Virtual Assistant for Multi-Session Content Segmentation
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Solution Overview
Problem
Users face difficulties in simultaneously engaging with multiple real-time communication sessions due to limitations in monitoring and actively participating in multiple conversations, leading to missed information and a degraded user experience.
Innovation Solution
An enhanced virtual assistant or chat-bot analyzes communication session content using natural language processing and machine learning to identify relevant content, generate summaries, and provide real-time notifications, thereby enhancing user engagement across multiple conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user attempts to simultaneously participate in multiple communication sessions, then the user can be present in multiple conversations, but the user experiences difficulty in monitoring and actively participating in all conversations, leading to missed information and degraded user experience
Solution Approach 1:
The patent segments the content from multiple communication sessions by identifying and separating likely relevant content from less relevant content. The system divides the information stream into discrete, manageable portions based on relevance criteria, allowing the user to focus on segmented relevant information rather than attempting to monitor all content from all sessions simultaneously.
Solution Approach 2:
The patent introduces an intermediary system (the communication session analysis system) that acts as a mediator between the multiple communication sessions and the user. This intermediary automatically analyzes sessions, identifies relevant content, and presents it to the user, eliminating the need for the user to directly monitor all conversations while still enabling participation.
2Loss of information
If a user manually reviews communication session logs to catch missed information, then the user can retrieve some lost information, but the user loses opportunities to respond at critical junctures and experiences time loss
Solution Approach 1:
The patent applies preliminary action by proactively identifying and flagging likely relevant content as it occurs in communication sessions, before the user needs to review it. The system performs preliminary analysis and selection of important content, so when the user reviews the summarized relevant portions, they are seeing pre-filtered information that requires minimal additional processing time.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors communication sessions and provides real-time or near-real-time notifications about likely relevant content. This feedback loop allows the user to respond to critical information promptly rather than discovering missed information only after manual log review, thereby reducing both information loss and response time delays.
3Loss of information
If a user tries to track complex arguments or analyses across multiple conversations, then the user can follow the topics, but the cognitive load increases and the user may miss information in other sessions
Solution Approach 1:
The patent extracts likely relevant content from the complex stream of multiple communication sessions, separating important information from the overall complexity. By taking out only the portions of content that meet relevance criteria, the system reduces the cognitive burden on the user while preserving the essential information needed to follow complex arguments and analyses across different topics.
Solution Approach 2:
The patent applies local quality by providing customized relevance assessment for different communication sessions and different users. The system adjusts the identification of likely relevant content based on user-specific factors such as role, interests, and context, allowing each user to receive a tailored view of relevant information from multiple sessions rather than a uniform complex overview.
Data Source
AI summary
Methods for providing enhanced services to users participating in communication sessions (CS), via a virtual assistant, are disclosed. One method receives content that is exchanged by users participating in the CS. The content includes natural language expressions that encode a conversation carried out by users. The method determines content features based on natural language models. The content features indicate intended semantics of the natural language expressions. The method determines a relevance of the content and identifies portions of the content that are likely relevant to the user. Determining the relevance is based on the content features, a context of the CS, a user-interest model, and a content-relevance model of the natural language models. Identifying the likely relevant content is based on the determined relevance of the content and a relevance threshold. A summary of the CS is automatically generated from summarized versions of the likely relevant portions of the content.