Transcript Summarization With Contextual Name-Spelling Correction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional speech-to-text and summarization systems lack the ability to reliably identify and correct misspellings and inconsistencies, particularly in user names, due to a lack of contextual awareness.

Innovation Solution

Utilizing named entity recognition models and additional sources like employee listings and contact logs to identify likely errors, and applying fuzzy logic for probabilistic matching to correct misspellings in transcripts and summaries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If speech-to-text technologies are used to generate transcripts, then transcript generation is achieved, but misspellings and inconsistencies are introduced

Engineering Contradiction:

Improvetranscript generation efficiencyVSAvoidspelling accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback by using named entity recognition models to identify potential errors in transcripts and summaries, then applies corrections based on contextual information from employee listings and contact logs. This closed-loop feedback mechanism continuously improves spelling accuracy without sacrificing generation efficiency.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces intermediary components including named entity recognition models and contextual data sources (employee listings, contact logs) that mediate between the speech-to-text generation process and the final transcript output. These intermediaries filter and correct errors without directly interfering with the core speech-to-text functionality.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If conventional summarization systems are used, then summarization is achieved, but errors are reproduced without correction

Engineering Contradiction:

Improvesummarization efficiencyVSAvoiderror correction accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system performs preliminary actions by consulting employee listings and contact logs before finalizing the summary output. This advance preparation allows the system to proactively identify and correct potential errors in names and entities before they are propagated to the final summary, ensuring higher precision without compromising summarization speed.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If contextual information sources are integrated for error correction, then spelling accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvename spelling accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies universality by using a single integrated processing system that handles multiple functions: speech-to-text transcription, named entity recognition, error identification, and correction using contextual data from multiple sources. This multi-functional approach consolidates what could be separate complex systems into one unified architecture, reducing overall system complexity while maintaining high spelling accuracy.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250259620A1Apparatuses and methods for facilitating a transcript summarization with spelling corrections

Publication Date: 2025.08.14 JPMORGAN CHASE BANK NA

AI summary

Aspects of the subject disclosure may include, for example, obtaining a model, obtaining input audio, processing the input audio to generate a transcript, obtaining at least one summary based on the transcript, identifying at least one error or inconsistency in the transcript or the at least one summary based on the model, and implementing, based on the identifying, a correction or a clarification in respect of the at least one error or inconsistency. Other aspects are disclosed.