Voice Rendering of Machine-Generated Emails via Schema Extraction
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Solution Overview
Problem
Conventional text-to-speech methods struggle to effectively render machine-generated electronic messages, such as emails, due to their repetitive and complex formats, which include images and tables, making it difficult to extract relevant information for users.
Innovation Solution
A system and method that determine an organizational schema for each message, extract and structure relevant data into sub-entities, and generate concise voice renderings based on user queries, allowing for efficient retrieval and playback of specific information from machine-generated emails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If generic text summarization techniques are used to read out machine-generated emails, then the virtual assistant can provide a consecutive and coherent text block, but the results are inadequate because relevant information is fragmented by complex formats including images and tables
Solution Approach 1:
The patent segments the email processing into distinct stages: format detection, schema identification, data extraction, and voice rendering. This segmentation allows the system to handle complex formats by breaking them down into manageable components, extracting relevant information from each segment according to the identified schema, and assembling the final voice output from these processed segments.
Solution Approach 2:
The patent introduces an organizational schema as an intermediary layer between the raw email content and the voice rendering process. This schema acts as a mediator that translates complex, fragmented formats into a structured representation that can be efficiently converted into coherent voice output, bridging the gap between the disorganized source material and the desired output format.
2Loss of information
If the virtual assistant reads through the full email content word by word, then all information is provided, but it becomes ineffective because most content is general and repetitive while only few bits contain useful information
Solution Approach 1:
The patent extracts only the relevant information from the email content based on the identified organizational schema. Instead of processing the entire email text, the system selectively extracts key data elements that match the schema patterns, discarding general and repetitive content. This extraction principle directly addresses the problem of information overload by taking out only the essential useful information.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the email content rather than the complete text. The system identifies and processes only those segments that contain useful information according to the organizational schema, avoiding the excessive action of transcribing every word while ensuring all critical information is captured.
3Ease of operation
If conventional text-to-speech methods are used on machine-generated emails, then the system can produce audio output, but it struggles with repetitive and complex formats including images and tables
Solution Approach 1:
The patent performs preliminary actions by detecting the email format and identifying the organizational schema before the actual voice rendering process. This preliminary analysis prepares the data in advance by structuring it according to the identified schema, making the subsequent text-to-speech conversion straightforward and efficient, thereby simplifying the overall operation despite the complexity of the source formats.
Data Source
AI summary
Disclosed are systems and methods for generating voice renderings of machine-generated electronic messages. The disclosed systems and methods provide a novel framework for organizing often fragmented machine-generated electronic messages and providing mechanisms for a virtual assistant to produce voice-renderings data extracted from electronic messages. The disclosed system may implement steps for receiving user queries via virtual assistants, extracting data from machine-generated electronic messages, converting the extracted data to purposeful organizational schemas, and generating human perceivable voice renderings based on the user queries and extracted data.