Autonomous Spreadsheet Population From Natural Language Queries
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Solution Overview
Problem
Conventional spreadsheets require manual data entry, which can lead to human errors and significant time and computing resource consumption, especially when deriving cell values from other cells.
Innovation Solution
A system and method for autonomously creating personalized documents with a tabular structure using natural language processing to retrieve data from external sources, including subjective and objective data sources, and automatically populate cells based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used to populate spreadsheet cells, then flexibility and control over data input are improved, but human errors increase and time consumption increases
Solution Approach 1:
The system enables autonomous spreadsheets to automatically retrieve and populate their own data from external sources without manual intervention. The spreadsheet autonomously identifies required data, queries external APIs, and updates cells, eliminating human error and time consumption associated with manual data entry while maintaining data flexibility through configurable data sources and update triggers
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with an automated computational system. Natural language processing converts user queries into structured data retrieval operations, and programmatic interfaces automatically fetch and populate data, substituting human manual operations with automated software agents that eliminate errors and reduce time
2Reliability
If manual data entry is used to populate spreadsheet cells, then control over data input is improved, but computing resource consumption increases
Solution Approach 1:
The autonomous spreadsheet automatically manages its own data population by identifying required data elements, formulating queries to external sources, and executing data retrieval operations. This self-service capability eliminates the need for manual data entry while optimizing computing resource usage through intelligent query formulation and selective data retrieval based on spreadsheet requirements
Solution Approach 2:
The system introduces natural language processing as an intermediary layer between user intent and data retrieval operations. This intermediary efficiently translates ambiguous user requests into precise data queries, reducing unnecessary computing operations and optimizing resource consumption while maintaining high data accuracy through multiple validation layers
3Ease of operation
If conventional spreadsheets are used with manual data entry, then ease of use is improved, but productivity decreases
Solution Approach 1:
The autonomous spreadsheet automatically performs data population tasks without requiring manual intervention. Users simply define the spreadsheet structure and data source parameters, then the system autonomously retrieves and populates data, maintaining ease of use while dramatically increasing productivity through automated data acquisition and cell population operations
Solution Approach 2:
The system performs preliminary actions by pre-configuring data retrieval logic and establishing connections to external data sources during spreadsheet creation. This preliminary setup enables rapid data population without requiring manual data entry during usage, thereby maintaining ease of operation while significantly boosting productivity through pre-established automated data workflows
4Productivity
If autonomous data retrieval is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The autonomous spreadsheet system implements a universal framework that handles multiple data retrieval operations through a single integrated architecture. The same core components manage diverse data sources, query formulations, and population operations across different spreadsheet scenarios, thereby achieving high productivity without proportionally increasing system complexity through reusable modular components
Data Source
AI summary
A method includes receiving, by a processing device from a client device associated with a user, a natural language query corresponding to a request to create, for the user, a personalized document having a tabular structure, determining, by the processing device, one or more attribute categories pertaining to the personalized document, identifying, by the processing device, at least one external data source including at least one subjective data source related to the user, retrieving, by the processing device from the at least one external data source, data items indicative of the one or more attribute categories, and generating, by the processing device, the personalized document for the user by populating each cell of the personalized document with a respective data item.