Contextual AI Insight Generation for Real-Time User Communication
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Solution Overview
Problem
Existing data insights are inefficient and fail to address the technical needs for execution of more intelligent and timely contextual analysis that is usable to improve data insight generation and provision.
Innovation Solution
Utilization of AI processing to learn user-specific insights contextually relevant to a state of a user communication by collecting and analyzing contextual information, cross-referencing it with an extensive knowledge graph, and applying relevance analysis to generate and curate data insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional systems continuously analyze thousands of documents to generate data insights each time a context change is detected, then data insights are generated, but processing efficiency is reduced as computing resources are continuously tied up
Solution Approach 1:
The system performs preliminary analysis by pre-processing and indexing document content before it is needed for insight generation. Documents are analyzed and structured in advance, creating a ready-to-query knowledge base that enables rapid insight generation without continuous full-document analysis
Solution Approach 2:
The system extracts only the specific contextual information and key elements relevant to the current meeting context from the larger document corpus, rather than re-analyzing entire documents. This extraction approach retrieves pre-processed information that is immediately applicable to the current context
2Quantity of substance
If traditional data insight systems analyze large amounts of data without considering user context, then data insights are produced, but the insights are low impact and do not advance the meeting
Solution Approach 1:
The system tailors the analysis depth and focus to the specific local context of each meeting situation. Instead of uniform analysis of all documents, the system identifies and analyzes only the specific portions of documents that are relevant to the current discussion context, user roles, and meeting objectives
Solution Approach 2:
The system incorporates feedback loops that continuously monitor meeting context, user interactions, and insight effectiveness. This feedback mechanism allows the system to adapt its analysis focus and adjust which documents and information are prioritized based on actual meeting dynamics and user needs
3Ease of operation
If manual actions are required to identify relevant meeting content and attach documents, then users can control the process, but the process is inefficient and negatively affects user experience
Solution Approach 1:
The system performs automatic document identification, relevance analysis, and attachment without requiring manual user intervention. The system autonomously monitors meeting contexts, identifies relevant documents from the knowledge base, and provides insights without users needing to manually search or attach files
Solution Approach 2:
The system introduces an intelligent intermediary layer between users and the document corpus. This intermediary automatically processes the matching between meeting contexts and relevant documents, translating user needs into automated document retrieval and insight generation without direct user involvement in the mechanical tasks
Data Source
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AI summary
In the present disclosure, artificial intelligence (AI) processing is trained and leveraged to learn user-specific insights that are contextually relevant to a state of a user communication. Contextual information about a state of a user communication may be collected and analyzed. That contextual information may be cross-referenced with an extensive knowledge graph that is constructed from user context data. Exemplary AI processing may further be trained to apply a relevance analysis to assist with processing described herein including generation and curation of data insights that are most relevant to a state of a user communication. In some examples, the data insight generation process may be augmented by pre-generating data insights that may be relevant to a user communication prior to occurrence of the user communication. Further technical examples pertain to the rendering and presentation of representations of data insights through a graphical user interface (GUI).