LLM Spreadsheet Visualization Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spreadsheet applications with integrated AI models are limited to spreadsheet data and functionalities, while large language models (LLMs) introduce latency, require significant compute power, and can hallucinate, leading to ineffective integrations.
Innovation Solution
The integration of spreadsheet environments with LLM services, where natural language inputs from users are processed to generate prompts for LLMs, allowing for modifications to visualizations based on the LLM's replies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM services are integrated into spreadsheet applications, then natural language processing capability and visualization modification ability are improved, but latency increases and user experience deteriorates
Solution Approach 1:
The patent segments the LLM service into a separate external component rather than embedding it within the spreadsheet application. The application generates prompts and sends them to the external LLM service, receiving responses asynchronously. This segmentation isolates the latency-inducing LLM processing from the core application, allowing the UI to remain responsive while LLM operations complete in the background.
Solution Approach 2:
The patent introduces an intermediary layer (the prompt generation and response processing mechanism) between the user interface and the LLM service. This intermediary manages the communication flow, formats prompts appropriately, and processes responses in a way that minimizes the impact of LLM latency on user experience.
2Adaptability or versatility
If LLM services are integrated into spreadsheet applications, then natural language processing capability is improved, but compute power requirements increase
Solution Approach 1:
The patent positions the LLM service as an external intermediary that handles the computationally intensive natural language processing tasks. Rather than requiring the spreadsheet application itself to possess LLM capabilities, the application acts as a thin client that communicates with the external LLM service, thereby avoiding the need to embed heavy compute resources within the application.
Solution Approach 2:
The patent leverages the universal, pre-trained capabilities of external LLM services that have already been trained on vast amounts of data across multiple domains. This allows the spreadsheet application to access powerful natural language processing capabilities without having to train or maintain its own specialized models, thus avoiding excessive compute power requirements.
3Adaptability or versatility
If LLM services are integrated into spreadsheet applications, then natural language processing capability is improved, but reliability decreases due to hallucinations
Solution Approach 1:
The patent implements a feedback mechanism where the application sends structured prompts containing specific spreadsheet context and data to the LLM service, and processes the LLM's responses to generate appropriate visualization modifications. This closed-loop feedback system helps ground the LLM's generative capabilities in the actual spreadsheet data, reducing hallucinations by continuously verifying information against the source data.
Solution Approach 2:
The patent extracts and removes the problematic hallucination-prone generative aspects of LLMs from direct user interaction. Instead of allowing the LLM to freely generate responses, the system extracts only the necessary information from the spreadsheet data, formulates precise prompts, and uses the LLM's capabilities in a constrained manner that minimizes hallucination risks while preserving natural language processing benefits.
4Adaptability or versatility
If LLM services are integrated into spreadsheet applications, then natural language processing capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the system into distinct components: the spreadsheet application, the prompt generation module, the LLM service interface, and the response processing module. Each component has a specific, well-defined function, which simplifies the overall integration complexity compared to embedding a complete LLM within the application. This modular segmentation makes the system easier to implement, maintain, and update.
Data Source
AI summary
Technology is disclosed herein for the integration of spreadsheet environments with an LLM service. In an implementation, an application receives a natural language input from a user associated with visualization of data hosted by the application. The application generates a prompt for a large language model (LLM) service based on the user input and the visualization and submits the prompt to the LLM service. The application receives a reply to the prompt from the LLM service and modifies the visualization based on the reply from the LLM service. In an implementation, the data includes spreadsheet data and the visualization includes a chart. In some implementations, to modify the visualization, the application generates source code based on the reply from the LLM service when the classification of the natural language input is a command.