Conversation Topic Prediction and Content Delivery
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Solution Overview
Problem
Users face inconvenience in searching for content related to ongoing conversations as it requires interrupting the conversation to input a search on their smartphones, and by the time they find relevant content, the conversation may have moved on to a different topic.
Innovation Solution
Systems and methods predict future conversation topics based on the initial part of the conversation and user information, using a database of prior conversations classified with data mining algorithms to identify and provide related content in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user manually searches for content related to conversation topics, then relevant content can be found, but the conversation flow is interrupted and time is lost
Solution Approach 1:
The system performs preliminary actions by predicting future conversation topics before they actually occur, and proactively retrieving related content in advance. This allows content to be ready and available when the user needs it, eliminating the need to interrupt the conversation for manual searching.
Solution Approach 2:
The system continuously monitors the ongoing conversation, analyzes topic transitions, and uses this feedback to update predictions and retrieve additional relevant content dynamically. This creates a closed-loop system that adapts to the conversation flow in real-time.
2Speed
If content is provided in real-time during conversation, then timeliness is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional architecture where the conversation analysis component serves multiple purposes: understanding current topics, predicting future topics, and guiding content retrieval. This universal approach reduces overall system complexity by avoiding separate specialized components for each function.
Solution Approach 2:
The patent introduces intermediary components such as topic models and prediction algorithms that act as mediators between the raw conversation data and the content retrieval system. These intermediaries simplify the complexity by abstracting the complex prediction logic into manageable computational layers.
3Measurement precision
If manual searching is required, then search precision can be controlled, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing the conversation, predicting topics, and retrieving relevant content without requiring user intervention. The system serves itself in identifying information needs and fulfilling them, thereby eliminating manual searching while maintaining precision through algorithmic topic analysis.
Solution Approach 2:
By performing preliminary topic prediction and content retrieval before the user would need the information, the system ensures that accurate, relevant content is already prepared and delivered, eliminating the need for users to perform precision-critical manual searching.
Data Source
AI summary
Systems and methods are described for predicting a future topic of a current conversation and providing content associated with the predicted topic. A topic may be predicted based on a first part of a current conversation between a first user and a second user and information about the first user and the second user. Content associated with the predicted topic may be identified, and information about the identified content may be provided to a device associated with the first user or the second user.