Natural Language Pub/Sub Matching for Tailored Notifications
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Solution Overview
Problem
Existing publish/subscribe systems lack flexibility and expressiveness in handling natural language queries and subscriptions, leading to inefficient and non-customized notifications.
Innovation Solution
A computer-implemented method and system that utilizes machine learning models, such as Large Language Models (LLMs), to match natural language publications with subscriptions in a vector database, generating customized notifications based on the content of both.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strictly defined schemas are used for publications and subscriptions, then system reliability is improved, but publication and subscription expressiveness deteriorates
Solution Approach 1:
The patent introduces an intermediary layer (the processing system with LLM) between the strict schema-based Pub/Sub system and the natural language queries/publications. This intermediary translates natural language expressions into schema-compliant formats for matching, while maintaining the reliability of the underlying schema-based system. The intermediary handles the expressiveness requirement without compromising the reliability of the core Pub/Sub mechanism.
Solution Approach 2:
The patent changes the parameter of how publications and subscriptions are represented. Instead of requiring users to directly specify strict schemas, the system accepts natural language parameters (queries and publications) and internally transforms them into schema-compliant representations for processing. This parameter change allows flexible user input while maintaining reliable schema-based matching underneath.
2Adaptability or versatility
If machine learning models are used to match natural language publications with subscriptions, then publication and subscription expressiveness is improved, but device complexity increases
Solution Approach 1:
The processing system acts as an intermediary layer that sits between the existing Pub/Sub infrastructure and the natural language processing components. The LLM-based matching mechanism is encapsulated within this intermediary, allowing the core Pub/Sub system to remain relatively simple while gaining enhanced expressiveness through the intermediary's natural language capabilities.
Solution Approach 2:
The patent uses vector embeddings as a simplified representation (copy) of natural language publications and subscriptions. Instead of working with complex natural language directly in the matching process, the system creates vector representations that capture the essence of the natural language content, enabling ML-based matching while reducing the immediate complexity of handling raw natural language in the core matching logic.
3Ease of operation
If natural language queries are used instead of strict schemas, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The processing system with LLM serves as an intermediary that translates natural language queries into precise schema-based matching criteria. This intermediary maintains the ease of operation by accepting natural language input while ensuring measurement precision by using the LLM's understanding to accurately interpret and match the intended query against the schema-based publication data.
Solution Approach 2:
The system incorporates feedback mechanisms where the LLM processes natural language queries and provides refined matching results. The feedback loop ensures that the imprecision inherent in natural language is compensated for by the LLM's contextual understanding and the vector-based matching, achieving both ease of operation and sufficient precision for effective query matching.
Data Source
AI summary
A computer product and methodology is provided for optimizing publication and subscription expressiveness. A first computerized subscription presenting a first query for desired information is established. A first computerized publication having first published information is identified. A determination is made whether the first computerized publication matches the first computerized subscription. After determining a match, the first computerized publication is combined with the first computerized subscription as a first data pair. The first data pair is employed to inference a first notification having an expression of the first query's desired information.