Table Data Summarizer Using Language Model Conversion
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Solution Overview
Problem
Existing digital marketing technologies face challenges in machine learning capabilities, user interfaces, and computer input/output processes, particularly in extracting and analyzing data from tables and providing effective user experiences.
Innovation Solution
The implementation of a language model that converts table data into natural language sentences, allowing for the generation of summaries and responses to user queries through a chat interface, thereby improving data extraction, user interface features, and reducing computer input/output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP models are used to process table data, then the models can handle free-form natural language text, but they fail to accurately extract and analyze data from tables due to the discrete and context-limited nature of table characters
Solution Approach 1:
The patent introduces an intermediary component that converts table data into natural language text before processing. This intermediary transformation layer bridges the gap between structured table formats and NLP model requirements, enabling accurate data extraction while maintaining model versatility. The conversion process creates a intermediary representation that preserves tabular data semantics while making it compatible with standard NLP processing pipelines.
2Loss of information
If existing user interfaces require manual drilling, paging, and tab selection to locate and analyze different tables, then users can access detailed information, but the process requires extensive manual input and increases computer I/O operations
Solution Approach 1:
The system implements self-service functionality by automatically generating natural language summaries of table data without requiring manual user navigation. The interface autonomously processes and presents information in digestible formats, eliminating the need for users to perform repetitive drilling, paging, and tab selection operations while maintaining full information accessibility.
3Loss of information
If existing user interfaces require extensive manual navigation through multiple pages and tabs, then users can access comprehensive data, but the process reduces productivity and increases time consumption
Solution Approach 1:
The patent merges multiple分散 tables and their corresponding information into unified natural language summaries. By consolidating data from various tables, pages, and tabs into integrated narrative outputs, the system maintains complete information while dramatically improving retrieval efficiency. This merging approach eliminates the need for users to navigate through multiple separate interfaces to access comprehensive data.
Data Source
AI summary
Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.