Event Record Templates for Accurate Audio Conversation Summaries
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Solution Overview
Problem
Generating accurate and succinct records of events or conversations from audio data is challenging due to the limitations of existing large language models (LLMs) in processing nuances and requiring excessive user input, often leading to incomplete or inaccurate summaries.
Innovation Solution
Utilizing event templates with placeholder, generator, and verbatim intent fields, and prompting LLMs to populate these fields with information from audio transcripts, with re-prompting for missing details and allowing user editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If LLMs are used to summarize events or conversations based on audio data, then the record generation becomes automated, but the LLMs may randomly select unimportant parts or omit important details
Solution Approach 1:
The patent segments the record generation process into multiple distinct fields within an event template (e.g., summary field, participant field, location field, outcome field). Each field is populated separately through targeted prompting, ensuring comprehensive coverage of important details while maintaining automation. This segmentation prevents the LLM from randomly selecting only certain parts of the conversation.
Solution Approach 2:
The event template serves as an intermediary structure between the audio input and the final record. The template provides a predetermined framework with specific fields that guide the LLM to extract and organize information systematically, ensuring that important details are captured according to a structured format rather than random selection.
2Extent of automation
If LLMs are prompted to generate records from audio transcripts, then record creation is automated, but input prompt limits and output size constraints restrict the comprehensiveness of the record
Solution Approach 1:
The patent divides the record into multiple segmented fields within the event template (summary, participants, location, outcome, etc.). Each field can be populated independently within the prompt limits, allowing comprehensive information capture across all fields without exceeding the overall input/output constraints of the LLM.
Solution Approach 2:
The patent transitions from a single-dimensional summary approach to a multi-dimensional event template structure with multiple fields. This dimensional expansion allows the system to capture comprehensive information across different aspects of the event simultaneously, overcoming the limitations of linear prompt length constraints.
3Manufacturing precision
If healthcare providers manually generate medical records at the end of the workday, then comprehensive records can be created, but it is time-consuming and burdensome
Solution Approach 1:
The patent implements preliminary action by automatically generating the medical record immediately after the patient interaction concludes, rather than delaying until the end of the workday. The system processes the audio recording and populates the event template in real-time or near-real-time, capturing details while they are still fresh in memory.
Solution Approach 2:
The system enables self-service by automatically generating the medical record without requiring manual intervention from the healthcare provider. The LLM processes the audio transcript and fills out the event template independently, eliminating the burden of manual record creation while maintaining completeness.
4Measurement precision
If the entire recorded audio or transcript is reviewed to generate an accurate record, then accuracy improves, but the process becomes time-consuming
Solution Approach 1:
The patent extracts only the essential information needed for each specific field of the event template from the audio transcript, rather than reviewing the entire transcript in detail. The LLM selectively identifies and extracts relevant details for each field (e.g., extracting participant names for the participant field, key outcomes for the outcome field), improving efficiency while maintaining accuracy.
Solution Approach 2:
The system performs partial action by focusing on extracting only the necessary information for each template field rather than comprehensively analyzing the entire transcript. This selective approach achieves sufficient accuracy for record generation without the time cost of complete transcript review.
Data Source
AI summary
A method of generating a record for an event based on audio data of the event involves retrieving an event template and determining whether the event template includes a placeholder intent field and/or a generator intent field. If the event template includes the placeholder intent field, a machine learning model may be prompted to search a transcript of the audio data for placeholder information to populate the at least one placeholder intent field. This may involve determining if the placeholder information is absent from the transcript, and if the placeholder information is absent from the transcript, prompting the machine learning model to generate a re-prompt to a user to provide the placeholder information. If the event template includes the generator intent field, the machine learning model may be prompted to process the transcript to generate generator information to populate the at least one generator intent field.