Generative AI People Analytics System for Open-Ended Queries
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Solution Overview
Problem
Traditional people analytics systems lack flexibility in accepting open-ended prompts and fail to provide accurate, relevant, and reliable data from multiple sources, limiting users' ability to gain insightful insights due to reliance on keyword matching and unsophisticated AI models.
Innovation Solution
An open-ended prompt and response conversational system that employs generative AI models to process natural language inputs, perform similarity searches, and generate executable expressions to provide comprehensive responses by integrating with a data warehouse, leveraging advanced NLP and machine learning techniques to understand user intent and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional pre-built dashboards with predetermined queries are used, then system simplicity is maintained, but user flexibility and ability to ask open-ended questions are inhibited
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user questions into analytical queries. This mediator component enables users to interact with complex analytics systems using simple natural language, resolving the contradiction between ease of operation and system complexity by shielding users from underlying system complexity while maintaining access to advanced analytical capabilities
Solution Approach 2:
The patent replaces traditional mechanical query interfaces (fixed dashboards, predetermined queries) with an intelligent language-based interface. This substitution allows users to dynamically express analytical needs in natural language rather than being constrained by pre-defined query structures, significantly improving user flexibility without requiring users to understand system complexity
2Reliability
If custom dashboards are built to provide responses to specific analytics questions, then response relevance is improved, but a dedicated team is needed to understand data analysis, process data, write code, and more
Solution Approach 1:
The patent implements self-service analytics where the system automatically processes natural language questions, performs data analysis, and generates responses without requiring dedicated analytical teams. The intelligent system autonomously handles data interpretation, query generation, and result presentation, maintaining high response accuracy while eliminating the need for specialized human teams
Solution Approach 2:
The patent transforms the operational parameters of analytics systems from requiring manual configuration and specialized knowledge to accepting natural language inputs. This parameter change enables the system to maintain reliability and accuracy while dramatically reducing operational complexity and eliminating dependence on dedicated analytical teams
3Measurement precision
If keyword matching and unsophisticated AI models are used, then system simplicity is maintained, but accuracy and relevance of responses are limited
Solution Approach 1:
The patent replaces simple keyword matching mechanisms with sophisticated natural language processing and generative AI models. This substitution dramatically improves measurement precision and response accuracy by enabling semantic understanding of user intentions, while the modular architecture manages the increased system complexity through efficient AI model integration
4Reliability
If sophisticated AI models and natural language processing are implemented, then understanding of user intent and context is improved, but system complexity increases
Solution Approach 1:
The patent segments the sophisticated AI system into modular functional components including natural language processing modules, semantic analysis modules, and response generation modules. This segmentation allows the system to achieve high context understanding capability while managing complexity through organized, independently deployable functional units that can be selectively activated
Data Source
AI summary
The systems and methods described herein provide intelligent people analytics from generative artificial intelligence. In one embodiment, the system: receives a prompt related to people analytics from a client device associated with a user; generates an embedding representation of the received prompt using a generative AI system including one or more generative AI models; performs a similarity search using the generated embedding representation to identify similar prompts that have been submitted before; obtains an executable expression for responding to the received prompt; executes the executable expression using a data warehouse comprising one or more data sources to obtain a response to the received prompt; determines a type of response based on the nature of the received prompt; generates a response output based on the determined type and the response to the received prompt; and provides the response output to the client device associated with the user.