience through a processor-based system including a speech recognition module, dialog manager, event manager, and recommendation manager.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the CIS provides comprehensive information and functionality, then the system capability is improved, but the user understanding and effective utilization deteriorates

Engineering Contradiction:

Improvesystem capabilityVSAvoiduser understanding

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system performs preliminary actions by proactively generating and presenting dialog recommendations to users before they need to formulate their own queries. The recommendation manager analyzes user profiles, interaction histories, and contextual information to prepare suggested dialogues in advance, making the system's capabilities visible and accessible to users without requiring them to understand complex system functionalities.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Dialog recommendations serve as an intermediary between the comprehensive system capabilities and the user. Instead of directly exposing all system functions which may overwhelm users, the recommendation manager acts as a mediator that translates complex capabilities into simple, context-relevant suggestions that users can easily understand and act upon.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If the CIS provides proactive recommendations, then the user experience is improved, but the system complexity increases

Engineering Contradiction:

Improveuser experienceVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system is segmented into distinct functional modules: speech recognition module for processing user input, dialog manager for maintaining conversation context, event manager for detecting triggering events, and recommendation manager for generating suggestions. This segmentation allows each component to handle specific tasks independently, managing overall system complexity while enabling proactive recommendations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The recommendation manager serves multiple functions simultaneously: it analyzes user profiles, processes interaction histories, detects contextual events, generates personalized recommendations, and presents them to users. This multi-functionality consolidates what could be separate complex systems into a single versatile component, improving user experience without proportionally increasing system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If the CIS monitors multiple criteria for recommendations, then the recommendation accuracy is improved, but the processing requirements increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoidprocessing requirements

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by monitoring multiple criteria (user profiles, interaction histories, contextual events) but only generating recommendations when specific triggering events are detected. Rather than continuously processing all available data, the event manager filters inputs and activates the recommendation manager only when relevant events occur, maintaining high recommendation accuracy while reducing overall processing requirements.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10026400B2Generating dialog recommendations for chat information systems based on user interaction and environmental data

Publication Date: 2018.07.17 GOOGLE LLC

AI summary

Disclosed is the technology for dynamic and intelligent generation of dialog recommendations for the users of chat information systems based on multiple criteria. An example method may include receiving a speech-based user input, recognizing at least a part of the speech-based user input to generate a recognized input, and providing at least one response to the recognized input. The method may further include identifying at least one triggering event, generating at least one dialog recommendation based at least in part on the identification, and presenting the at least one dialog recommendation to a user via a user device.