Natural Language Rule Generation for Communication Filtering
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Solution Overview
Problem
Modern communication applications face challenges in efficiently processing large volumes of electronic communications due to cumbersome rule-setting processes that consume computing resources and often result in erroneous outcomes, leading to user frustration and inefficiency.
Innovation Solution
A machine-learning model processes natural language inputs to generate automation rules by converting user intent into tagged primitives and actions, utilizing a resolution database to resolve user-specific terms, and generating accurate automation rules for filtering and acting on communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional rule-setting processes are used to process communications, then users can create automation rules, but the process is cumbersome and consumes excessive computing resources
Solution Approach 1:
The patent replaces the traditional mechanical rule-setting process with a machine learning-based natural language processing system. Instead of requiring users to manually configure complex automation rules through intuitive interfaces, the system uses ML models to automatically interpret natural language commands and generate appropriate automation rules, thereby reducing computing resource consumption while maintaining ease of use
Solution Approach 2:
The system enables users to create automation rules through simple natural language commands without needing to understand the underlying complex configuration processes. The machine learning model processes the natural language input and automatically generates the appropriate automation rules, making the system self-serve the rule creation task and eliminating the need for users to manually configure complex parameters
2Reliability
If traditional rule-setting processes are used, then users can create automation rules, but the rules often result in erroneous outcomes
Solution Approach 1:
The patent incorporates feedback mechanisms where the machine learning model continuously learns from user interactions and adjusts its rule generation capabilities. The system processes natural language inputs and provides feedback loops that allow users to correct or refine generated rules, improving accuracy over time while maintaining user-friendly operation
Solution Approach 2:
The system replaces manual rule configuration with machine learning-based automated rule generation that processes natural language commands. This substitution ensures more accurate rule generation by leveraging the ML model's ability to interpret user intent correctly, reducing erroneous outcomes while keeping the interface simple and easy to use
3Ease of operation
If machine learning models process natural language inputs to generate automation rules, then ease of use and accuracy improve, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the user's natural language inputs and the automation rule execution system. These intermediaries process and translate complex natural language commands into structured automation rules, managing the system complexity internally while presenting a simple interface to users
Solution Approach 2:
The machine learning-based system serves multiple functions: it processes natural language inputs, generates automation rules, and can adapt to different communication scenarios. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified platform, managing complexity through integration rather than separation
Data Source
AI summary
Methods and systems for generating automation rules based on natural language inputs. In an example, the technology relates to a computer-implemented method for generating automation rules from natural language input. The method includes receiving a natural language input into a communications application for performing an action on communications received by the communications application; providing the natural language input into a trained machine learning model; receiving, as output from the trained machine learning model, a tagged primitive and an identified action from the natural language input; generating an automation rule for performing the action on a subset of communications received by the communications application, the subset of communications corresponding to the tagged primitives; and executing the generated automation rule to perform action on the subset of communications.