Cognizant AI Lab Earns Three New U.S. Patents, Totaling 65 U.S. Patents and 88 International Patents

Rhea-AI Summary

Cognizant (NASDAQ: CTSH) announced its AI Lab received three new U.S. patents, raising its U.S. total to 65 patents and 88 global patents as of April 23, 2026. The patents cover prescriptive decision systems, automatic activation-function tuning, and distributed ML knowledge sharing.

The grants were issued Feb–Mar 2026 and are intended to advance human-AI collaboration, model adaptability, and reuse across teams.

Positive

• U.S. patents: total increased to 65
• Global patents: total increased to 88
• Three patents issued in 2026: Feb 24, Mar 3, Mar 10
• Patent topics: prescriptors, activation-function tuning, distributed ML metadata

Negative

• None.

News Market Reaction – CTSH

-6.31%

26 alerts

-6.31% Session close to close

-2.5% Trough in 34 min

$27.87B Market Cap

0.2x Rel. Volume

In the Apr 23 session, CTSH declined 6.31%, reflecting a notable negative market reaction. Argus tracked a trough of -2.5% from its starting point during tracking. Our momentum scanner triggered 26 alerts that day, indicating elevated trading interest and price volatility.

Data tracked by StockTitan Argus on the day of publication.

Market Context

The stock moved -6.3% in the session following this news. A negative reaction despite additional AI ...

Analysis

The stock moved -6.3% in the session following this news. A negative reaction despite additional AI patents fits a pattern where AI-tagged news has averaged a -0.91% move over recent events. The stock was already trading below its 200-day MA and close to its 52-week low, so sentiment had been cautious. Any pronounced downside move could have reflected that existing skepticism, even as the company added to its patent base and highlighted prior growth and margin expansion in proxy disclosures.

Key Figures

New U.S. patents: 3 patents Total U.S. patents: 65 patents Total global patents: 88 patents +5 more

8 metrics

New U.S. patents 3 patents Newly granted to Cognizant AI Lab

Total U.S. patents 65 patents Cognizant AI Lab cumulative U.S. patents

Total global patents 88 patents Cognizant AI Lab cumulative international patents

2025 revenue $21.1 billion Reported in 2026 proxy statement

Revenue growth 7.0% year-over-year 2025 vs prior year, per proxy

GAAP margin expansion 140 basis points GAAP operating margin 2025 vs prior year

Adj. margin expansion 50 basis points Adjusted operating margin 2025 vs prior year

AI-trained associates 340,000+ associates Completed AI training over ~2.5 years

Previous AI Reports

5 past events · Latest: Apr 22 (Positive)

Same Type Pattern 5 events

Date	Event	Sentiment	24h Move	Catalyst
Apr 22	AI product launch	Positive	-3.4%	Launch of Agentic Retail CX on Google Cloud’s Gemini Enterprise for CX.
Apr 21	AI partnership	Positive	+0.3%	Selection by OpenAI to help scale Codex across enterprise clients.
Apr 21	AI platform launch	Positive	+0.3%	Launch of Cognizant Skillspring AI-native workforce training platform.
Mar 16	AI infra launch	Positive	+0.8%	Launch of Cognizant AI Factory with Dell and NVIDIA for AI infrastructure.
Mar 10	AI research report	Neutral	-2.6%	Research showing enterprises prefer custom full‑stack AI builder services.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

Recent AI-tagged announcements show mixed but generally modest price moves, with an average 24h reaction of about -0.91%, and one notable negative reaction to a major retail AI launch.

Recent Company History

Over the past months, CTSH has issued multiple AI-focused updates, including launches of Agentic Retail CX on Apr 22, 2026, an OpenAI Codex partnership and the Skillspring workforce training platform on Apr 21, 2026, the Cognizant AI Factory infrastructure on Mar 16, 2026, and AI strategy research on Mar 10, 2026. Price reactions around these AI announcements were generally small, with only the retail CX launch drawing a sharper -3.41% move. The new AI patent grants extend this ongoing AI-builder narrative.

