Natural Language Rule Creation for Communication Automation
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Solution Overview
Problem
Existing 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 trial and error.
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 for user-specific terms, and generating automation rules that can be executed or stored for recurring actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional rule-setting processes are used in communication applications, 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 cumbersome interfaces, the system uses an ML model to interpret natural language commands and automatically generate appropriate automation rules, significantly reducing both operational complexity and computing resource consumption.
Solution Approach 2:
The system enables users to create automation rules through simple natural language input without requiring technical expertise. The machine learning model processes the user's natural language command, understands the intended action, and automatically generates the corresponding automation rule, making the system self-serve the rule creation process rather than requiring manual configuration.
2Reliability
If traditional rule-setting processes are used, then automation rules can be created, but they often result in erroneous outcomes requiring trial and error
Solution Approach 1:
The machine learning model processes natural language input and generates automation rules based on understood user intent. The system incorporates feedback mechanisms where the ML model continuously learns from user interactions and adjusts its rule generation accuracy, reducing erroneous outcomes and minimizing the need for trial and error adjustments.
Solution Approach 2:
The system performs preliminary processing of the user's natural language command through the machine learning model before generating the automation rule. This preliminary action involves interpreting the natural language, identifying the intended action, and pre-configuring the rule parameters correctly, thereby preventing erroneous outcomes from the outset rather than requiring subsequent corrections.
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 machine learning model acts as an intermediary between the user's natural language input and the automation rule generation system. This intermediary layer translates complex user intentions expressed in natural language into structured automation rules, simplifying the user interface while managing the underlying system complexity through the ML model's processing capabilities.
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.