Natural Language Intent Parsing for Automatic Reminder Scheduling
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Solution Overview
Problem
Current personal assistant applications require users to explicitly specify locations and times for fulfilling intents, limiting their ability to infer and automatically schedule reminders from unstructured natural language inputs.
Innovation Solution
A system that parses unstructured natural language inputs to infer semantic meanings, determining the intent, location, and time for fulfilling user intentions, and automatically creates reminders for when the user is at the inferred location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users explicitly specify locations and times for fulfilling intents, then the system can reliably create reminders, but the ease of operation decreases as users must provide detailed information
Solution Approach 1:
The system automatically infers location and time information from unstructured natural language inputs and user context data, allowing the reminder system to serve itself by filling in missing details without requiring explicit user specification. This maintains reliability while improving ease of operation.
Solution Approach 2:
The patent introduces an intermediary inference mechanism that bridges between simple user inputs and the structured reminder creation process. This intermediary layer parses natural language, infers missing context, and translates it into actionable reminder parameters, resolving the contradiction between simple input and reliable execution.
2Ease of operation
If the system accepts unstructured natural language inputs without explicit location specifications, then the ease of operation improves, but the measurement precision of location inference decreases
Solution Approach 1:
The system employs feedback mechanisms where inferred location and time information is validated against user context, historical data, and consistency checks. This feedback loop refines the precision of location inference from unstructured inputs while maintaining ease of operation.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and analyzing user context, location history, and semantic patterns before final reminder creation. This preliminary inference work improves location measurement precision from unstructured inputs while keeping the user interface simple.
3Adaptability or versatility
If the system infers location and time from unstructured natural language inputs, then the adaptability increases, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The patent segments the complex natural language processing task into distinct modules: semantic parsing, context extraction, location inference, and time inference. This segmentation manages device complexity by organizing processing functions while maintaining high adaptability for handling diverse natural language inputs.
Solution Approach 2:
The system implements a universal inference engine that handles multiple types of inputs (natural language, structured data) and produces multiple output types (location, time, reminder parameters) through a single integrated processing framework. This multi-functionality increases adaptability while controlling overall system complexity.
Data Source
AI summary
Methods, apparatus, and system to parse an unstructured, natural language input of a user, infer a semantic meaning of an intent of the user, and determine a present or future user state, including a location and a time, in which the intent can be fulfilled.