Key Terms

human-ai collaboration, deep learning, decision-recommendation systems, activation functions, +3 more

7 terms

human-ai collaboration technical

"areas such as human-AI collaboration for decision-making and deep learning"

Human-AI collaboration is a working relationship where people and software systems share tasks: humans provide judgement, creativity and oversight while AI handles data processing, pattern recognition and repetitive work. For investors it matters because this pairing can boost productivity, reduce errors and create new revenue streams or cost savings—think of AI as a power tool that makes skilled workers faster and more accurate, which can change a company’s growth and risk profile.

deep learning technical

"collaboration for decision-making and deep learning for specialized tasks."

Deep learning is a type of artificial intelligence that uses multiple layers of computer models to recognize patterns and make decisions from large amounts of data, similar to how someone improves at a task by practicing many examples. Investors care because companies that harness deep learning can automate work, improve products, cut costs or create new revenue streams — but adoption also involves investment, data and regulatory risks that can influence profits and valuation.

decision-recommendation systems technical

"Improves decision-recommendation systems, or "prescriptors," by evolving"

Automated tools that analyze data and recommend specific actions or choices, similar to a GPS that suggests the fastest route based on current traffic. They turn large, messy information into clear, ranked options or scores so managers can act faster and more consistently. Investors care because these systems can change a company’s costs, speed of decisions, regulatory risk and competitive edge, and their mistakes or biases can materially affect earnings and reputation.

activation functions technical

"Automatically creates and tunes activation functions—core "on/off" switches inside"

Activation functions are mathematical rules inside artificial neural networks that decide how incoming signals are transformed as they move between processing layers. They determine a model’s ability to learn, recognize patterns and make decisions—like a gatekeeper that amplifies useful signals and suppresses noise—so they affect prediction accuracy, computational cost and the risk of errors or biased outputs, which matters to investors evaluating AI-driven products and risks.

neural networks technical

"activation functions—core "on/off" switches inside neural networks – so models"

A neural network is a type of computer model that learns to recognize patterns by adjusting many simple connections, similar to how a brain’s network of neurons strengthens certain pathways after practice. Investors care because these models can analyze large, messy datasets to forecast trends, detect fraud, or automate decisions, potentially improving trading signals or operational efficiency; their performance and transparency can materially affect a company’s competitive edge and risk profile.

distributed machine learning technical

"Enhances distributed machine learning by enabling systems to share and"

Distributed machine learning is a method of building and improving artificial intelligence by spreading the work and data across multiple computers or devices that coordinate to train a single model, like a team each lifting part of a heavy load. It matters to investors because it enables faster development, larger-scale data processing, lower per-unit compute costs and greater service resilience—factors that can affect a company’s growth, margins and competitive position.

metadata technical

"share and combine learned knowledge through standardized metadata, improving"

Metadata is descriptive information about a piece of data—like a label on a file or an index card for a document—that explains what the data is, when it was created, who produced it, and how it was generated. For investors, metadata matters because it helps verify authenticity, track provenance, and make large data sets searchable and comparable, which supports due diligence, regulatory compliance, and more reliable analysis of financial or clinical information.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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TEANECK, N.J., April 23, 2026 /PRNewswire/ -- Cognizant (NASDAQ: CTSH) today announced that its AI Lab has received three new U.S. patents, bringing its total number of U.S. patents to 65 and 88 globally.

The newly granted patents build on the lab's work in areas such as human-AI collaboration for decision-making and deep learning for specialized tasks. Together, they reflect continued innovation in the core building blocks of AI systems – helping models learn more effectively, collaborate more seamlessly and support better decisions in real-world environments.

"As enterprises scale AI, they need systems that are not only powerful, but also adaptable, collaborative, and efficient," said Babak Hodjat, Chief AI Officer at Cognizant. "These patents represent advances in how AI systems learn and evolve, helping organizations move from experimentation to real business impact."

