Natural Language Grammars for Interactive Messaging Conversion
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Solution Overview
Problem
Conventional messaging systems lack interactivity and precision in targeting users, resulting in low conversion rates and ineffective message delivery, as they primarily rely on static or non-interactive content.
Innovation Solution
The development of systems and methods that utilize natural language grammars to define experience units, enabling conversational interactions, precise message targeting, and machine learning from user interactions, allowing for interactive and contextually relevant message delivery through human-machine interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional messaging systems use static or non-interactive content, then message delivery is simple and straightforward, but conversion rates are low and targeting precision is poor
Solution Approach 1:
The patent transforms static messaging into dynamic interactive conversations. The system uses natural language processing to enable two-way dialogues between machines and users, where messages adapt based on user responses and conversation context. This dynamic approach significantly improves conversion rates by engaging users in meaningful interactions rather than passive message consumption.
Solution Approach 2:
The patent introduces natural language grammars as intermediaries that bridge the gap between simple message delivery and complex user interactions. These grammars enable the system to interpret user intent, maintain conversation state, and generate contextually appropriate responses, thereby improving targeting precision without requiring overly complex system architecture.
2Adaptability or versatility
If messaging systems add interactivity features, then user engagement improves, but message delivery becomes more complex and harder to measure
Solution Approach 1:
The patent segments interactive messaging into discrete experience units, each with defined grammars and response patterns. This modular approach allows the system to handle complexity in manageable chunks, where each experience unit can be independently designed, tested, and measured. The segmentation makes it easier to track which units generate conversions while maintaining high interactivity.
Solution Approach 2:
The patent uses parameter changes in conversation state to manage interactivity. By tracking variables such as user intent, conversation context, and interaction history, the system dynamically adjusts message content and delivery timing. This parameter-based approach provides clear metrics for measuring interactivity effectiveness while maintaining systematic control over complexity.
3Reliability
If systems deliver contextually relevant messages through natural language interactions, then conversion rates increase, but the complexity of interpreting and responding to user expressions increases
Solution Approach 1:
The patent applies preliminary action by pre-defining natural language grammars and intent patterns before user interactions occur. These pre-configured grammars enable the system to quickly match user expressions to known patterns, reducing the real-time complexity of interpretation. The preliminary structuring of possible user inputs allows for efficient pattern matching while maintaining high conversion rates through contextually relevant responses.
Data Source
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AI summary
Natural language grammars interpret expressions at the conversational human-machine interfaces of devices. Under conditions favoring engagement, as specified in a unit of conversational code, the device initiates a discussion using one or more of TTS, images, video, audio, and animation depending on the device capabilities of screen and audio output. Conversational code units specify conditions based on conversation state, mood, and privacy. Grammars provide intents that cause calls to system functions. Units can provide scripts for guiding the conversation. The device, or supporting server system, can provide feedback to creators of the conversational code units for analysis and machine learning.