AI Assistant Context Injection for Enterprise Communication
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Solution Overview
Problem
Current digital writing assistants are limited in addressing communication effectiveness, as they primarily focus on correcting content errors without incorporating knowledge and context from communication exchanges.
Innovation Solution
A computer-implemented system that uses an artificial intelligence assistant to process user prompts and context, generating engineered prompts for large language models (LLMs) to produce more effective and relevant responses by accessing enterprise-specific documents and user communication profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If public LLMs are used to generate text responses, then the system is simple and accessible, but the responses are inadequate or incomplete due to inability to access application-specific training data
Solution Approach 1:
The patent introduces an intermediary system that sits between the user and the public LLM. This intermediary retrieves application-specific context and training data from enterprise document stores, then injects this information into the LLM's context window before generating responses. This mediator layer enables access to proprietary data without requiring the LLM itself to be retrained or modified.
Solution Approach 2:
The system segments the response generation process into distinct components: (1) context retrieval from enterprise documents, (2) prompt engineering with retrieved context, (3) LLM inference, and (4) response generation. This segmentation allows each component to be optimized independently while maintaining overall system reliability.
2Adaptability or versatility
If enterprise-specific documents are accessed to improve response relevance, then the quality of responses improves, but the system complexity and data access requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing enterprise documents in structured formats suitable for rapid retrieval. Context-relevant information is extracted and organized before being needed, allowing the LLM to access pre-digested knowledge rather than searching raw documents during inference.
Solution Approach 2:
An intermediary retrieval system acts as a bridge between enterprise document stores and the LLM. This mediator handles document parsing, context extraction, and information formatting, shielding the LLM from the complexity of raw document access while providing refined, relevant context.
3Ease of operation
If simple writing assistant corrections are provided, then the system is easy to operate, but communication effectiveness is limited as only content errors are addressed
Solution Approach 1:
The system evolves from a single-function grammar checker to a multi-functional communication assistant. It simultaneously performs (1) traditional grammar and spelling correction, (2) context-aware content generation, (3) style adaptation based on communication profiles, and (4) effectiveness optimization. This universal approach maintains ease of use while dramatically improving communication effectiveness.
Solution Approach 2:
The system implements feedback loops where user interactions, communication outcomes, and effectiveness metrics are continuously monitored. This feedback informs prompt engineering adjustments and context retrieval strategies, enabling the system to learn and improve communication effectiveness while maintaining user-friendly operation.
Data Source
AI summary
A method of electronic communication assistance involves receiving a partial electronic communication at an AI assistant computing facility from a first electronic identifier linked to a first user. This communication includes content associated with both the first user and a second user. The method extracts the communication context and encodes the partial communication for processing, creating an encoded version. It retrieves the first user's communication profile from a database using their identifier, containing user attributes, and retrieves the second user's profile similarly. The encoded communication is then processed by a processor to generate a compositional change using at least one of the communication context, the first user communication attribute, or the second user communication attribute. Finally, a revised electronic communication is generated from the partial communication and the compositional change.