Contextual AI Platform for Intent-Based Data Insights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for data management in enterprises struggle to provide real-time insights and complex analytics, as they are often reactive, inflexible, and require extensive user training, leading to inaccessibility of valuable data and lost business opportunities.
Innovation Solution
A context and intent-based artificial intelligence platform that synthesizes knowledge from both structured and unstructured data using a knowledge graph and word embeddings, enabling it to understand user requests and generate insights proactively, adapting to user attributes and needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional document management systems and established system architectures are used, then data storage and basic access are maintained, but the systems are incapable of adequately addressing dynamic user needs for data insight and require extensive user training
Solution Approach 1:
The patent implements a dynamic system that adapts to user needs through machine learning models that continuously learn from user interactions and feedback. The system evolves its knowledge base and recommendation capabilities based on actual usage patterns, making the architecture flexible rather than static.
Solution Approach 2:
The system performs self-learning and self-improvement through automated machine learning processes. It automatically updates its knowledge base, refines its understanding of user needs, and improves its insights generation without requiring manual reconfiguration or extensive user training.
2Adaptability or versatility
If reactive systems with established rules are used, then system stability is maintained, but the systems do not adapt to diverse user attributes or rapid and dynamic changes in user needs over time
Solution Approach 1:
The system incorporates continuous feedback loops where user interactions, selections, and corrections are fed back into the machine learning models. This feedback mechanism allows the system to adapt to diverse user attributes while maintaining reliability through controlled, data-driven adjustments rather than arbitrary changes.
3Productivity
If predefined reports and chatbots are used, then interface simplicity is achieved, but only a limited amount of first level insights can be provided and complex analytics require technology teams
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between users and complex analytics systems. This assistant handles the complexity of data analysis, query formulation, and insight generation, while presenting simplified results to users through natural language interfaces.
Solution Approach 2:
The system performs preliminary analysis and preparation of data insights in advance, using machine learning to pre-process and structure information. This allows complex analytics to be ready for rapid delivery when users query, reducing wait times from days to moments.
4Loss of information
If extensive user training is required for software access and data interpretation, then comprehensive data understanding is achieved, but user compliance decreases and business opportunities are lost
Solution Approach 1:
The patent replaces manual user learning and interpretation processes with automated machine learning systems. Instead of users needing to learn complex data structures and analysis methods, the AI system automatically performs these functions, substituting mechanical training requirements with intelligent automation.
Data Source
AI summary
Methods and systems are disclosed for an artificial intelligence (AI)-based, conversational insight and action platform for user context and intent-based natural language processing and data insight generation. Using artificial intelligence and semantic analysis techniques, a knowledge graph is generated from structured data, and a word embedding is generated from unstructured data. A semantic meaning is extracted from a user request, and at least one user attribute and context are determined. One or more entities and relationships on the knowledge graph that match the semantic meaning are determined, based on the user attribute, context, and the word embedding. A sequence of analytical instructions is generated from the matching results, and applied to the structured data to generate a data insight response to the user request. If no matches are found, similar entities and relationships are presented to the user, and user selections are used to further train the system.