Contextual Insight System for Efficient Information Retrieval
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Solution Overview
Problem
Individuals face significant time-consuming challenges in identifying relevant information and knowledgeable individuals when encountering new or unfamiliar tasks, as they must manually search through vast amounts of data and people, making it difficult to filter out irrelevant information.
Innovation Solution
A contextual and event-driven insights system that monitors user data, including static and dynamic context, to process natural language requests, query resources for relevant users and content, and refine results using a ranking engine to provide meaningful and efficient information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a person manually searches through voluminous search results to find relevant information, then the information can be found, but the process becomes extremely time-consuming and daunting
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the user and the voluminous search results. This system automatically analyzes search results, identifies relevant information, and presents it to the user in a refined manner, eliminating the need for manual filtering through vast amounts of data while ensuring relevant information is not lost
Solution Approach 2:
The patent replaces the mechanical manual search and filtering process with an automated computational system. Instead of a person manually reviewing search results, the system uses algorithms and processing power to automatically identify, filter, and present relevant information, dramatically reducing the time required while maintaining information quality
2Loss of information
If a person seeks out people who may have information regarding the task, then relevant expertise can be identified, but the person is unable to identify which people have relevant information
Solution Approach 1:
The patent introduces an intermediary system that analyzes user profiles, expertise databases, and contextual information to identify which people have relevant information. This intermediary automatically matches user needs with appropriate experts, making the detection of knowledgeable people straightforward rather than difficult
Solution Approach 2:
The system incorporates feedback mechanisms that continuously learn from user interactions and expertise data. By analyzing patterns in what information users seek and what expertise proves valuable, the system improves its ability to identify knowledgeable people, making the process increasingly accurate over time
3Loss of information
If the system queries resources to identify candidate users and content items, then comprehensive results can be found, but computing resources are consumed
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing user profiles, content items, and expertise data before queries are made. This preparation work is done in advance, allowing the system to quickly retrieve and filter relevant information during actual queries without consuming excessive computing resources at query time
Solution Approach 2:
The system applies partial action by querying only the necessary subset of resources based on the specific query context and user profile, rather than exhaustively searching all available data. This selective approach finds relevant users and content without the excessive resource consumption of comprehensive searches
Data Source
AI summary
Aspects of systems and methods for providing contextual and event driven insights are provided. The system monitors information about the users and their conversations. Upon receiving a natural language request for information for a topic, the system utilizes a model to extract one or more topics from the request. The system utilizes the topic to query a resource for candidate users with knowledge about the topic. The system then queries a resource to identify candidate content items associated with the topic and the candidate users. Thereafter, the system refines the candidate users and the candidate content items to identify relevant users and content items that are meaningful to the user.