How AI Cleans Up Irrelevant Topics in Recorded Phone Calls
A system that automatically identifies and removes 'noisy' or irrelevant topics from call center transcripts by analyzing how consistently and broadly those words appear.
Original patent title: “Detecting extraneous topic information using artificial intelligence models”
A system that automatically identifies and removes 'noisy' or irrelevant topics from call center transcripts by analyzing how consistently and broadly those words appear. Granted to Invoca Inc in 2022 with 20 claims and 1 forward citation.
Key facts
Coverage
What does this patent actually cover?
This patent describes a way to make AI-powered call analysis more accurate by filtering out 'extraneous' topics that don't provide useful business insights. It works by looking at call transcripts and calculating two specific metrics for each identified topic: a vocabulary score (based on the entropy of word distributions) and a consistency score (how often that topic appears across different calls). If a topic is too broad or lacks consistency, the system flags it as noise. It then automatically updates the AI model to either remove that topic from the results or visually separate it, ensuring that human analysts only see the most relevant conversation data.
The gap
What does this patent NOT cover?
- Does not cover general speech-to-text transcription methods.
- Does not cover manual topic tagging performed by human agents.
- Does not cover sentiment analysis or emotion detection in calls.
- Does not cover real-time audio processing (it requires pre-stored transcripts).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system uses 'burst concentration' and entropy-based vocabulary scoring to mathematically define what makes a topic 'extraneous,' effectively turning a subjective human judgment (is this topic noise?) into a repeatable, automated filter.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Call center analytics platforms
Automated customer service quality assurance software
Sales intelligence tools for tracking prospect objections
Why it matters
The bigger picture
In call centers, AI models often get distracted by 'filler' topics like standard greetings or background noise, which dilutes the value of data analytics. By automating the cleanup of these topics, Invoca's system helps businesses focus on actionable insights, such as specific customer pain points or sales objections, without needing manual data scrubbing.
Filed
August 18, 2020
Granted
December 6, 2022
Market context
Who's building on this
Companies in this space
Invoca is the primary assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more → and continues to build this into their conversation intelligence platform. Other companies in the conversational AI space, such as Gong, Chorus.ai, and various enterprise-grade contact center software providers, utilize similar filtering techniques to clean up transcript data.
Market impact
This patent supports the shift toward 'clean' conversation intelligence, where the value lies not just in transcribing calls, but in refining the data so it is actually useful for business decision-making. It helps standardize how AI models handle noise in large-scale voice data, reducing the need for expensive manual data labeling.
Claim 1 — Plain English
What this patent covers
This patent describes a way to make AI-powered call analysis more accurate by filtering out 'extraneous' topics that don't provide useful business insights. It works by looking at call transcripts and calculating two specific metrics for each identified topic: a vocabulary score (based on the entropy of word distributions) and a consistency score (how often that topic appears across different calls). If a topic is too broad or lacks consistency, the system flags it as noise. It then automatically updates the AI model to either remove that topic from the results or visually separate it, ensuring that human analysts only see the most relevant conversation data.
The clever bit
The system uses 'burst concentration' and entropy-based vocabulary scoring to mathematically define what makes a topic 'extraneous,' effectively turning a subjective human judgment (is this topic noise?) into a repeatable, automated filter.
What it does not cover
- Does not cover general speech-to-text transcription methods.
- Does not cover manual topic tagging performed by human agents.
- Does not cover sentiment analysis or emotion detection in calls.
- Does not cover real-time audio processing (it requires pre-stored transcripts).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$75K – $240K
Midpoint $150K · 14.2 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
20 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
McCourt, M., & Lawrence, M. (2022). How AI Cleans Up Irrelevant Topics in Recorded Phone Calls (U.S. Patent No. 11,521,601). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11521601/stylegan
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI Cleans Up Irrelevant Topics in Recorded Phone Calls cover?
A system that automatically identifies and removes 'noisy' or irrelevant topics from call center transcripts by analyzing how consistently and broadly those words appear.
Who owns patent US 11521601?
Invoca Inc owns this patent, granted in 2022.
When does this patent expire?
This patent is expected to expire on December 6, 2042, when the invention enters the public domain.
What is patent US 11521601 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
In call centers, AI models often get distracted by 'filler' topics like standard greetings or background noise, which dilutes the value of data analytics. By automating the cleanup of these topics, Invoca's system helps businesses focus on actionable insights, such as specific customer pain points or sales objections, without needing manual data scrubbing.
What does this patent NOT cover?
Does not cover general speech-to-text transcription methods.
Patent monitoring