The latest patents include:

• U.S. Patent No. 12,572,810 (issued March 10, 2026): Improves decision-recommendation systems, or "prescriptors," by evolving human-designed strategies into stronger, higher-performing policies as conditions change
• U.S. Patent No. 12,566,942 (issued March 3, 2026): Automatically creates and tunes activation functions—core "on/off" switches inside neural networks – so models can perform better for a given task and architecture, reducing manual trial-and-error
• U.S. Patent No. 12,561,223 (issued February 24, 2026): Enhances distributed machine learning by enabling systems to share and combine learned knowledge through standardized metadata, improving coordination and reuse across teams.

"These patents reflect our focus on the building blocks of AI," said Risto Miikkulainen, Vice President of AI Research at Cognizant and Professor of Computer Science at UT Austin. "We are making models more adaptive, decisions more effective, and distributed systems more collaborative. This work moves AI toward more flexible and scalable real-world applications."

The innovations were developed by Cognizant researchers including Dr. Elliot Meyerson, Professor Risto Miikkulainen, Olivier Francon, Dr. Babak Hodjat, Darren Sargent, and former Cognizant researchers Karl Mutch and Dr. Garrett Bingham.

As an AI builder, Cognizant helps companies turn AI spending into results they can use in the business. The Cognizant AI Lab helps make that happen by identifying promising new ideas, proving they work, and moving them from research into ready-to-use solutions that create real impact for clients

About Cognizant
Cognizant (NASDAQ: CTSH) is an AI builder and technology services provider, building the bridge between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, realize tangible returns and keep global enterprises ahead in a fast-changing world. See how at www.cognizant.com or @cognizant.

About the Cognizant AI Lab 
The mission of the Cognizant AI Lab is to maximize human potential with Decision AI, a form of AI that combines generative AI, multi-agent architecture, deep learning, and evolutionary AI to create sophisticated decision-making systems. Decision AI powers Cognizant's Neuro® AI platform, which is utilized by Fortune 500 companies and non-profits to discover new ways to exceed their goals. The platform enables organizations to rapidly build AI that optimizes decision-making, leading to revenue growth and societal progress.  

Led by AI pioneers Babak Hodjat and Risto Miikkulainen, the lab collaborates with institutions, academia, and technology partners to develop groundbreaking AI solutions responsibly. With over 120 patents (issued or pending) globally, the lab excels at combining scientific innovation with commercial application. It supports Cognizant's goal of improving everyday life, focusing on business and AI-for-good applications. 

For more information, contact:

U.S.
Name: Paul Jarratt
Email: Paul.Jarratt@cognizant.com  

Europe / APAC
Name: Sarah Douglas
Email: sarah.douglas@cognizant.com 

India
Name: Vipin Nair
Email: Vipin.Nair@cognizant.com

 

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SOURCE Cognizant Technology Solutions

FAQ

How many patents does Cognizant (CTSH) hold after the April 23, 2026 announcement?

Cognizant holds 65 U.S. patents and 88 international patents after the new grants. According to the company, three U.S. patents were added in early 2026, increasing documented intellectual property totals focused on AI building blocks.

What do the three new U.S. patents for CTSH cover and when were they issued?

The three patents cover prescriptive decision systems, activation-function automation, and distributed ML metadata. According to the company, they were issued Feb 24, 2026; Mar 3, 2026; and Mar 10, 2026, respectively, targeting model adaptability and collaboration.

What investor implications does Cognizant (CTSH) cite for the new AI Lab patents?

The company frames the patents as supporting transitions from AI experimentation to business impact. According to the company, the technologies aim to improve decision recommendations, reduce manual tuning, and enable knowledge reuse across teams, potentially accelerating productization.

Who are the inventors credited on Cognizant's new AI Lab patents (CTSH)?

Inventors include Dr. Elliot Meyerson, Risto Miikkulainen, Olivier Francon, Babak Hodjat, Darren Sargent, Karl Mutch, and Dr. Garrett Bingham. According to the company, the patents reflect work by current and former Cognizant AI Lab researchers and collaborators.

When did the three U.S. patents referenced by Cognizant (CTSH) issue in 2026?

The patents issued on Feb 24, 2026; Mar 3, 2026; and Mar 10, 2026. According to the company, these issuance dates correspond to U.S. Patent Nos. 12,561,223; 12,566,942; and 12,572,810, respectively.