Intelligent Document System With Embedded LLM For Dynamic Interaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital document systems are static and limit user interaction, making it difficult for users to engage with the content, leading to inefficiencies and a fragmented learning experience.
Innovation Solution
An intelligent document system that embeds a compressed large language model, allowing users to interact dynamically with documents through an AI conversation interface, enabling queries, annotations, and personalized experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static document reading system is used, then the system structure is simple, but user interaction capability is limited
Solution Approach 1:
The patent embeds a compressed large language model within the document file itself, creating a nested structure where the AI model is contained inside the document format. This allows the document to gain intelligent interaction capabilities while maintaining a relatively simple overall system architecture, as the complexity is encapsulated within the document rather than requiring a complex external system.
Solution Approach 2:
The patent creates a simplified local version of a large language model by compressing and embedding it within the document. This copied, compressed version provides the essential AI capabilities for user interaction without requiring the full complexity of a complete large language model system, thus improving interaction capability while controlling system complexity.
2Adaptability or versatility
If multiple applications are used to achieve desired results, then user interaction capability is enhanced, but system complexity increases
Solution Approach 1:
The patent merges multiple functions (reading, annotating, analyzing, and AI-based interaction) into a single document format by embedding the compressed large language model directly within the document. This consolidation eliminates the need for multiple separate applications, enhancing document functionality while reducing overall system complexity.
Solution Approach 2:
The patent makes the document multi-functional by embedding a compressed large language model that enables various interaction modes including querying, summarizing, analyzing, and annotating. This single document structure performs multiple functions that would traditionally require separate applications, thereby improving versatility without increasing the number of applications needed.
3Adaptability or versatility
If traditional document reading is used, then the system is easy to operate, but user engagement is limited
Solution Approach 1:
The patent enables the document to serve itself by embedding a compressed large language model that automatically processes user queries, generates responses, creates annotations, and performs analysis directly within the document. This self-service capability enhances user engagement through active interaction while maintaining ease of operation, as users can interact with the document using simple natural language without needing to master complex tools or workflows.
Data Source
AI summary
Disclosed are various embodiments for an advanced and intelligent document system. A computing device can show a user interface on a display of the computing device, wherein at least a portion of an intelligent dynamic document is presented within the user interface. The computing device can then receive a prompt via the user interface. Subsequently, the computing device can execute a large language model (LLM) to generate a response to the prompt, wherein the LLM is embedded within the intelligent dynamic document and the response is based at least in part on the content of the intelligent dynamic document. Finally, the computing device can present the response within the user interface.