Automated Assistant Template Selection for Complex Message Composition
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Solution Overview
Problem
Automated assistants struggle to efficiently handle complex message composition, requiring users to rely on keyboard interfaces and consuming additional time and computational resources, especially when drafting similar messages, as they lack the ability to effectively process and generate messages with multiple layers of complexity.
Innovation Solution
An automated assistant that creates content templates based on user requests, selecting the most relevant template by processing audio data and contextual information using machine learning models, and populating dynamic sections with relevant data to streamline message creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users rely on keyboard interfaces to draft complex messages, then message structure complexity can be achieved, but time consumption and computational resource usage increase
Solution Approach 1:
The system pre-generates multiple message drafts with different structural complexities before the user needs to send a message. When a user requests to send a message, the system has already prepared various template options (simple, moderate, complex structures) that can be quickly selected and customized, eliminating the need for users to manually construct complex messages from scratch using keyboard interfaces.
Solution Approach 2:
The system creates simplified copies or templates of complex message structures that can be rapidly instantiated. Instead of requiring users to build complex messages using detailed keyboard input, the system provides pre-formed message templates that replicate the essential structure and content of complex communications, which users can then lightly customize before sending.
2Stability of the object's composition
If users manually review past messages to draft similar messages, then message consistency can be improved, but computational resources and time are wasted
Solution Approach 1:
The system automatically analyzes the user's own past messaging patterns, communication styles, and preferred structures without requiring manual review. The system self-generates message drafts that are consistent with the user's historical behavior by processing and learning from past messages autonomously, thereby maintaining message consistency while eliminating the computational overhead and time associated with manual review processes.
3Productivity
If speech-to-text features are used for message drafting, then transcription speed is improved, but limitation to simple commands reduces message complexity
Solution Approach 1:
The system merges the speed advantage of speech-to-text transcription with the structural capability of pre-generated complex message templates. Users can provide simple spoken commands that are rapidly transcribed, and the system automatically maps these commands to appropriate complex message templates, combining the efficiency of voice input with the sophistication of structured message composition without requiring users to manually construct complex messages.
Data Source
AI summary
Implementations set forth herein relate to an automated assistant that facilitates the creation of complex messages from user input(s) to the automated assistant. Each message can be created according to a respective template that is selected based on user input that directs the automated assistant to communicate a message to a recipient. Furthermore, sections of a template can be designated for certain content based on prior messages communicated by one or more users to one or more recipients. In this way, in response to a user requesting that the automated assistant send a message, the automated assistant can select a related template and fill out the template accordingly. In some instances, content that is assigned to certain sections of the selected template can come from a variety of different sources and/or may not be explicitly specified in the request from the user to the automated assistant.