How Computers Rank Financial News for Analysts
This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning.
Original patent title: “Automated news ranking and recommendation system”
This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning. Granted to S&P Global in 2024 with 21 claims, and it is expected to expire in 2042.
Key facts
Coverage
What does this patent actually cover?
This system first "ingests" (takes in) news articles from many different sources. It then "extracts named entities" (like company names or people) from each article to create a "one-hot vector" for initial grouping. The articles are then "clustered" based on these vectors. For each cluster, a "representative news article" is chosen. The system then uses a "machine learning model" with "character embeddings" and a "convolutional layer followed by a max-pool layer" to understand the meaning of words and sentences in these representative articles. Similar clusters are then "merged" based on their semantic meaning. Finally, the system generates a "set of ranked clusters," which it "digitally displays" in a user interface, allowing analysts to interact with the ranked news. For example, a financial analyst could see a cluster of news about a specific company's earnings report, with the most important articles ranked at the top, and then filter these results.
The gap
What does this patent NOT cover?
- Does not cover news ranking systems that do not use 'one-hot vectors' generated from 'named entities' for initial clustering.
- Does not cover systems that do not employ 'character embeddings' to create 'word representations' for representative articles.
- Does not cover systems that do not use a 'convolutional layer followed by a max-pool layer' to generate input representations for articles.
- Does not cover ranking news articles for general audiences, as it specifically targets 'financial analysts in the capital markets'.
- Does not cover ranking articles within a cluster solely based on publication date without considering 'trustworthiness and linking volume' of the news sources.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in combining specific natural language processing techniques, like named entity extraction for initial clustering and character embeddings with convolutional neural networks for deeper semantic understanding, to create a hierarchical, ranked view of financial news tailored for analysts.
The Patent Drawing
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
S&P Global Market Intelligence platform
Bloomberg Terminal news feeds
Refinitiv Eikon news analysis
FactSet Research Systems news aggregation
Why it matters
The bigger picture
Financial analysts need to quickly process vast amounts of news to make informed decisions. This system automates the complex task of sifting through, categorizing, and prioritizing financial news. By providing relevant, clustered, and ranked information, it helps analysts save time and potentially identify critical market movements or risks faster, which is crucial in fast-paced financial markets.
Filed
April 1, 2022
Granted
March 5, 2024
Market context
Who's building on this
Companies in this space
S&P Global Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively building on this technology to enhance its financial data and analytics platforms. Other major financial data providers like Bloomberg, Refinitiv (LSEG), and FactSet also develop and utilize similar automated news analysis and recommendation systems to serve their institutional clients.
Market impact
This type of automated news ranking system has significantly improved how financial professionals consume and react to market information. It enables financial data and news providers to offer highly curated and personalized news feeds, reducing information overload and allowing analysts to focus on the most impactful stories. This capability has become a standard expectation in high-end financial platforms, driving competition among providers to offer the most accurate and timely insights.
Claim 1 — Plain English
What this patent covers
This system first "ingests" (takes in) news articles from many different sources. It then "extracts named entities" (like company names or people) from each article to create a "one-hot vector" for initial grouping. The articles are then "clustered" based on these vectors. For each cluster, a "representative news article" is chosen. The system then uses a "machine learning model" with "character embeddings" and a "convolutional layer followed by a max-pool layer" to understand the meaning of words and sentences in these representative articles. Similar clusters are then "merged" based on their semantic meaning. Finally, the system generates a "set of ranked clusters," which it "digitally displays" in a user interface, allowing analysts to interact with the ranked news. For example, a financial analyst could see a cluster of news about a specific company's earnings report, with the most important articles ranked at the top, and then filter these results.
The clever bit
The novelty lies in combining specific natural language processing techniques, like named entity extraction for initial clustering and character embeddings with convolutional neural networks for deeper semantic understanding, to create a hierarchical, ranked view of financial news tailored for analysts.
What it does not cover
- Does not cover news ranking systems that do not use 'one-hot vectors' generated from 'named entities' for initial clustering.
- Does not cover systems that do not employ 'character embeddings' to create 'word representations' for representative articles.
- Does not cover systems that do not use a 'convolutional layer followed by a max-pool layer' to generate input representations for articles.
- Does not cover ranking news articles for general audiences, as it specifically targets 'financial analysts in the capital markets'.
- Does not cover ranking articles within a cluster solely based on publication date without considering 'trustworthiness and linking volume' of the news sources.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
14/20
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
$37K – $120K
Midpoint $75K · 15.8 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
21 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Pomerville, S., Liu, X., Kociuba, R., Kim, L., Wang, C., Bang, G., Ma, Z., & Singh, H. (2024). How Computers Rank Financial News for Analysts (U.S. Patent No. 11,922,469). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11922469/automated-news-ranking-and-recommendation-system
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 Computers Rank Financial News for Analysts cover?
This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning.
Who owns patent US 11922469?
S&P Global owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on April 1, 2042, when the invention enters the public domain.
What problem does this patent solve?
Financial analysts need to quickly process vast amounts of news to make informed decisions. This system automates the complex task of sifting through, categorizing, and prioritizing financial news. By providing relevant, clustered, and ranked information, it helps analysts save time and potentially identify critical market movements or risks faster, which is crucial in fast-paced financial markets.
What does this patent NOT cover?
Does not cover news ranking systems that do not use 'one-hot vectors' generated from 'named entities' for initial clustering.
Patent monitoring