Event Context Extraction for LLM-Generated Product Data Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data analysis processes are time-consuming, computationally expensive, and require specialized expertise, leading to inefficiencies, biases, and inaccuracies, particularly in handling large-scale datasets or real-time analytics, and there is a need for automated data-driven decision-making and user-friendly interfaces for digital product analysis.
Innovation Solution
An event-based analysis using Large Language Models (LLMs) processes data sources to generate an event catalog, which includes contextual information of digital product events, enabling automated data queries and insights without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data analysis is performed using language models, then productivity and accessibility are improved, but device complexity and computational cost increase
Solution Approach 1:
The system segments the data analysis process into distinct modules: event context data preparation, prompt generation, language model inference, and result presentation. This modular architecture allows each component to be optimized independently while maintaining overall system productivity.
Solution Approach 2:
The patent introduces an intermediary event context data structure that bridges the inquiry and the language model. This intermediary layer preprocesses and structures the input data, reducing the computational burden on the language model while maintaining analysis accuracy.
2Measurement precision
If event context data is pre-prepared from multiple data sources, then measurement precision is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-preparing event context data from multiple data sources before the actual analysis inquiry is processed. This includes extracting event names, parameters, and descriptions in advance, which improves measurement precision when the data is needed.
Solution Approach 2:
The patent applies local quality by selectively preparing event context data based on the specific inquiry type. Not all event data is processed with the same level of detail - only the relevant event contexts corresponding to the inquiry are extensively prepared, reducing overall time loss.
3Ease of operation
If language models generate data queries automatically, then ease of operation is improved, but reliability and accuracy may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the generated data queries are validated against the event context data and database schema. This feedback loop ensures that automatically generated queries maintain high reliability by checking consistency and correctness before execution.
Solution Approach 2:
The language model performs self-service by automatically generating appropriate data queries based on the inquiry and event context. This self-service capability improves ease of operation while maintaining reliability through the model's trained understanding of data structures and query syntax.
Data Source
AI summary
A method, product, and apparatus for performing automated data analysis of a digital product, comprising: obtaining an inquiry representing a data analysis task associated with the digital product; performing the data analysis task automatically based on a database of the digital product by: generating a prompt for a language model based on the inquiry and based on event context data that comprises a plurality of event entries and sub-entries for parameters of the event, an entry comprises a name of an event and a textual description of the event, said generating comprises incorporating within the prompt an instruction to generate a data query; feeding the prompt to the language model; obtaining the data query; executing the data query over the database; and determining data values based on said executing; and presenting an answer to the inquiry.